{ "data": { "a": { "slug": "longcat-2-5", "name": "LongCat-2.5", "vendor": "Meituan (LongCat)", "vendorUrl": "https://longcat.ai", "kind": "weights", "category": "open-weights", "summary": "LongCat-2.5 is Meituan's open-weight flagship, a multimodal mixture-of-experts model of 1.6T parameters with about 48B active per token, built for long agentic coding work. MIT licence. The weights are 3,562 GB, and the card documents deployment on NPU hardware only.", "url": "https://www.anchorterminal.com/tools/longcat-2-5", "markdownUrl": "https://www.anchorterminal.com/tools/longcat-2-5.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/longcat-2-5.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/longcat-2-5.json", "dns": "longcat-2-5.grade.anchorterminal.com", "repo": "https://huggingface.co/meituan-longcat/LongCat-2.5", "license": "MIT", "transports": [], "packages": [ { "registry": "huggingface", "name": "meituan-longcat/LongCat-2.5" }, { "registry": "huggingface", "name": "meituan-longcat/LongCat-2.5-W4A4" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither repository is gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use under the MIT licence. Running it yourself is a data-centre job: 3,562 GB in BF16, or 1,336 GB for Meituan's 4-bit build, which the card says is for INT4 computation on NPU accelerators (checked 2026-11-10).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-10" }, "docsUrl": "https://huggingface.co/meituan-longcat/LongCat-2.5", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "reasoning", "tool-calling", "agentic-coding", "mit", "safetensors", "int4", "npu", "self-hosted" ], "lastRelease": "2026-11-10", "graded": true, "anchor": { "graded": true, "score": 50.5, "grade": "D", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1518, "categoryRank": 107, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 75, "reach": 35, "risk": 60, "upkeep": 46 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "Meituan's flagship is MIT-licensed and documented for tool calling and switchable reasoning, with strong numbers of its own on agentic coding. Almost nobody can run it: 3,562 GB of weights, a 1,336 GB 4-bit build for NPU accelerators, and no released runtime with its architecture. A model to watch until a runtime or a provider picks it up.", "bestFor": "Operators with NPU clusters, and anyone tracking what the open flagships can do", "strengths": [ "MIT licence, ungated", "Tool calling documented with a worked example", "Reasoning can be switched per request and comes back as reasoning_content", "Meituan publishes its own 4-bit build", "Updated on the day we checked" ], "weaknesses": [ "3,562 GB in BF16 and 1,336 GB at 4 bits", "No released runtime has its architecture", "The only documented deployment is on NPU hardware through Meituan's fork", "config.json names model code that isn't in the repository", "No stated context length, languages, safety results or data sources", "No inference provider serves it" ], "agentNotes": [ "You can't run this on a workstation or a single GPU server. Plan for a provider, and none serves it yet", "If an operator serves it, call it as an OpenAI-compatible chat API with tools in the function format", "Pass `chat_template_kwargs` with `enable_thinking` true or false", "With thinking on, keep earlier reasoning in the history (`save_history_reasoning_content` true), as the card recommends", "The card's results use temperature 1.0 and top_p 0.95", "Its benchmark table is Meituan's own. The unmarked scores are in-house" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "D", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 50.5 } ], "editorialScores": { "interface": 75, "reach": 35, "risk": 60, "upkeep": 46 }, "provenanceScore": 49, "checked": { "categories": { "interface": "2026-11-10", "reach": "2026-11-10", "risk": "2026-11-10", "upkeep": "2026-11-10" }, "last": "2026-11-10", "oldest": "2026-11-10", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "# No released runtime loads it. Meituan's NPU instructions are in its SGLang fork:\n# github.com/meituan-longcat/SGLang-FluentLLM, branch longcat-2.5-npu, npu_test/ReadMe.md", "http": "# The card's example calls an OpenAI-compatible chat completions API with tools, and passes\n# extra_body={\"chat_template_kwargs\": {\"enable_thinking\": true, \"save_history_reasoning_content\": true}}" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/longcat-2-5" }, "weights": { "trainedBy": "Meituan (LongCat team)", "builtOn": "LongCat-2.0 and LongCat-Next, per the card", "params": "1.6T", "active": "48B", "context": 524288, "goodFor": "Long agentic coding and research runs with images, for an operator with NPU clusters. Nobody else can run it yet.", "runtimes": [], "builds": [ { "name": "LongCat-2.5-W4A4", "url": "https://huggingface.co/meituan-longcat/LongCat-2.5-W4A4", "who": "publisher", "precision": "int4", "sizeGB": 1336, "memoryGB": 2048, "format": "safetensors", "note": "Meituan's 4-bit build of the experts' weights and activations, for INT4 computation on NPU accelerators" }, { "name": "LongCat-2.5 (BF16 safetensors)", "url": "https://huggingface.co/meituan-longcat/LongCat-2.5", "who": "publisher", "precision": "bf16", "sizeGB": 3562, "memoryGB": 8192, "format": "safetensors", "note": "210 files" } ], "run": "See github.com/meituan-longcat/SGLang-FluentLLM, branch longcat-2.5-npu", "runBuild": "LongCat-2.5-W4A4", "checked": "2026-11-10", "minMemoryGB": 2048 }, "family": { "lab": "Meituan (LongCat)", "family": "LongCat-2", "url": "https://www.anchorterminal.com/open-weights/#meituan-longcat", "others": [ { "name": "LongCat-2.5-W4A4", "url": "https://huggingface.co/meituan-longcat/LongCat-2.5-W4A4" }, { "name": "LongCat-2.0", "url": "https://huggingface.co/meituan-longcat/LongCat-2.0" } ] }, "area": "models", "provenance": { "legalEntity": "Meituan, named in the card's licence section. The legal entity wasn't looked up", "domain": "longcat.ai", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "https://huggingface.co/meituan-longcat/LongCat-2.5/blob/main/LICENSE", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/meituan-longcat/LongCat-2.5/commits/main", "securityTxt": "unknown", "checked": "2026-11-10", "notes": [ "longcat.ai has no robots.txt (the request returns a not-found message) and its pages are JavaScript applications that gave no text on 10 November 2026, so the site's own statements weren't read.", "The domain's registration date wasn't looked up.", "Nothing is hosted under this listing, so the MIT licence stands in for terms of service and no privacy policy is scored.", "The changelog is the commit history, 50 commits or more since 4 November 2026." ], "score": 49 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/longcat-2-5.json" }, "answer": "Qwen3.5-122B-A10B scores 63.1 (B) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 1 of 4 scored categories. LongCat-2.5 leads on risk, agent interface and documentation and upkeep.", "b": { "slug": "qwen3-5-122b-a10b", "name": "Qwen3.5-122B-A10B", "vendor": "Alibaba (Qwen team)", "vendorUrl": "https://qwen.ai", "kind": "weights", "category": "open-weights", "summary": "Qwen3.5-122B-A10B is Alibaba's mid-size open-weight mixture-of-experts vision-language model, 122B parameters with 10B active, thinking on by default and a 262,144-token context, published on Hugging Face in BF16, FP8 and GPTQ 4-bit under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/qwen3-5-122b-a10b", "markdownUrl": "https://www.anchorterminal.com/tools/qwen3-5-122b-a10b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen3-5-122b-a10b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen3-5-122b-a10b.json", "dns": "qwen3-5-122b-a10b.grade.anchorterminal.com", "repo": "https://huggingface.co/Qwen/Qwen3.5-122B-A10B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Qwen/Qwen3.5-122B-A10B" }, { "registry": "huggingface", "name": "Qwen/Qwen3.5-122B-A10B-FP8" }, { "registry": "huggingface", "name": "Qwen/Qwen3.5-122B-A10B-GPTQ-Int4" }, { "registry": "ollama", "name": "qwen3.5:122b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of the three repositories is gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Alibaba Cloud Model Studio and several inference providers also serve it per token. Running it yourself costs the hardware, 250.2 GB for the BF16 files, 78.9 GB for Alibaba's GPTQ 4-bit build and 81 GB for Ollama's build (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://huggingface.co/Qwen/Qwen3.5-122B-A10B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "reasoning", "tool-calling", "apache-2.0", "safetensors", "fp8", "gptq", "self-hosted" ], "lastRelease": "2026-02-24", "graded": true, "anchor": { "graded": true, "score": 63.1, "grade": "B", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1518, "categoryRank": 19, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 62, "interface": 70, "reach": 80, "risk": 55, "upkeep": 34 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-11-02" }, "negative": 0, "verdict": "A strong mid-size cousin for coding, agent steps and reading images or video, with thinking you can switch off per request. Alibaba's 4-bit build needs about 95 GB, which fits one 128 GB unified-memory machine, and several providers host it. The main risk is silence, with no safety results, agent guidance or answers from Alibaba.", "bestFor": "Agents with a 128 GB unified-memory machine or two large GPUs that want a capable vision-language model with a plain licence.", "strengths": [ "Released support in llama.cpp, Ollama, vLLM, SGLang and Transformers", "Alibaba's own FP8 and GPTQ 4-bit builds, the 4-bit one about 95 GB in use", "Plain Apache 2.0 with no gate, no account and no use policy", "Safetensors only, with a checksum on every file in the publisher's own organisation", "262,144 tokens native, several tool calls per turn and thinking that turns off per request", "Served by Alibaba's Model Studio and at least four other providers" ], "weaknesses": [ "Too big for a single 64 GB machine even at 4 bits", "No safety results, agent-use guidance or reporting route for flaws", "No reply from Qwen to any of the 21 discussions, and a template fix waits since April", "Tool format is only in the template and structured output isn't documented", "No limitations, knowledge cut-off or training data sources on the card" ], "agentNotes": [ "Quickest start on a 128 GB machine is `ollama run qwen3.5:122b` (Q4_K_M, 81 GB of files, about 97 GB with context)", "Alibaba's 4-bit build serves with `vllm serve Qwen/Qwen3.5-122B-A10B-GPTQ-Int4 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder --quantization moe_wna16`, though that kernel isn't documented for AMD gfx1151", "Use the qwen3_coder tool parser and the qwen3 reasoning parser, as the card's commands do", "Turn thinking off with `\"chat_template_kwargs\": {\"enable_thinking\": false}` in the request body, since it's on by default and `/think` and `/nothink` aren't supported", "Sampling per the card is temperature 0.6, top_p 0.95 and top_k 20 when thinking, and temperature 0.7, top_p 0.8, top_k 20 and presence_penalty 1.5 without", "Keep the context at 128K or more if you rely on thinking, and turn on YaRN only past 262,144 tokens", "Add `--language-model-only` in vLLM to skip the vision encoder and free memory for the KV cache when you only need text", "Validate any JSON it returns, since structured output isn't documented" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "B", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 63.1 } ], "editorialScores": { "capability": 62, "interface": 70, "reach": 80, "risk": 55, "upkeep": 34 }, "provenanceScore": 54, "checked": { "categories": { "capability": "2026-11-02", "interface": "2026-11-02", "reach": "2026-11-02", "risk": "2026-11-02", "upkeep": "2026-11-02" }, "last": "2026-11-02", "lastMoved": "2026-11-02", "oldest": "2026-11-02", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run qwen3.5:122b\n# or, with the card's vLLM command for Alibaba's 4-bit build:\nvllm serve Qwen/Qwen3.5-122B-A10B-GPTQ-Int4 --max-model-len 262144 --reasoning-parser qwen3 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_coder --quantization moe_wna16", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"qwen3.5:122b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/qwen3-5-122b-a10b" }, "sameCompany": [ "qwen3-embedding-0-6b", "qwen3-embedding-4b", "qwen3-embedding-8b", "qwen3-vl-embedding-2b", "qwen3-vl-embedding-8b", "qwen3-asr-0-6b", "qwen3-asr-1-7b", "qwen3-tts-1-7b-customvoice", "qwen-agentworld-35b-a3b", "qwen3-30b-a3b-instruct-2507", "qwen3-4b-instruct-2507", "qwen3-coder-30b-a3b-instruct", "qwen3-coder-next", "qwen3-omni-30b-a3b-instruct", "qwen3-vl-2b-instruct", "qwen3-vl-30b-a3b-instruct", "qwen3-vl-32b-instruct", "qwen3-vl-4b-instruct", "qwen3-vl-8b-instruct", "qwen3-5-0-8b", "qwen3-5-2b", "qwen3-5-397b-a17b", "qwen3-5-4b", "qwen3-5-9b", "qwen3-6-27b", "qwen3-6-35b-a3b", "qwen3-8-27b", "qwen3-8-flash-next" ], "weights": { "trainedBy": "Alibaba (Qwen team)", "params": "122B total, 10B active", "context": 262144, "goodFor": "Mid-size mixture-of-experts vision-language model for coding, agents and tool calling, text, images and video in, thinking on by default.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Qwen3.5-122B-A10B-GPTQ-Int4", "url": "https://huggingface.co/Qwen/Qwen3.5-122B-A10B-GPTQ-Int4", "who": "publisher", "precision": "gptq-int4", "sizeGB": 78.9, "memoryGB": 96, "format": "safetensors", "note": "The card serves it with `--quantization moe_wna16` in vLLM and SGLang. Running this kernel on ROCm gfx1151 isn't documented" }, { "name": "lmstudio-community Qwen3.5-122B-A10B-GGUF Q4_K_M", "url": "https://huggingface.co/lmstudio-community/Qwen3.5-122B-A10B-GGUF", "who": "lmstudio", "precision": "q4_k_m", "sizeGB": 75.1, "memoryGB": 96, "format": "gguf", "runsOn": [ "llama.cpp", "LM Studio" ], "note": "74.2 GB of Q4_K_M weights plus a 0.9 GB BF16 vision projector, summed from the Hub's file list. LM Studio's own catalogue page for this size wasn't found, so it's recorded but not used for Reach" }, { "name": "ollama qwen3.5:122b", "url": "https://ollama.com/library/qwen3.5:122b", "who": "ollama", "precision": "q4_k_m", "sizeGB": 81, "memoryGB": 128, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q4_K_M with image input (digest 8b9d11d807c5), the same build as the 122b-a10b and 122b-a10b-q4_K_M tags and the only 122B tag. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB" }, { "name": "Qwen3.5-122B-A10B-FP8", "url": "https://huggingface.co/Qwen/Qwen3.5-122B-A10B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 127.2, "memoryGB": 192, "format": "safetensors" }, { "name": "Qwen3.5-122B-A10B", "url": "https://huggingface.co/Qwen/Qwen3.5-122B-A10B", "who": "publisher", "precision": "bf16", "sizeGB": 250.2, "memoryGB": 512, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "ollama run qwen3.5:122b", "runBuild": "ollama qwen3.5:122b", "checked": "2026-10-21", "minMemoryGB": 96 }, "family": { "lab": "Alibaba (Qwen)", "family": "Qwen3", "url": "https://www.anchorterminal.com/open-weights/#qwen", "others": [ { "name": "Qwen3-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-0.6B", "params": "0.6B" }, { "name": "Qwen3-1.7B", "url": "https://huggingface.co/Qwen/Qwen3-1.7B", "params": "1.7B" }, { "name": "Qwen3-14B", "url": "https://huggingface.co/Qwen/Qwen3-14B", "params": "14B" }, { "name": "Qwen3-235B-A22B", "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B", "params": "235B-A22B" }, { "name": "Qwen3-30B-A3B", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B", "params": "30B-A3B" }, { "name": "Qwen3-32B", "url": "https://huggingface.co/Qwen/Qwen3-32B", "params": "32B" }, { "name": "Qwen3-4B", "url": "https://huggingface.co/Qwen/Qwen3-4B", "params": "4B" }, { "name": "Qwen3-8B", "url": "https://huggingface.co/Qwen/Qwen3-8B", "params": "8B" }, { "name": "Qwen3-0.6B-Base", "url": "https://huggingface.co/Qwen/Qwen3-0.6B-Base", "params": "0.6B" }, { "name": "Qwen3-1.7B-Base", "url": "https://huggingface.co/Qwen/Qwen3-1.7B-Base", "params": "1.7B" }, { "name": "Qwen3-14B-Base", "url": "https://huggingface.co/Qwen/Qwen3-14B-Base", "params": "14B" }, { "name": "Qwen3-30B-A3B-Base", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Base", "params": "30B-A3B" }, { "name": "Qwen3-4B-Base", "url": "https://huggingface.co/Qwen/Qwen3-4B-Base", "params": "4B" }, { "name": "Qwen3-8B-Base", "url": "https://huggingface.co/Qwen/Qwen3-8B-Base", "params": "8B" }, { "name": "Qwen3-Reranker-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-Reranker-0.6B", "params": "0.6B" }, { "name": "Qwen3-Reranker-8B", "url": "https://huggingface.co/Qwen/Qwen3-Reranker-8B", "params": "8B" }, { "name": "Qwen3-Embedding-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-0.6B", "params": "0.6B", "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-0-6b" }, { "name": "Qwen3-Embedding-4B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-4B", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-4b" }, { "name": "Qwen3-Embedding-8B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-8B", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-8b" }, { "name": "Qwen3-Reranker-4B", "url": "https://huggingface.co/Qwen/Qwen3-Reranker-4B", "params": "4B" }, { "name": "Qwen3-235B-A22B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507", "params": "235B-A22B" }, { "name": "Qwen3-Coder-480B-A35B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct", "params": "480B-A35B" }, { "name": "Qwen3-235B-A22B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507", "params": "235B-A22B" }, { "name": "Qwen3-30B-A3B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-30b-a3b-instruct-2507" }, { "name": "Qwen3-30B-A3B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507", "params": "30B-A3B" }, { "name": "Qwen3-Coder-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-coder-30b-a3b-instruct" }, { "name": "Qwen3-4B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-4b-instruct-2507" }, { "name": "Qwen3-4B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507", "params": "4B" }, { "name": "Qwen3-Next-80B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct", "params": "80B-A3B" }, { "name": "Qwen3-Next-80B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Thinking", "params": "80B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Captioner", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Captioner", "params": "30B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Thinking", "params": "30B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Instruct", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-omni-30b-a3b-instruct" }, { "name": "Qwen3-VL-235B-A22B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Instruct", "params": "235B-A22B" }, { "name": "Qwen3-VL-235B-A22B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Thinking", "params": "235B-A22B" }, { "name": "Qwen3Guard-Gen-0.6B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-0.6B", "params": "0.6B" }, { "name": "Qwen3Guard-Gen-4B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-4B", "params": "4B" }, { "name": "Qwen3Guard-Gen-8B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-8B", "params": "8B" }, { "name": "Qwen3Guard-Stream-0.6B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-0.6B", "params": "0.6B" }, { "name": "Qwen3Guard-Stream-4B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-4B", "params": "4B" }, { "name": "Qwen3Guard-Stream-8B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-8B", "params": "8B" }, { "name": "Qwen3-4B-SafeRL", "url": "https://huggingface.co/Qwen/Qwen3-4B-SafeRL", "params": "4B" }, { "name": "Qwen3-VL-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-30b-a3b-instruct" }, { "name": "Qwen3-VL-30B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Thinking", "params": "30B-A3B" }, { "name": "Qwen3-VL-4B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-4b-instruct" }, { "name": "Qwen3-VL-4B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Thinking", "params": "4B" }, { "name": "Qwen3-VL-8B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-8b-instruct" }, { "name": "Qwen3-VL-8B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Thinking", "params": "8B" }, { "name": "Qwen3-VL-2B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-2B-Instruct", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-2b-instruct" }, { "name": "Qwen3-VL-2B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-2B-Thinking", "params": "2B" }, { "name": "Qwen3-VL-32B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-32B-Instruct", "params": "32B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-32b-instruct" }, { "name": "Qwen3-VL-32B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-32B-Thinking", "params": "32B" }, { "name": "Qwen3-VL-Embedding-2B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Embedding-2B", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-embedding-2b" }, { "name": "Qwen3-VL-Embedding-8B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Embedding-8B", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-embedding-8b" }, { "name": "Qwen3-VL-Reranker-2B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Reranker-2B", "params": "2B" }, { "name": "Qwen3-VL-Reranker-8B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Reranker-8B", "params": "8B" }, { "name": "Qwen3-TTS-12Hz-0.6B-Base", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-0.6B-Base", "params": "0.6B" }, { "name": "Qwen3-TTS-12Hz-0.6B-CustomVoice", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice", "params": "0.6B" }, { "name": "Qwen3-TTS-12Hz-1.7B-Base", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-Base", "params": "1.7B" }, { "name": "Qwen3-TTS-12Hz-1.7B-CustomVoice", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice", "params": "1.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-tts-1-7b-customvoice" }, { "name": "Qwen3-TTS-12Hz-1.7B-VoiceDesign", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign", "params": "1.7B" }, { "name": "Qwen3-TTS-Tokenizer-12Hz", "url": "https://huggingface.co/Qwen/Qwen3-TTS-Tokenizer-12Hz", "params": "not in name" }, { "name": "Qwen3-ASR-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-ASR-0.6B", "params": "0.6B", "listing": "https://www.anchorterminal.com/tools/qwen3-asr-0-6b" }, { "name": "Qwen3-ASR-1.7B", "url": "https://huggingface.co/Qwen/Qwen3-ASR-1.7B", "params": "1.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-asr-1-7b" }, { "name": "Qwen3-ForcedAligner-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-ForcedAligner-0.6B", "params": "0.6B" }, { "name": "Qwen3-Coder-Next", "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next", "params": "not in name; Hub safetensors total 79.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-coder-next" }, { "name": "Qwen3-Coder-Next-Base", "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next-Base", "params": "not in name; Hub safetensors total 79.7B" }, { "name": "WebWorld-14B", "url": "https://huggingface.co/Qwen/WebWorld-14B", "params": "14B" }, { "name": "WebWorld-32B", "url": "https://huggingface.co/Qwen/WebWorld-32B", "params": "32B" }, { "name": "WebWorld-8B", "url": "https://huggingface.co/Qwen/WebWorld-8B", "params": "8B" }, { "name": "Qwen3.5-397B-A17B", "url": "https://huggingface.co/Qwen/Qwen3.5-397B-A17B", "params": "397B-A17B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-397b-a17b" }, { "name": "Qwen3.5-27B", "url": "https://huggingface.co/Qwen/Qwen3.5-27B", "params": "27B" }, { "name": "Qwen3.5-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen3.5-35B-A3B", "params": "35B-A3B" }, { "name": "Qwen3.5-35B-A3B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-35B-A3B-Base", "params": "35B-A3B" }, { "name": "Qwen3.5-9B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-9B-Base", "params": "9B" }, { "name": "Qwen3.5-4B", "url": "https://huggingface.co/Qwen/Qwen3.5-4B", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-4b" }, { "name": "Qwen3.5-4B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-4B-Base", "params": "4B" }, { "name": "Qwen3.5-9B", "url": "https://huggingface.co/Qwen/Qwen3.5-9B", "params": "9B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-9b" }, { "name": "Qwen3.5-0.8B", "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B", "params": "0.8B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-0-8b" }, { "name": "Qwen3.5-0.8B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B-Base", "params": "0.8B" }, { "name": "Qwen3.5-2B", "url": "https://huggingface.co/Qwen/Qwen3.5-2B", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-2b" }, { "name": "Qwen3.5-2B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-2B-Base", "params": "2B" }, { "name": "Qwen3.6-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen3.6-35B-A3B", "params": "35B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-6-35b-a3b" }, { "name": "Qwen3.6-27B", "url": "https://huggingface.co/Qwen/Qwen3.6-27B", "params": "27B", "listing": "https://www.anchorterminal.com/tools/qwen3-6-27b" }, { "name": "Qwen-AgentWorld-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B", "params": "35B-A3B" }, { "name": "Qwen3.8-27B", "url": "https://huggingface.co/Qwen/Qwen3.8-27B", "params": "27B", "listing": "https://www.anchorterminal.com/tools/qwen3-8-27b" }, { "name": "Qwen3.8-2.4T-A95B", "url": "https://huggingface.co/Qwen/Qwen3.8-2.4T-A95B", "params": "2.4T-A95B" }, { "name": "Qwen3.8-Flash-Next", "url": "https://huggingface.co/Qwen/Qwen3.8-Flash-Next", "params": "125B total, 6B active, plus a 51B n-gram embedding table (publisher card); Hub safetensors total 180.0B", "listing": "https://www.anchorterminal.com/tools/qwen3-8-flash-next" }, { "name": "Qwen-Drive-1.0-4B", "url": "https://huggingface.co/Qwen/Qwen-Drive-1.0-4B", "params": "4B" } ], "olderCount": 109, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "Alibaba Cloud (named in the licence's copyright line)", "domain": "qwen.ai", "domainRegistered": "2023-08-27", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The LICENSE file reads Copyright 2026 Alibaba Cloud. The domain's registration date is per the 20 October 2026 RDAP check for qwen3-8-27b.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "No changelog for the files. The commit history's messages are uploads and README updates, the last on 24 April 2026. The News list in github.com/QwenLM/Qwen3.5 dates the release to 24 February 2026.", "qwen.ai answers /.well-known/security.txt with a redirect to its home page, so there is no security.txt." ], "score": 54 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-5-122b-a10b.json", "live": { "slug": "qwen3-5-122b-a10b", "securityTxt": { "url": "https://qwen.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-10T16:09:18.413964975Z" }, "domain": { "domain": "qwen.ai", "registered": "2023-08-27", "source": "https://rdap.identitydigital.services/rdap/domain/qwen.ai", "checkedAt": "2026-11-08T15:07:12.468678256Z" }, "updatedAt": "2026-11-10T16:09:18.413964975Z" } }, "edges": [ { "by": 45, "category": "reach", "leads": "qwen3-5-122b-a10b", "name": "Reach", "reasons": { "a": "Memory 0, the smallest build is Meituan's W4A4 repository, 1,336 GB, and 1,336 GB × 1.2 = 1,603 GB, far above 256 GB. The BF16 files are 3,562 GB. Runtimes 0, none of the six runs it in a released version: vLLM 0.33.0, SGLang 0.5.23, Transformers 5.22.2 and llama.cpp b12242 have no LongcatCausalLM or pro_v code, and the model code config.json names isn't in the repository. Builds 10, one quantised build from Meituan, the W4A4 repository (4-bit experts, 1,336 GB). Terms 15, MIT in the metadata, the LICENSE file and the card, with nothing added. Download 10, the Hub API reports gated false for both repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 0 + 0 + 10 + 15 + 10 + 0 = 35.", "b": "Memory earns 10 of 30, since Alibaba's GPTQ 4-bit build is 78.9 GB, about 95 GB with a fifth added, which fits our reference machine but no smaller band. Runtimes reach the 25 cap on released llama.cpp, Ollama, vLLM, SGLang and Transformers. Alibaba's FP8 and GPTQ-Int4 builds give builds the full 15. Apache 2.0 with nothing added gives terms 15, the ungated repositories give download 10, and Alibaba's own Model Studio plus Novita and DeepInfra give hosted too its 5." }, "scores": { "a": 35, "b": 80 } }, { "by": 12, "category": "upkeep", "leads": "longcat-2-5", "name": "Documentation and upkeep", "reasons": { "a": "Model card 11, Intended use, limitations (the usage considerations), the architecture and the parameter counts are on the card. Training data 5, A general description of the training stages and where the reinforcement learning tasks came from, with no sources, sizes or cut-off. Last change 20, the last commit, on 10 November 2026, updated the card, on the day of this check. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (9 and 10 November 2026) has a reply from a meituan-longcat member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, LongCat-2.0 stays in its own repositories and 2.5 came out in new ones, with no dated note on the older ones that we read. File health 5, the template is in both places and agrees, but config.json's auto_map names configuration_longcat_oe and modeling_longcat_oe, which aren't in the repository, and the tokenizer config's 131,072-token length is a quarter of the config's 524,288 positions. 11 + 5 + 20 + 0 + 5 + 5 = 46.", "b": "The card covers five of the seven items and misses limitations and a cut-off (14). Training data is a general description without a cut-off (5). The files last changed on 24 April 2026, 192 days before this check, past the 180-day band and within a year (5), and Qwen's GitHub repository holds only a README and licence, so no reference code of Alibaba's changed later. No Qwen member has answered any of the 21 discussions that aren't pull requests, recounted on 2 November (0), and GitHub issues couldn't be counted. It's a first release at this size (5), and a template fix and a run report still wait unanswered (5). 14 + 5 + 5 + 0 + 5 + 5 = 34." }, "scores": { "a": 46, "b": 34 } }, { "by": 5, "category": "risk", "leads": "longcat-2-5", "name": "Risk", "reasons": { "a": "File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in the meituan-longcat organisation, the name of Meituan's GitHub organisation that the card links for deployment, and the card's licence names Meituan (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions under Usage Considerations: not evaluated for every use, assess accuracy, safety and fairness before sensitive use, and follow the law. Licence risk 15, MIT with no use policy, no threshold and no right to change the terms. Security programme 0, no way to report a flaw on the card beyond a general contact, and Meituan's own security pages weren't looked up, so this scores as absent and is an open question. 25 + 15 + 0 + 5 + 15 + 0 = 60.", "b": "Safetensors only with no repository code run on load or to build prompts gives file safety 25, and files in the Qwen organisation the publisher's GitHub links to, each hashed on the Hub, give provenance 15. Plain Apache 2.0 keeps licence risk at 15. Safety evaluation, agent guidance and a security programme score 0, since the card has no safety results or cautions and no reporting route was found. The Qwen3.5 blog and Alibaba's security response centre couldn't be read, so both are scored as absent and left open." }, "scores": { "a": 60, "b": 55 } }, { "by": 5, "category": "interface", "leads": "longcat-2-5", "name": "Agent interface", "reasons": { "a": "Chat template 20, chat_template.jinja in the repository (11,823 characters), with a copy in tokenizer_config.json. Tool-calling format 25, the card documents how tools are declared (the OpenAI function format) and how calls come back (tool_calls with an id, a name and JSON arguments, answered by a tool message), in a worked example against an OpenAI-compatible server. Trained tool calling 10, the card describes reinforcement learning on long agentic tasks and shows one tool call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, enable_thinking on or off per request through chat_template_kwargs, with the reasoning returned as reasoning_content, both shown on the card. Context 10, the card states no context length, but config.json gives 524,288 positions and the card's own evaluations ran at 512K and 1M sequence lengths after what it calls long-context training, so 128K or more is taken as trained. 20 + 25 + 10 + 0 + 10 + 10 = 75.", "b": "The template ships in the repository (20) and renders several tool calls per turn for a model trained for tool use (20). Tool-calling format gets 10, because the card names the qwen3_coder parser but leaves the declaration and call format to the template. Thinking turns off per request and comes back in think tags split by the qwen3 parser (10), and 262,144 tokens is native (10). Structured output scores 0, as nothing documents JSON output for the model." }, "scores": { "a": 75, "b": 70 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Meituan (LongCat)", "b": "Alibaba (Qwen team)", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "MIT", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-11-10", "b": "2026-02-24", "name": "Last release" }, { "a": "not read yet", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "none", "b": "3/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Qwen3.5-122B-A10B scores 63.1 (B) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 1 of 4 scored categories. LongCat-2.5 leads on risk, agent interface and documentation and upkeep.", "question": "Which is better for AI agents, LongCat-2.5 or Qwen3.5-122B-A10B?" } ], "goodFor": [ { "aheadOn": [ "Risk, 60 against 55", "Agent interface, 75 against 70", "Documentation and upkeep, 46 against 34" ], "also": null, "goodFor": "Operators with NPU clusters, and anyone tracking what the open flagships can do", "slug": "longcat-2-5", "watchFor": "3,562 GB in BF16 and 1,336 GB at 4 bits" }, { "aheadOn": [ "Reach, 80 against 35" ], "also": null, "goodFor": "Agents with a 128 GB unified-memory machine or two large GPUs that want a capable vision-language model with a plain licence.", "slug": "qwen3-5-122b-a10b", "watchFor": "Too big for a single 64 GB machine even at 4 bits" } ], "history": { "longcat-2-5": null, "qwen3-5-122b-a10b": [ { "causes": [ "time" ], "date": "2026-11-02", "delta": -0.5, "from": 63.6, "fromGrade": "B", "to": 63.1, "toGrade": "B" } ] }, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-longcat-2-5.json", "title": "Agents-A1-4B vs LongCat-2.5", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-longcat-2-5" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-5-122b-a10b.json", "title": "Agents-A1-4B vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-longcat-2-5.json", "title": "Apertus v1.5 8B vs LongCat-2.5", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-longcat-2-5" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-5-122b-a10b.json", "title": "Apertus v1.5 8B vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-longcat-2-5.json", "title": "Apodex-1.1-mini vs LongCat-2.5", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-longcat-2-5" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-5-122b-a10b.json", "title": "Apodex-1.1-mini vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-longcat-2-5.json", "title": "Arete-27B vs LongCat-2.5", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-longcat-2-5" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-5-122b-a10b.json", "title": "Arete-27B vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-longcat-2-5.json", "title": "BigBang-v1 vs LongCat-2.5", "url": 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"https://www.anchorterminal.com/compare/deepseek-v3-2-vs-longcat-2-5.json", "title": "DeepSeek-V3.2 vs LongCat-2.5", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-longcat-2-5" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-5-122b-a10b.json", "title": "DeepSeek-V3.2 vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-longcat-2-5.json", "title": "DeepSeek V4.1 Flash vs LongCat-2.5", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-longcat-2-5" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-122b-a10b.json", "title": "DeepSeek V4.1 Flash vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-longcat-2-5.json", "title": 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"json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-122b-a10b.json", "title": "Gemma 4 12B IT vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-longcat-2-5.json", "title": "Gemma 4 26B A4B IT vs LongCat-2.5", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-longcat-2-5" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-5-122b-a10b.json", "title": "Gemma 4 26B A4B IT vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-longcat-2-5.json", "title": "Gemma 4 31B IT vs LongCat-2.5", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-longcat-2-5" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-122b-a10b.json", "title": 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}, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-longcat-2-5.json", "title": "GLM-4.5-Air vs LongCat-2.5", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-longcat-2-5" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-5-122b-a10b.json", "title": "GLM-4.5-Air vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-longcat-2-5.json", "title": "GLM-4.7-Flash vs LongCat-2.5", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-longcat-2-5" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-122b-a10b.json", "title": "GLM-4.7-Flash vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-longcat-2-5.json", "title": "GLM-5.3-Flash vs LongCat-2.5", "url": 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"gpt-oss-120b vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-longcat-2-5.json", "title": "gpt-oss-20b vs LongCat-2.5", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-longcat-2-5" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-5-122b-a10b.json", "title": "gpt-oss-20b vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-longcat-2-5.json", "title": "Granite 4.2 30B vs LongCat-2.5", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-longcat-2-5" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-5-122b-a10b.json", "title": "Granite 4.2 30B vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-5-122b-a10b" }, { "json": 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"url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-longcat-2-5" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-122b-a10b.json", "title": "Hy-Memory-30B-A3B vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-longcat-2-5.json", "title": "Hy3 vs LongCat-2.5", "url": "https://www.anchorterminal.com/compare/hy3-vs-longcat-2-5" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-5-122b-a10b.json", "title": "Hy3 vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-longcat-2-5.json", "title": "Inkling-Small vs LongCat-2.5", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-longcat-2-5" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-5-122b-a10b.json", "title": 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Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-vl-32b-instruct.json", "title": "Qwen3.5-122B-A10B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3.5-122B-A10B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-vl-8b-instruct.json", "title": "Qwen3.5-122B-A10B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3.5-122B-A10B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-recursive-flint.json", "title": "Qwen3.5-122B-A10B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-ripple-65b-preview.json", "title": "Qwen3.5-122B-A10B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-sensenova-loop-9b-preview.json", "title": "Qwen3.5-122B-A10B vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-smollm3-3b.json", "title": "Qwen3.5-122B-A10B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-spark-x2-6-4b.json", "title": "Qwen3.5-122B-A10B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-step-3-7-flash.json", "title": "Qwen3.5-122B-A10B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwen3.5-122B-A10B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3.5-122B-A10B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.5-122B-A10B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-unlimited-ocr-2.json", "title": "Qwen3.5-122B-A10B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-wevisone-vl-2b.json", "title": "Qwen3.5-122B-A10B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-wevisone-vl-4b.json", "title": "Qwen3.5-122B-A10B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-zaya1-8b.json", "title": "Qwen3.5-122B-A10B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 45, "edge": "qwen3-5-122b-a10b", "key": "reach", "longcat-2-5": 35, "name": "Reach", "qwen3-5-122b-a10b": 80, "weight": 25 }, { "by": 5, "edge": "longcat-2-5", "key": "risk", "longcat-2-5": 60, "name": "Risk", "qwen3-5-122b-a10b": 55, "weight": 20 }, { "by": 5, "edge": "longcat-2-5", "key": "interface", "longcat-2-5": 75, "name": "Agent interface", "qwen3-5-122b-a10b": 70, "weight": 10 }, { "by": 12, "edge": "longcat-2-5", "key": "upkeep", "longcat-2-5": 46, "name": "Documentation and upkeep", "qwen3-5-122b-a10b": 34, "weight": 10 } ], "summary": "Qwen3.5-122B-A10B scores 63.1 (B) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 1 of 4 scored categories. LongCat-2.5 leads on risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "longcat-2-5": "Meituan's flagship is MIT-licensed and documented for tool calling and switchable reasoning, with strong numbers of its own on agentic coding. Almost nobody can run it: 3,562 GB of weights, a 1,336 GB 4-bit build for NPU accelerators, and no released runtime with its architecture. A model to watch until a runtime or a provider picks it up.", "qwen3-5-122b-a10b": "A strong mid-size cousin for coding, agent steps and reading images or video, with thinking you can switch off per request. Alibaba's 4-bit build needs about 95 GB, which fits one 128 GB unified-memory machine, and several providers host it. The main risk is silence, with no safety results, agent guidance or answers from Alibaba." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-5-122b-a10b", "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-5-122b-a10b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-5-122b-a10b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-5-122b-a10b.min.md" }, "markdown": "Qwen3.5-122B-A10B scores 63.1 (B) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 1 of 4 scored categories. LongCat-2.5 leads on risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself.\n\n- LongCat-2.5: grade D, 50.5/100, rank #145 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/longcat-2-5.md · JSON https://www.anchorterminal.com/api/v1/tools/longcat-2-5.json\n- Qwen3.5-122B-A10B: grade B, 63.1/100, rank #25 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-5-122b-a10b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-5-122b-a10b.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### LongCat-2.5 (D)\n\nGood for: Operators with NPU clusters, and anyone tracking what the open flagships can do\n\nAhead on:\n- Risk, 60 against 55\n- Agent interface, 75 against 70\n- Documentation and upkeep, 46 against 34\n\nWatch for: 3,562 GB in BF16 and 1,336 GB at 4 bits\n\n### Qwen3.5-122B-A10B (B)\n\nGood for: Agents with a 128 GB unified-memory machine or two large GPUs that want a capable vision-language model with a plain licence.\n\nAhead on:\n- Reach, 80 against 35\n\nWatch for: Too big for a single 64 GB machine even at 4 bits\n\n\n## Why the edge\n\n### Reach: Qwen3.5-122B-A10B +45\n\n- LongCat-2.5 (35): Memory 0, the smallest build is Meituan's W4A4 repository, 1,336 GB, and 1,336 GB × 1.2 = 1,603 GB, far above 256 GB. The BF16 files are 3,562 GB. Runtimes 0, none of the six runs it in a released version: vLLM 0.33.0, SGLang 0.5.23, Transformers 5.22.2 and llama.cpp b12242 have no LongcatCausalLM or pro_v code, and the model code config.json names isn't in the repository. Builds 10, one quantised build from Meituan, the W4A4 repository (4-bit experts, 1,336 GB). Terms 15, MIT in the metadata, the LICENSE file and the card, with nothing added. Download 10, the Hub API reports gated false for both repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 0 + 0 + 10 + 15 + 10 + 0 = 35.\n- Qwen3.5-122B-A10B (80): Memory earns 10 of 30, since Alibaba's GPTQ 4-bit build is 78.9 GB, about 95 GB with a fifth added, which fits our reference machine but no smaller band. Runtimes reach the 25 cap on released llama.cpp, Ollama, vLLM, SGLang and Transformers. Alibaba's FP8 and GPTQ-Int4 builds give builds the full 15. Apache 2.0 with nothing added gives terms 15, the ungated repositories give download 10, and Alibaba's own Model Studio plus Novita and DeepInfra give hosted too its 5.\n\n### Documentation and upkeep: LongCat-2.5 +12\n\n- LongCat-2.5 (46): Model card 11, Intended use, limitations (the usage considerations), the architecture and the parameter counts are on the card. Training data 5, A general description of the training stages and where the reinforcement learning tasks came from, with no sources, sizes or cut-off. Last change 20, the last commit, on 10 November 2026, updated the card, on the day of this check. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (9 and 10 November 2026) has a reply from a meituan-longcat member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, LongCat-2.0 stays in its own repositories and 2.5 came out in new ones, with no dated note on the older ones that we read. File health 5, the template is in both places and agrees, but config.json's auto_map names configuration_longcat_oe and modeling_longcat_oe, which aren't in the repository, and the tokenizer config's 131,072-token length is a quarter of the config's 524,288 positions. 11 + 5 + 20 + 0 + 5 + 5 = 46.\n- Qwen3.5-122B-A10B (34): The card covers five of the seven items and misses limitations and a cut-off (14). Training data is a general description without a cut-off (5). The files last changed on 24 April 2026, 192 days before this check, past the 180-day band and within a year (5), and Qwen's GitHub repository holds only a README and licence, so no reference code of Alibaba's changed later. No Qwen member has answered any of the 21 discussions that aren't pull requests, recounted on 2 November (0), and GitHub issues couldn't be counted. It's a first release at this size (5), and a template fix and a run report still wait unanswered (5). 14 + 5 + 5 + 0 + 5 + 5 = 34.\n\n### Risk: LongCat-2.5 +5\n\n- LongCat-2.5 (60): File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in the meituan-longcat organisation, the name of Meituan's GitHub organisation that the card links for deployment, and the card's licence names Meituan (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions under Usage Considerations: not evaluated for every use, assess accuracy, safety and fairness before sensitive use, and follow the law. Licence risk 15, MIT with no use policy, no threshold and no right to change the terms. Security programme 0, no way to report a flaw on the card beyond a general contact, and Meituan's own security pages weren't looked up, so this scores as absent and is an open question. 25 + 15 + 0 + 5 + 15 + 0 = 60.\n- Qwen3.5-122B-A10B (55): Safetensors only with no repository code run on load or to build prompts gives file safety 25, and files in the Qwen organisation the publisher's GitHub links to, each hashed on the Hub, give provenance 15. Plain Apache 2.0 keeps licence risk at 15. Safety evaluation, agent guidance and a security programme score 0, since the card has no safety results or cautions and no reporting route was found. The Qwen3.5 blog and Alibaba's security response centre couldn't be read, so both are scored as absent and left open.\n\n### Agent interface: LongCat-2.5 +5\n\n- LongCat-2.5 (75): Chat template 20, chat_template.jinja in the repository (11,823 characters), with a copy in tokenizer_config.json. Tool-calling format 25, the card documents how tools are declared (the OpenAI function format) and how calls come back (tool_calls with an id, a name and JSON arguments, answered by a tool message), in a worked example against an OpenAI-compatible server. Trained tool calling 10, the card describes reinforcement learning on long agentic tasks and shows one tool call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, enable_thinking on or off per request through chat_template_kwargs, with the reasoning returned as reasoning_content, both shown on the card. Context 10, the card states no context length, but config.json gives 524,288 positions and the card's own evaluations ran at 512K and 1M sequence lengths after what it calls long-context training, so 128K or more is taken as trained. 20 + 25 + 10 + 0 + 10 + 10 = 75.\n- Qwen3.5-122B-A10B (70): The template ships in the repository (20) and renders several tool calls per turn for a model trained for tool use (20). Tool-calling format gets 10, because the card names the qwen3_coder parser but leaves the declaration and call format to the template. Thinking turns off per request and comes back in think tags split by the qwen3 parser (10), and 262,144 tokens is native (10). Structured output scores 0, as nothing documents JSON output for the model.\n\n## Score by category\n\n| Category | Weight | LongCat-2.5 | Qwen3.5-122B-A10B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 35 | 80 | Qwen3.5-122B-A10B +45 |\n| Risk | 20% (20 this run) | 60 | 55 | LongCat-2.5 +5 |\n| Agent interface | 10% (10 this run) | 75 | 70 | LongCat-2.5 +5 |\n| Documentation and upkeep | 10% (10 this run) | 46 | 34 | LongCat-2.5 +12 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **50.5 · D** | **63.1 · B** | |\n\n## Facts side by side\n\n| Fact | LongCat-2.5 | Qwen3.5-122B-A10B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Meituan (LongCat) | Alibaba (Qwen team) |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | MIT | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-11-10 | 2026-02-24 |\n| Terms last updated | not read yet | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | none | 3/5 (4) |\n\n## How the scores have moved\n\n### LongCat-2.5\n\nNo score change since it was listed.\n\n### Qwen3.5-122B-A10B\n\n- -0.5 on 2 November 2026: time\n\n## Verdicts\n\n**LongCat-2.5.** Meituan's flagship is MIT-licensed and documented for tool calling and switchable reasoning, with strong numbers of its own on agentic coding. Almost nobody can run it: 3,562 GB of weights, a 1,336 GB 4-bit build for NPU accelerators, and no released runtime with its architecture. A model to watch until a runtime or a provider picks it up.\n\n**Qwen3.5-122B-A10B.** A strong mid-size cousin for coding, agent steps and reading images or video, with thinking you can switch off per request. Alibaba's 4-bit build needs about 95 GB, which fits one 128 GB unified-memory machine, and several providers host it. The main risk is silence, with no safety results, agent guidance or answers from Alibaba.\n\n## Before you call either\n\n### LongCat-2.5\n\n1. You can't run this on a workstation or a single GPU server. Plan for a provider, and none serves it yet\n2. If an operator serves it, call it as an OpenAI-compatible chat API with tools in the function format\n3. Pass `chat_template_kwargs` with `enable_thinking` true or false\n4. With thinking on, keep earlier reasoning in the history (`save_history_reasoning_content` true), as the card recommends\n5. The card's results use temperature 1.0 and top_p 0.95\n6. Its benchmark table is Meituan's own. The unmarked scores are in-house\n\n### Qwen3.5-122B-A10B\n\n1. Quickest start on a 128 GB machine is `ollama run qwen3.5:122b` (Q4_K_M, 81 GB of files, about 97 GB with context)\n2. Alibaba's 4-bit build serves with `vllm serve Qwen/Qwen3.5-122B-A10B-GPTQ-Int4 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder --quantization moe_wna16`, though that kernel isn't documented for AMD gfx1151\n3. Use the qwen3_coder tool parser and the qwen3 reasoning parser, as the card's commands do\n4. Turn thinking off with `\"chat_template_kwargs\": {\"enable_thinking\": false}` in the request body, since it's on by default and `/think` and `/nothink` aren't supported\n5. Sampling per the card is temperature 0.6, top_p 0.95 and top_k 20 when thinking, and temperature 0.7, top_p 0.8, top_k 20 and presence_penalty 1.5 without\n6. Keep the context at 128K or more if you rely on thinking, and turn on YaRN only past 262,144 tokens\n7. Add `--language-model-only` in vLLM to skip the vision encoder and free memory for the KV cache when you only need text\n8. Validate any JSON it returns, since structured output isn't documented\n\n## Questions\n\n### Which is better for AI agents, LongCat-2.5 or Qwen3.5-122B-A10B?\n\nQwen3.5-122B-A10B scores 63.1 (B) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 1 of 4 scored categories. LongCat-2.5 leads on risk, agent interface and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-5-122b-a10b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-5-122b-a10b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"longcat-2-5\", \"b\": \"qwen3-5-122b-a10b\"}`. From a terminal: `anchor compare longcat-2-5 qwen3-5-122b-a10b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/longcat-2-5.json and https://www.anchorterminal.com/api/v1/tools/qwen3-5-122b-a10b.json\n\n## Other comparisons with LongCat-2.5 or Qwen3.5-122B-A10B\n\n- [Agents-A1-4B vs LongCat-2.5](https://www.anchorterminal.com/compare/agents-a1-4b-vs-longcat-2-5.md)\n- [Agents-A1-4B vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-5-122b-a10b.md)\n- [Apertus v1.5 8B vs LongCat-2.5](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-longcat-2-5.md)\n- [Apertus v1.5 8B vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-5-122b-a10b.md)\n- [Apodex-1.1-mini vs LongCat-2.5](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-longcat-2-5.md)\n- [Apodex-1.1-mini vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-5-122b-a10b.md)\n- [Arete-27B vs 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research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "LongCat-2.5 vs Qwen3.5-122B-A10B", "url": "" } ], "description": "Qwen3.5-122B-A10B scores 63.1 (B) to LongCat-2.5's 50.5 (D) for open-weight models you run yourself. 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