[{"id":"qwen3-8-27b","name":"Qwen3.8-27B","maker":"Alibaba / Qwen","categories":["multimodal","text","code","vision"],"parameters":"27B","context":"262,144 native · up to 1M with YaRN","license":"Apache 2.0","openness":"Open weights · permissive licence","description":"A 27B dense vision-language model for reasoning, coding, document work and long-horizon agents. It accepts images and video and supports 262K native context, extendable to 1M with YaRN.","architecture":"64-layer dense hybrid Transformer; Gated DeltaNet plus global attention and multi-token prediction","input":"Text, image and video → text","destinations":[{"kind":"hf","label":"Hugging Face","url":"https://huggingface.co/Qwen/Qwen3.8-27B"},{"kind":"poe","label":"Poe","url":"https://poe.com/Qwen3.8-27B"}],"benchmarks":{"suite":"llm","values":{"mmlu":null,"gpqa":89.2,"lcb":90.3,"swe":null}},"benchmarkNotes":"SWE-bench Pro 61.7 · Terminal-Bench 2.1 73.0 · OSWorld-Verified 84.3 · AndroidWorld 81.9 · IFBench 79.5 · MathVision 90.0 · LiveCodeBench is v6 (publisher card, xhigh)","aliases":[],"provenance":["existing catalogue","supplied catalogue snapshot"],"listingType":"Open-weight model","sources":[{"label":"Publisher model card","url":"https://huggingface.co/Qwen/Qwen3.8-27B","note":""}],"benchmarkHighlights":[{"label":"SWE-bench Pro","value":"61.7%"},{"label":"Terminal-Bench 2.1 · Terminus","value":"73.0%"},{"label":"NL2Repo-Bench","value":"42.3%"},{"label":"QwenSWEBench · avg@3","value":"79.0%"},{"label":"CoWorkBench","value":"70.7%"},{"label":"IFBench","value":"79.5%"},{"label":"Humanity’s Last Exam","value":"30.8%"},{"label":"OSWorld-Verified","value":"84.3%"},{"label":"AndroidWorld","value":"81.9%"},{"label":"MathVision · without CI","value":"90.0%"},{"label":"OmniDocBench 1.5","value":"91.1%"}]},{"id":"qwen3-coder-32b","name":"Qwen3-Coder-30B-A3B-Instruct","maker":"Alibaba","categories":["code"],"parameters":"30.5B total · 3.3B active","context":"262,144 native · up to 1M with YaRN","license":"Apache 2.0","openness":"Open weights · permissive licence","description":"An instruction-tuned coding model for repository understanding, code generation and tool-based engineering. The official checkpoint is Qwen3-Coder-30B-A3B-Instruct: 30.5B total parameters, 3.3B active, 256K native context, and up to 1M with YaRN.","architecture":"Sparse MoE Transformer; 128 experts with 8 active","input":"Text and code → text/code","destinations":[{"kind":"hf","label":"Hugging Face","url":"https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct"},{"kind":"poe","label":"Poe","url":"https://poe.com/Qwen3-Coder-32B"}],"benchmarks":{"suite":"llm","values":{"mmlu":null,"gpqa":null,"lcb":null,"swe":null}},"benchmarkNotes":"The publisher model card does not report numeric results for this 30B-A3B checkpoint. Qwen’s Qwen3-Coder launch materials describe the separate 480B-A35B model, so those scores are not assigned here.","aliases":["Qwen3-Coder-32B"],"provenance":["existing catalogue"],"listingType":"Open-weight model","sources":[{"label":"Publisher model card","url":"https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct","note":""},{"label":"Qwen3-Coder launch report","url":"https://qwenlm.github.io/blog/qwen3-coder/","note":""}],"old":"Qwen3-Coder-32B"},{"id":"kimi-k2-instruct","name":"Kimi-K2-Instruct","maker":"Moonshot AI","categories":["code"],"parameters":"1T (32B active)","context":"128K","license":"Modified MIT","openness":"Open weights · permissive licence","description":"Moonshot AI’s instruction-tuned sparse MoE for reasoning, coding and agent workflows. It contains 1T total parameters with 32B active per token, supports 128K context and tool calling.","architecture":"Sparse MoE Transformer with 384 experts","input":"Text → text; tool calling","destinations":[{"kind":"hf","label":"Hugging Face","url":"https://huggingface.co/moonshotai/Kimi-K2-Instruct"},{"kind":"poe","label":"Poe","url":"https://poe.com/Kimi-K2-Instruct"}],"benchmarks":{"suite":"llm","values":{"mmlu":81.1,"gpqa":75.1,"lcb":53.7,"swe":65.8}},"benchmarkNotes":"Publisher instruction-model table. GPQA-Diamond uses Avg@8; LiveCodeBench v6 is Pass@1; SWE-bench Verified is the single-attempt agentic result with no test-time compute. The separately reported 71.6% uses multiple attempts and an internal reranker.","aliases":[],"provenance":["official model card"],"listingType":"Open-weight model","sources":[{"label":"Publisher model card","url":"https://huggingface.co/moonshotai/Kimi-K2-Instruct","note":""}],"benchmarkHighlights":[{"label":"AIME 2024 · Avg@64","value":"69.6%"},{"label":"AIME 2025 · Avg@64","value":"49.5%"},{"label":"MATH-500","value":"97.4%"},{"label":"τ²-Bench · retail · Avg@4","value":"70.6%"},{"label":"τ²-Bench · airline · Avg@4","value":"56.5%"},{"label":"τ²-Bench · telecom · Avg@4","value":"65.8%"},{"label":"SWE-bench Verified · multi-attempt reranked","value":"71.6%"}]},{"id":"kimi-k2-6","name":"Kimi-K2.6","maker":"Moonshot AI","categories":["text","code","vision","multimodal"],"parameters":"1T total · 32B active + 0.4B vision","context":"256K","license":"Modified MIT","openness":"Open weights · modified MIT licence","description":"Moonshot's 1T-parameter MoE with 32B active parameters and a 400M vision encoder. It keeps the same total and active parameter counts as Kimi K2 while doubling context to 256K and adding image input. Moonshot's K2.6 benchmark table shows gains over K2.5; its scores use thinking mode, so token usage can vary with reasoning length.","architecture":"Sparse MoE with 384 experts, MLA attention and a 400M MoonViT vision encoder","input":"Text and images → text; reasoning, coding and tool use","destinations":[{"kind":"hf","label":"Hugging Face","url":"https://huggingface.co/moonshotai/Kimi-K2.6"},{"kind":"api","label":"Moonshot API","url":"https://platform.moonshot.ai"}],"benchmarks":{"suite":"llm","values":{"mmlu":null,"gpqa":90.5,"lcb":89.6,"swe":80.2}},"benchmarkNotes":"Moonshot reports K2.6 with thinking mode enabled and a 262,144-token context. In its own K2.5 comparison, K2.6 scores GPQA-Diamond 90.5 vs 87.6, LiveCodeBench v6 89.6 vs 85.0, and SWE-bench Verified 80.2 vs 76.8. These publisher protocols are not directly matched to the older Kimi-K2-Instruct record in this catalogue.","aliases":[],"provenance":["publisher model card"],"listingType":"Open-weight model","sources":[{"label":"Publisher model card","url":"https://huggingface.co/moonshotai/Kimi-K2.6","note":"Specifications, licence and benchmark comparisons are published by Moonshot AI."}],"benchmarkHighlights":[{"label":"GPQA-Diamond · Kimi K2.6 / K2.5","value":"90.5 / 87.6"},{"label":"LiveCodeBench v6 · Kimi K2.6 / K2.5","value":"89.6 / 85.0"},{"label":"SWE-bench Verified · Kimi K2.6 / K2.5","value":"80.2 / 76.8"},{"label":"Terminal-Bench 2.0 · Kimi K2.6 / K2.5","value":"66.7 / 50.8"},{"label":"HLE with tools · Kimi K2.6 / K2.5","value":"54.0 / 50.2"}]},{"id":"glm-5-2-osl","name":"GLM-5.2","maker":"Open-Source-Lab / Z.ai","categories":["code","experimental"],"parameters":"744B total · 40B active","context":"1M","license":"MIT","openness":"Hosted listing · base model and licence identified","description":"Z.ai’s long-horizon reasoning and coding model for large repository work and agentic tool use. Its publisher specifies 1M context and MIT licensing; the OSL entry is a hosted route to GLM-5.2.","architecture":"Sparse MoE Transformer with IndexShare attention and multi-token prediction","input":"Text → text; tool calling","destinations":[{"kind":"hf","label":"Hugging Face","url":"https://huggingface.co/zai-org/GLM-5.2"},{"kind":"poe","label":"Poe","url":"https://poe.com/GLM-5.2-OSL"}],"benchmarks":{"suite":"llm","values":{"mmlu":null,"gpqa":91.2,"lcb":null,"swe":null}},"benchmarkNotes":"Publisher table: GPQA-Diamond 91.2. The other core columns are not reported for this checkpoint; SWE-bench Pro 62.1 is a different suite from SWE-bench Verified. Additional publisher results: HLE 40.5, HLE with tools 54.7, AIME 2026 99.2, Terminal Bench 2.1 (Terminus-2) 81.0, MCP-Atlas 76.8 and Tool-Decathlon 48.2.","aliases":[],"provenance":["existing catalogue"],"listingType":"Hosted assistant listing","sources":[{"label":"Publisher model card","url":"https://huggingface.co/zai-org/GLM-5.2","note":"Specifications describe the publisher’s base model; the hosted service can use a different serving configuration."}],"benchmarkHighlights":[{"label":"Humanity’s Last Exam","value":"40.5%"},{"label":"Humanity’s Last Exam · with tools","value":"54.7%"},{"label":"AIME 2026","value":"99.2%"},{"label":"SWE-bench Pro","value":"62.1%"},{"label":"NL2Repo","value":"48.9%"},{"label":"DeepSWE","value":"46.2%"},{"label":"Terminal Bench 2.1 · Terminus-2","value":"81.0%"},{"label":"MCP-Atlas · Public Set","value":"76.8%"},{"label":"Tool-Decathlon","value":"48.2%"}]},{"id":"deepseek-v4-osl","name":"DeepSeek-V4-Flash · legacy weights","maker":"Open-Source-Lab / DeepSeek","categories":["text","code"],"parameters":"284B total · 13B active","context":"1M tokens","license":"MIT","openness":"Open weights · permissive licence","description":"Historical 284B-total / 13B-active DeepSeek-V4-Flash checkpoint with a 1M-token context. DeepSeek retired this checkpoint in September 2026; the legacy API name deepseek-v4-flash now routes to V4.1-Flash. This record keeps the older weights and scores for comparison.","architecture":"Mixture-of-Experts Transformer with Compressed Sparse Attention (CSA), Heavily Compressed Attention (HCA) and mHC","input":"Text → text; reasoning, coding and tool use","destinations":[{"kind":"hf","label":"Hugging Face","url":"https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash"},{"kind":"poe","label":"Poe","url":"https://poe.com/DeepSeek-V4-OSL"}],"benchmarks":{"suite":"llm","values":{"mmlu":86.2,"gpqa":88.1,"lcb":91.6,"swe":79}},"benchmarkNotes":"Historical V4-Flash Max scores. DeepSeek retired this checkpoint on 10 Sep 2026; the legacy API model name deepseek-v4-flash is temporarily routed to V4.1-Flash. These results describe the older 284B / 13B-active weights, not the current API response.","aliases":[],"provenance":["existing catalogue"],"listingType":"Hosted assistant listing","sources":[{"label":"Publisher model card","url":"https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash","note":"Publisher specification: 284B total parameters, 13B activated, 1M context, and MIT license."},{"label":"DeepSeek-V4.1-Flash release and API migration","url":"https://api-docs.deepseek.com/news/news260910/","note":"DeepSeek says the old V4-Flash checkpoint is retired and its legacy API name routes to V4.1-Flash."}],"benchmarkHighlights":[{"label":"Humanity’s Last Exam · Max","value":"34.8%"},{"label":"Terminal Bench 2.0 · Max","value":"56.9%"},{"label":"SWE-bench Pro · Max","value":"52.6%"},{"label":"SWE-bench Multilingual · Max","value":"73.3%"},{"label":"BrowseComp · Max","value":"73.2%"},{"label":"Humanity’s Last Exam with tools · Max","value":"45.1%"}]},{"id":"deepseek-v3-2-osl","name":"DeepSeek-V3.2","maker":"Open-Source-Lab / DeepSeek","categories":["multimodal","experimental"],"parameters":"671B total · 37B active","context":"128K","license":"MIT","openness":"Hosted listing · base model and licence identified","description":"DeepSeek’s reasoning and agentic text model built around DeepSeek Sparse Attention. It targets coding, problem solving and tool use and is released under MIT.","architecture":"Sparse MoE Transformer with DeepSeek Sparse Attention (DSA)","input":"Text → text; reasoning and tool use","destinations":[{"kind":"hf","label":"Hugging Face","url":"https://huggingface.co/deepseek-ai/DeepSeek-V3.2"},{"kind":"poe","label":"Poe","url":"https://poe.com/DeepSeek-V3.2-OSL"}],"benchmarks":{"suite":"llm","values":{"mmlu":85,"gpqa":82.4,"lcb":null,"swe":70}},"benchmarkNotes":"The Hugging Face evaluation-results panel for this checkpoint lists MMLU-Pro 85.0, GPQA Diamond 82.4 and SWE-bench Verified 70.0 (resolved). LiveCodeBench is not listed; pre-release V3.2-Exp figures are not carried over to this final checkpoint.","aliases":[],"provenance":["existing catalogue"],"listingType":"Hosted assistant listing","sources":[{"label":"Publisher model card","url":"https://huggingface.co/deepseek-ai/DeepSeek-V3.2","note":"Specifications describe the publisher’s base model; the hosted service can use a different serving configuration."},{"label":"DeepSeek-V3.2 technical report","url":"https://huggingface.co/deepseek-ai/DeepSeek-V3.2/blob/main/assets/paper.pdf","note":"The model-card evaluation record cites this report for MMLU-Pro 85.0."}]},{"id":"deepseek-v4-pro-osl","name":"DeepSeek-V4-Pro","maker":"Open-Source-Lab / DeepSeek","categories":["multimodal","experimental"],"parameters":"1.6T total · 49B active","context":"1M","license":"MIT","openness":"Hosted listing · base model and licence identified","description":"DeepSeek’s Pro model for long-context reasoning, coding and agent tasks. It has 1.6T total parameters, 49B activated parameters and 1M context; the -OSL Poe listing is the same base model.","architecture":"Sparse MoE with CSA/HCA hybrid attention and mHC residual connections","input":"Text → text; reasoning and tool use","destinations":[{"kind":"hf","label":"Hugging Face","url":"https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro"},{"kind":"poe","label":"Poe","url":"https://poe.com/DeepSeek-V4-Pro-OSL"}],"benchmarks":{"suite":"llm","values":{"mmlu":87.5,"gpqa":90.1,"lcb":93.5,"swe":80.6}},"benchmarkNotes":"Publisher table: all four core scores are DeepSeek-V4-Pro-Max results — MMLU-Pro (EM) 87.5, GPQA Diamond (Pass@1) 90.1, LiveCodeBench (Pass@1) 93.5 and SWE Verified (resolved) 80.6. The publisher evaluates multiple reasoning-effort settings; these figures are specifically Max.","aliases":["DeepSeek-V4-Pro-OSL"],"provenance":["existing catalogue"],"listingType":"Hosted assistant listing","sources":[{"label":"Publisher model card","url":"https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro","note":"Canonical name and specifications confirmed against DeepSeek’s publisher card; this is the same base model as the OSL listing."}],"old":"DeepSeek-V4-Pro-OSL"},{"id":"mistral-7b-osl","name":"Mistral-7B-Instruct-v0.2","maker":"Open-Source-Lab / Mistral","categories":["text","experimental"],"parameters":"7B","context":"32K","license":"Apache 2.0","openness":"Hosted listing · base model and licence identified","description":"A 7B instruction-tuned language model for local chat, summarization and code assistance. This listing maps to Mistral-7B-Instruct-v0.2, with 32K context and Apache 2.0 licensing.","architecture":"Decoder-only Transformer with grouped-query attention","input":"Text → text","destinations":[{"kind":"hf","label":"Hugging Face","url":"https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2"},{"kind":"poe","label":"Poe","url":"https://poe.com/Mistral-7B-OSL"}],"benchmarks":null,"benchmarkNotes":"","aliases":[],"provenance":["existing catalogue"],"listingType":"Hosted assistant listing","sources":[{"label":"Publisher model card","url":"https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2","note":"Specifications describe the publisher’s base model; the hosted service can use a different serving configuration."}]},{"id":"mistral-nemo","name":"Mistral-Nemo-Instruct-2407","maker":"Open-Source-Lab / Mistral","categories":["multimodal","experimental"],"parameters":"12B","context":"128K","license":"Apache 2.0","openness":"Hosted listing · base model and licence identified","description":"A 12B instruction model co-developed by Mistral AI and NVIDIA. It supports 128K context, multilingual text generation and function calling under Apache 2.0.","architecture":"Decoder-only Transformer with multilingual tokenizer and function calling","input":"Text → text; tool calling","destinations":[{"kind":"hf","label":"Hugging Face","url":"https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407"},{"kind":"poe","label":"Poe","url":"https://poe.com/Mistral-NeMo"}],"benchmarks":null,"benchmarkNotes":"","aliases":[],"provenance":["existing catalogue"],"listingType":"Hosted assistant listing","sources":[{"label":"Publisher model card","url":"https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407","note":"Specifications describe the publisher’s base model; the hosted service can use a different serving configuration."}]},{"id":"mistral-medium-3-5","name":"Mistral Medium 3.5","maker":"Open-Source-Lab / Mistral","categories":["multimodal","experimental"],"parameters":"128B dense","context":"256K","license":"Modified MIT","openness":"Hosted listing · base model and licence identified","description":"Mistral’s 128B flagship for reasoning, coding and agent workflows. It accepts text and images, supports 256K context and function calling, and uses a modified MIT licence.","architecture":"Dense Transformer with vision encoder and native function calling","input":"Text and images → text; tool calling","destinations":[{"kind":"hf","label":"Hugging Face","url":"https://huggingface.co/mistralai/Mistral-Medium-3.5-128B"},{"kind":"poe","label":"Poe","url":"https://poe.com/Mistral-Medium-3.5"}],"benchmarks":{"suite":"llm","values":{"mmlu":null,"gpqa":null,"lcb":null,"swe":77.6}},"benchmarkNotes":"Publisher-reported: SWE-bench Verified 77.6% and τ³-Telecom 91.4%. The linked card does not provide directly comparable MMLU-Pro, GPQA Diamond or LiveCodeBench results.","aliases":[],"provenance":["existing catalogue","publisher model card"],"listingType":"Hosted assistant listing","sources":[{"label":"Publisher model card","url":"https://huggingface.co/mistralai/Mistral-Medium-3.5-128B","note":"Publisher benchmark figures are reported on the linked card; a hosted service can use a different serving configuration."}],"benchmarkHighlights":[{"label":"τ³-Telecom","value":"91.4%"}]},{"id":"gemma-4-26b","name":"Gemma 4 26B A4B IT","maker":"Open-Source-Lab / Google","categories":["text","experimental"],"parameters":"25.2B total · 3.8B active","context":"256K","license":"Apache 2.0","openness":"Hosted listing · base model and licence identified","description":"An instruction-tuned Gemma 4 MoE for chat, reasoning, coding and image understanding. The 26B A4B variant has 25.2B total and 3.8B active parameters, 256K context, and text/image input; it does not support audio.","architecture":"MoE with hybrid local sliding-window and global attention; vision encoder","input":"Text and images → text","destinations":[{"kind":"hf","label":"Hugging Face","url":"https://huggingface.co/google/gemma-4-26B-A4B-it"},{"kind":"poe","label":"Poe","url":"https://poe.com/Gemma-4-31B"}],"benchmarks":{"suite":"llm","values":{"mmlu":82.6,"gpqa":82.3,"lcb":77.1,"swe":null}},"benchmarkNotes":"Google’s instruction-model table reports MMLU-Pro 82.6, GPQA Diamond 82.3 and LiveCodeBench v6 77.1. SWE-bench Verified is not listed for this checkpoint in that table.","aliases":[],"provenance":["existing catalogue"],"listingType":"Hosted assistant listing","sources":[{"label":"Publisher model card","url":"https://huggingface.co/google/gemma-4-26B-A4B-it","note":"Specifications describe the publisher’s base model; the hosted service can use a different serving configuration."},{"label":"Google Gemma 4 benchmark results","url":"https://blog.google/innovation-and-ai/technology/developers-tools/gemma-4/","note":""}],"benchmarkHighlights":[{"label":"AIME 2026 · no tools","value":"88.3%"},{"label":"Tau2 · average over 3","value":"68.2%"},{"label":"BigBench Extra Hard","value":"64.8%"},{"label":"HLE · no tools","value":"8.7%"},{"label":"HLE · with search","value":"17.2%"},{"label":"MMMU-Pro","value":"73.8%"},{"label":"MATH-Vision","value":"82.4%"}]},{"id":"nemotron-3-super","name":"Nemotron 3 Super 120B A12B","maker":"Open-Source-Lab / NVIDIA","categories":["text","experimental"],"parameters":"120B total · 12B active","context":"Up to 1M (256K default configuration)","license":"NVIDIA Nemotron Open Model License","openness":"Hosted listing · base model and licence identified","description":"NVIDIA’s reasoning model for long-context and agentic workloads. It combines Mamba-2, latent MoE and attention layers, supports up to 1M context, and is released under the NVIDIA Nemotron Open Model License.","architecture":"LatentMoE hybrid: Mamba-2, MoE, attention and multi-token prediction","input":"Text → text; reasoning and tool use","destinations":[{"kind":"hf","label":"Hugging Face","url":"https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16"},{"kind":"poe","label":"Poe","url":"https://poe.com/Nemotron-3-Super"}],"benchmarks":{"suite":"llm","values":{"mmlu":83.73,"gpqa":null,"lcb":null,"swe":60.47}},"benchmarkNotes":"NVIDIA reports MMLU-Pro 83.73 and SWE-bench Verified (OpenHands) 60.47. Its card reports GPQA as “no tools” without identifying the Diamond protocol, and LiveCodeBench v5 (2024-07–2024-12); those differently specified results remain separate highlights.","aliases":[],"provenance":["existing catalogue"],"listingType":"Hosted assistant listing","sources":[{"label":"Publisher model card","url":"https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16","note":"Specifications describe the publisher’s base model; the hosted service can use a different serving configuration."}],"benchmarkHighlights":[{"label":"GPQA · no tools","value":"79.23%"},{"label":"GPQA · with tools","value":"82.70%"},{"label":"LiveCodeBench v5 · 2024-07–2024-12","value":"81.19%"},{"label":"Terminal Bench Core 2.0","value":"31.00%"},{"label":"TauBench V2 · average","value":"61.15%"}]},{"id":"wan-2-2-osl","name":"Wan 2.2","maker":"Open-Source-Lab / Alibaba","categories":["video","multimodal","experimental"],"parameters":"Family: 5B dense TI2V or 27B total A14B MoE","context":"Video generation · up to 720p; 5 seconds for T2V-A14B","license":"Apache 2.0","openness":"Hosted listing · base model and licence identified","description":"Wan 2.2 is a family of video-generation models, not one checkpoint. It includes 14B-class MoE text-to-video and image-to-video variants and a 5B text-and-image-to-video model, with outputs up to 720p.","architecture":"Video diffusion family; dual-expert MoE A14B and dense TI2V-5B variants","input":"Text or image → video","destinations":[{"kind":"hf","label":"Hugging Face","url":"https://huggingface.co/Wan-AI/Wan2.2-T2V-A14B"},{"kind":"poe","label":"Poe","url":"https://poe.com/WAN-2.2-OSL"}],"benchmarks":null,"benchmarkNotes":"","aliases":[],"provenance":["existing catalogue"],"listingType":"Hosted assistant listing","sources":[{"label":"Publisher model card","url":"https://huggingface.co/Wan-AI/Wan2.2-T2V-A14B","note":"The OSL listing does not identify a specific Wan 2.2 variant; source describes the family and its variants."}]},{"id":"trellis-3d-osl","name":"TRELLIS Image Large","maker":"Open-Source-Lab / Microsoft","categories":["3d","experimental"],"parameters":"2B","context":"One image → 3D assets","license":"MIT","openness":"Hosted listing · base model and licence identified","description":"Microsoft Research’s image-to-3D model turns an image into textured assets and multiple 3D representations. 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The card's GPQA Diamond 66.8 and LiveCodeBench 61.6 are for the separate Reasoning checkpoint.","aliases":[],"provenance":["supplied catalogue snapshot"],"listingType":"Open-weight model","sources":[{"label":"Publisher model card","url":"https://huggingface.co/mistralai/Ministral-3-8B-Instruct-2512","note":""}],"benchmarkHighlights":[{"label":"IFEval · Instruct checkpoint","value":"87.6%"},{"label":"WildBench · Instruct checkpoint","value":"66.8%"},{"label":"Arena Hard · Instruct checkpoint","value":"50.9%"},{"label":"MM-MTBench · Instruct checkpoint","value":"8.08"}]},{"id":"qwen3-coder-next","name":"Qwen3-Coder-Next","maker":"Alibaba / Qwen","categories":["code"],"parameters":"80B / 3B active","context":"262,144","license":"Apache 2.0","openness":"Open weights · permissive licence","description":"Coding-focused hybrid mixture-of-experts model for tool use and agentic development, released 3 February 2026. 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MMLU-Pro, GPQA Diamond and LiveCodeBench are not listed in that panel.","aliases":[],"provenance":["supplied catalogue snapshot"],"listingType":"Open-weight model","sources":[{"label":"Publisher model card","url":"https://huggingface.co/Qwen/Qwen3-Coder-Next","note":""}],"benchmarkHighlights":[{"label":"SWE-bench Pro","value":"44.3%"},{"label":"Terminal-Bench 2.0","value":"36.2%"}]},{"id":"glm-5-3","name":"GLM-5.3","maker":"Z.ai","categories":["text","code"],"parameters":"753B","context":"Up to 1M in publisher evaluations","license":"GLM-5.3 Model License","openness":"Open weights · custom or non-commercial licence","description":"Flagship open-weight text model for coding and agent workflows with configurable reasoning effort. 753B-parameter listing on Hugging Face. Review the model-specific GLM-5.3 licence before deployment; this entry is not MIT or Apache licensed.","architecture":"Sparse MoE Transformer with IndexShare attention and multi-token prediction","input":"Text → text; reasoning and tool use","destinations":[{"kind":"hf","label":"Hugging Face","url":"https://huggingface.co/zai-org/GLM-5.3"},{"kind":"poe","label":"Poe","url":"https://poe.com/GLM-5.3"}],"benchmarks":{"suite":"llm","values":{"mmlu":null,"gpqa":null,"lcb":null,"swe":null}},"benchmarkNotes":"The publisher table reports Terminal Bench 2.1 88.2, Terminal Bench 3.0 28.3, DeepSWE v1.1 66.9, NL2Repo 58.0, ProgramBench 19.0, FrontierSWE 78.1, SWE-Marathon v1.1 42.5, PostTrainBench 39.8, CyberGym 84.5 and ExploitGym 105 / 130 (2h / 6h). It does not list directly matching MMLU-Pro, GPQA Diamond, LiveCodeBench or SWE-bench Verified values.","aliases":[],"provenance":["supplied catalogue snapshot"],"listingType":"Open-weight model","sources":[{"label":"Publisher model card","url":"https://huggingface.co/zai-org/GLM-5.3","note":""}],"benchmarkHighlights":[{"label":"Terminal Bench 2.1","value":"88.2%"},{"label":"Terminal Bench 3.0","value":"28.3%"},{"label":"DeepSWE v1.1","value":"66.9%"},{"label":"NL2Repo","value":"58.0%"},{"label":"ProgramBench · Almost Solved","value":"19.0%"},{"label":"FrontierSWE · Dominance","value":"78.1%"},{"label":"SWE-Marathon v1.1","value":"42.5%"},{"label":"PostTrainBench","value":"39.8%"},{"label":"CyberGym","value":"84.5%"},{"label":"ExploitGym · 2h / 6h","value":"105 / 130"}]},{"id":"glm-5-3-flash","name":"GLM-5.3-Flash","maker":"Z.ai","categories":["text","code","vision","multimodal"],"parameters":"320B total · 18B active","context":"Up to 1M in publisher evaluations","license":"MIT","openness":"Open weights · permissive licence","description":"Z.ai's 320B-total / 18B-active multimodal GLM model with a 1M-token evaluation context. 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