{ "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": "Qwen3.8-Flash-Next scores 61.8 (C) 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": "qwen3-8-flash-next", "name": "Qwen3.8-Flash-Next", "vendor": "Alibaba (Qwen team)", "vendorUrl": "https://qwen.ai", "kind": "weights", "category": "open-weights", "summary": "Qwen3.8-Flash-Next is Alibaba's open-weight multimodal mixture-of-experts model and an experimental preview of the Qwen4 architecture, 125B parameters with 6B active plus a 51B n-gram embedding table, published on Hugging Face in BF16 and FP8.", "url": "https://www.anchorterminal.com/tools/qwen3-8-flash-next", "markdownUrl": "https://www.anchorterminal.com/tools/qwen3-8-flash-next.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen3-8-flash-next.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen3-8-flash-next.json", "dns": "qwen3-8-flash-next.grade.anchorterminal.com", "repo": "https://huggingface.co/Qwen/Qwen3.8-Flash-Next", "license": "Qwen Community License 1.0 (custom, not an OSI licence). Free of charge, the model name shown in products above 100M monthly users or US$20M monthly revenue, and a separate licence from Qwen before commercial use by a Model as a Service or AI Work Assistant business", "transports": [], "packages": [ { "registry": "huggingface", "name": "Qwen/Qwen3.8-Flash-Next" }, { "registry": "huggingface", "name": "Qwen/Qwen3.8-Flash-Next-FP8" }, { "registry": "ollama", "name": "qwen3.8-flash-next" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither repository is gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use under the Qwen Community License 1.0. A licensee whose group runs a Model as a Service or AI Work Assistant business (AI coding or office productivity products) needs a separate licence from Qwen, with no published price, before any commercial use beyond internal use. Running it yourself costs the hardware, 360.0 GB for the BF16 files, 185.5 GB for FP8 and 105 GB for Ollama's MLX 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/Qwen3.8-Flash-Next", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "reasoning", "tool-calling", "source-available", "safetensors", "fp8", "self-hosted", "preview" ], "lastRelease": "2026-08-31", "graded": true, "anchor": { "graded": true, "score": 61.8, "grade": "C", "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": 25, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 74, "interface": 70, "reach": 65, "risk": 45, "upkeep": 36 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-11-02" }, "negative": 0, "verdict": "A large sparse cousin with 6B active parameters for agentic coding and multimodal work, previewing Qwen's next architecture. It needs a 128 GB Mac for Ollama's MLX build or about 144 GB for the 4-bit GGUF, and Qwen Cloud serves it. The main risk is a licence that needs a separate deal for hosted-model and coding or office assistant businesses.", "bestFor": "Teams with a multi-GPU server or a 128 GB Mac who want Qwen's newest sparse architecture for agentic coding and multimodal work, and aren't a hosted-model or coding-assistant business.", "strengths": [ "Released support in llama.cpp, Ollama, vLLM, SGLang and Transformers", "262,144 tokens of native context, several tool calls per turn, and thinking that turns off per request with three effort levels", "Safetensors only, a chat template in the repository and a SHA-256 for every weight file", "No gate on the download, and Alibaba's Qwen Cloud serves it for agents without the hardware" ], "weaknesses": [ "No build fits the reference machine's 107.4 GB, and Ollama's smallest Linux build is 120 GB", "The Qwen Community License requires a separate licence before commercial use by a model-as-a-service or coding or office assistant business", "No safety results, agent guidance, training data, limitations, languages or knowledge cut-off published", "Labelled an experimental preview, and Qwen has answered none of the 40 most recent discussions", "The tool-calling format is only in the template, and structured output isn't documented" ], "agentNotes": [ "On a 128 GB Mac start it with `ollama run qwen3.8-flash-next:125b-mlx` (105 GB, NVFP4), on Linux the smallest Ollama build is `qwen3.8-flash-next:125b-a6b-q4_K_M` at 120 GB, which won't fit a 107.4 GB GPU share", "On a server, `vllm serve Qwen/Qwen3.8-Flash-Next-FP8 --tensor-parallel-size 4 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`", "Use the qwen3_coder tool parser and the qwen3 reasoning parser, as Qwen's GitHub README does", "Thinking is on by default, pass `chat_template_kwargs` with `enable_thinking` false to skip it, and read the reasoning from `reasoning_content`, even though SGLang's cookbook says thinking can't be turned off", "`reasoning_effort` takes xhigh (default), medium or low, and any other value makes the template raise an error", "Send at most one system message and put it first, since the template raises an error otherwise", "Sampling per the card is temperature 1.0 and top_p 0.95 when thinking, temperature 0.7, top_p 0.8 and presence_penalty 1.5 without", "vLLM keeps the 51B n-gram table in host memory on 80 GB GPUs by default, per its recipe", "Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented", "Read the Qwen Community License before serving it to others as an API or inside a coding or office assistant product" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61.8 } ], "editorialScores": { "capability": 74, "interface": 70, "reach": 65, "risk": 45, "upkeep": 36 }, "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", "oldest": "2026-11-02", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run qwen3.8-flash-next:125b-mlx\n# or, as Qwen's GitHub README serves it (4 GPUs):\nvllm serve Qwen/Qwen3.8-Flash-Next --port 8000 --tensor-parallel-size 4 --max-model-len 262144 \\\n --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder\n# or, with llama.cpp and llama.cpp's own GGUF:\nllama-server -hf ggml-org/Qwen3.8-Flash-Next-GGUF:IQ4_NL --jinja", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"Qwen/Qwen3.8-Flash-Next\",\n \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}],\n \"chat_template_kwargs\": {\"enable_thinking\": false}\n }'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/qwen3-8-flash-next" }, "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", "qwen3-5-122b-a10b", "qwen3-5-2b", "qwen3-5-397b-a17b", "qwen3-5-4b", "qwen3-5-9b", "qwen3-6-27b", "qwen3-6-35b-a3b", "qwen3-8-27b" ], "weights": { "trainedBy": "Alibaba (Qwen team)", "params": "125B", "active": "6B", "context": 262144, "goodFor": "Experimental preview of the Qwen4 architecture: a multimodal mixture-of-experts model with tool calling and adjustable reasoning effort.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Qwen3.8-Flash-Next-FP4-Local", "url": "https://huggingface.co/nvidia/Qwen3.8-Flash-Next-FP4-Local", "who": "NVIDIA", "precision": "nvfp4", "sizeGB": 102.2, "memoryGB": 128, "format": "safetensors", "note": "NVIDIA's own FP4 build (Model Optimizer), made for NVIDIA Blackwell GPUs such as DGX Spark and RTX PRO 6000; FP4 kernels don't run on the AMD reference machine. Not the publisher's build, so it doesn't count toward Reach's memory item. Size summed from the Hub's file list on 21 October 2026." }, { "name": "ggml-org Qwen3.8-Flash-Next-GGUF (IQ4_NL)", "url": "https://huggingface.co/ggml-org/Qwen3.8-Flash-Next-GGUF", "who": "llama.cpp", "precision": "iq4_nl", "sizeGB": 102.67, "memoryGB": 128, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From ggml-org, the llama.cpp project's organisation. 102.05 GB of IQ4_NL weights plus the 0.62 GB Q8_0 vision projector, from the Hub's model information on 10 November 2026. The 2-bit Q2_K file (83.1 GB) is below the 4-bit line and isn't listed. Recorded after the last grading, so Reach doesn't count it yet" }, { "name": "ollama qwen3.8-flash-next:125b-a6b-q4_K_M", "url": "https://ollama.com/library/qwen3.8-flash-next:125b-a6b-q4_K_M", "who": "ollama", "precision": "q4_k_m", "sizeGB": 120, "memoryGB": 192, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Smallest non-MLX Ollama build; the library has no latest tag, and its 105 GB 125b-mlx tag is Apple-only. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant" }, { "name": "Qwen3.8-Flash-Next-NVFP4", "url": "https://huggingface.co/nvidia/Qwen3.8-Flash-Next-NVFP4", "who": "NVIDIA", "precision": "nvfp4", "sizeGB": 132.7, "memoryGB": 192, "format": "safetensors", "note": "NVIDIA's own FP4 build (Model Optimizer), made for NVIDIA Blackwell GPUs such as DGX Spark and RTX PRO 6000; FP4 kernels don't run on the AMD reference machine. Not the publisher's build, so it doesn't count toward Reach's memory item. Size summed from the Hub's file list on 21 October 2026." }, { "name": "Qwen3.8-Flash-Next-FP8", "url": "https://huggingface.co/Qwen/Qwen3.8-Flash-Next-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 185.5, "memoryGB": 256, "format": "safetensors" }, { "name": "ggml-org Qwen3.8-Flash-Next-GGUF (Q8_0)", "url": "https://huggingface.co/ggml-org/Qwen3.8-Flash-Next-GGUF", "who": "llama.cpp", "precision": "q8_0", "sizeGB": 163.24, "memoryGB": 256, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From ggml-org, the llama.cpp project's organisation. 162.62 GB of Q8_0 weights plus the 0.62 GB Q8_0 vision projector, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it yet" }, { "name": "Qwen3.8-Flash-Next", "url": "https://huggingface.co/Qwen/Qwen3.8-Flash-Next", "who": "publisher", "precision": "bf16", "sizeGB": 360, "memoryGB": 512, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "vllm serve Qwen/Qwen3.8-Flash-Next-FP8 --tensor-parallel-size 4 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder", "runBuild": "Qwen3.8-Flash-Next-FP8", "checked": "2026-10-21", "minMemoryGB": 128 }, "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": "Qwen3.5-122B-A10B", "url": "https://huggingface.co/Qwen/Qwen3.5-122B-A10B", "params": "122B-A10B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-122b-a10b" }, { "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": "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 (Singapore) Private Limited, the registrant of qwen.ai. The licence names Qwen as copyright holder", "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 (c) 2026 Qwen. RDAP names Alibaba Cloud (Singapore) Private Limited as the registrant of qwen.ai.", "Nothing is hosted, so the Qwen Community License 1.0 stands in for terms of service and no privacy policy is scored.", "No changelog. The BF16 repository's commit messages are an upload and README updates; the FP8 repository's say what changed.", "qwen.ai redirects /.well-known/security.txt to its home page, so there is no security.txt." ], "score": 54 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-8-flash-next.json", "live": { "slug": "qwen3-8-flash-next", "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": 30, "category": "reach", "leads": "qwen3-8-flash-next", "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 5 of 30, the smallest listed 4-bit build is Ollama's 120 GB q4_K_M, 144 GB with a fifth added, which is beyond the reference machine and inside 256 GB. Ollama's Apple-only 105 GB MLX build isn't among the builds and would land in the same band. Runtimes 25, released versions of llama.cpp, Ollama, vLLM, SGLang and Transformers all run the qwen4_exp architecture. Builds 10 of 15, Alibaba's only quantised build is FP8. Terms 10 of 15, a custom licence that's free for commercial use except for model-as-a-service and coding or office assistant businesses, which need a separate licence. Download 10, neither repository is gated. Hosted 5, Hugging Face lists two live providers, Featherless AI for the BF16 repository and Nscale for the FP8 build, and Alibaba's Qwen Cloud serves Qwen3.8-Flash, which the card says is based on these weights." }, "scores": { "a": 35, "b": 65 } }, { "by": 15, "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": "File safety 25, safetensors only and nothing from the repository runs to load it or build prompts. Provenance 15, the files are in the Qwen organisation that Qwen's GitHub links to, and every file has a checksum. Safety evaluation 0, none published on the card or in the technical report. Agent guidance 0, nothing on prompt injection, refusals or safeguards. Licence risk 5 of 15, 5 off for the field-of-use limit on model-as-a-service and coding or office assistant businesses and 5 off for the naming duty above a user or revenue threshold. Security programme 0, no security.txt, disclosure policy or security contact for Qwen was found, and Alibaba's group security centre couldn't be checked, so it's scored as absent." }, "scores": { "a": 60, "b": 45 } }, { "by": 10, "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": "Model card 11 of 20, intended use, architecture, parameters and context are there, but limitations, languages and a knowledge cut-off aren't. Training data 0, not described beyond a token count relative to an earlier model. Last change 15, the FP8 config fix on 31 August 2026, 63 days before 2 November. Responsiveness 0, Qwen answered none of the 40 most recent questions and reports (29 to 79, 27 August to 26 October), its only reply came in the first week. Version policy 5, a first release with nothing replaced. File health 5, the files agree with the card, but a 19 October report about the template and a pull request to fix it still wait without a reply. 11 + 0 + 15 + 0 + 5 + 5 = 36." }, "scores": { "a": 46, "b": 36 } }, { "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": "Chat template 20, a Jinja file in the repository. Tool-calling format 10 of 25, the format lives only in the template and the parser name only in serving commands, and Alibaba doesn't describe how tools are declared or returned. Trained tool calling 20, the template renders several calls in one turn. Structured output 0, not documented for the weights. Reasoning control 10, thinking turns off per request, reasoning_effort budgets it and the card shows the reasoning coming back in reasoning_content. Context 10, 262,144 tokens native." }, "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": "Qwen Community License 1.0 (custom, not an OSI licence). Free of charge, the model name shown in products above 100M monthly users or US$20M monthly revenue, and a separate licence from Qwen before commercial use by a Model as a Service or AI Work Assistant business", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-11-10", "b": "2026-08-31", "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": "2/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Qwen3.8-Flash-Next scores 61.8 (C) 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 Qwen3.8-Flash-Next?" } ], "goodFor": [ { "aheadOn": [ "Risk, 60 against 45", "Agent interface, 75 against 70", "Documentation and upkeep, 46 against 36" ], "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, 65 against 35" ], "also": null, "goodFor": "Teams with a multi-GPU server or a 128 GB Mac who want Qwen's newest sparse architecture for agentic coding and multimodal work, and aren't a hosted-model or coding-assistant business.", "slug": "qwen3-8-flash-next", "watchFor": "No build fits the reference machine's 107.4 GB, and Ollama's smallest Linux build is 120 GB" } ], "history": null, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { 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"Qwen3.8-Flash-Next vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-sensenova-loop-9b-preview.json", "title": "Qwen3.8-Flash-Next vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-smollm3-3b.json", "title": "Qwen3.8-Flash-Next vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-spark-x2-6-4b.json", "title": "Qwen3.8-Flash-Next vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-step-3-7-flash.json", "title": "Qwen3.8-Flash-Next vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwen3.8-Flash-Next vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3.8-Flash-Next vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.8-Flash-Next vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-unlimited-ocr-2.json", "title": "Qwen3.8-Flash-Next vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-wevisone-vl-2b.json", "title": "Qwen3.8-Flash-Next vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-wevisone-vl-4b.json", "title": "Qwen3.8-Flash-Next vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-zaya1-8b.json", "title": "Qwen3.8-Flash-Next vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 30, "edge": "qwen3-8-flash-next", "key": "reach", "longcat-2-5": 35, "name": "Reach", "qwen3-8-flash-next": 65, "weight": 25 }, { "by": 15, "edge": "longcat-2-5", "key": "risk", "longcat-2-5": 60, "name": "Risk", "qwen3-8-flash-next": 45, "weight": 20 }, { "by": 5, "edge": "longcat-2-5", "key": "interface", "longcat-2-5": 75, "name": "Agent interface", "qwen3-8-flash-next": 70, "weight": 10 }, { "by": 10, "edge": "longcat-2-5", "key": "upkeep", "longcat-2-5": 46, "name": "Documentation and upkeep", "qwen3-8-flash-next": 36, "weight": 10 } ], "summary": "Qwen3.8-Flash-Next scores 61.8 (C) 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.", "qwen3-8-flash-next": "A large sparse cousin with 6B active parameters for agentic coding and multimodal work, previewing Qwen's next architecture. It needs a 128 GB Mac for Ollama's MLX build or about 144 GB for the 4-bit GGUF, and Qwen Cloud serves it. The main risk is a licence that needs a separate deal for hosted-model and coding or office assistant businesses." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-8-flash-next", "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-8-flash-next.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-8-flash-next.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-8-flash-next.min.md" }, "markdown": "Qwen3.8-Flash-Next scores 61.8 (C) 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- Qwen3.8-Flash-Next: grade C, 61.8/100, rank #34 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-8-flash-next.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-8-flash-next.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 45\n- Agent interface, 75 against 70\n- Documentation and upkeep, 46 against 36\n\nWatch for: 3,562 GB in BF16 and 1,336 GB at 4 bits\n\n### Qwen3.8-Flash-Next (C)\n\nGood for: Teams with a multi-GPU server or a 128 GB Mac who want Qwen's newest sparse architecture for agentic coding and multimodal work, and aren't a hosted-model or coding-assistant business.\n\nAhead on:\n- Reach, 65 against 35\n\nWatch for: No build fits the reference machine's 107.4 GB, and Ollama's smallest Linux build is 120 GB\n\n\n## Why the edge\n\n### Reach: Qwen3.8-Flash-Next +30\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- Qwen3.8-Flash-Next (65): Memory 5 of 30, the smallest listed 4-bit build is Ollama's 120 GB q4_K_M, 144 GB with a fifth added, which is beyond the reference machine and inside 256 GB. Ollama's Apple-only 105 GB MLX build isn't among the builds and would land in the same band. Runtimes 25, released versions of llama.cpp, Ollama, vLLM, SGLang and Transformers all run the qwen4_exp architecture. Builds 10 of 15, Alibaba's only quantised build is FP8. Terms 10 of 15, a custom licence that's free for commercial use except for model-as-a-service and coding or office assistant businesses, which need a separate licence. Download 10, neither repository is gated. Hosted 5, Hugging Face lists two live providers, Featherless AI for the BF16 repository and Nscale for the FP8 build, and Alibaba's Qwen Cloud serves Qwen3.8-Flash, which the card says is based on these weights.\n\n### Risk: LongCat-2.5 +15\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- Qwen3.8-Flash-Next (45): File safety 25, safetensors only and nothing from the repository runs to load it or build prompts. Provenance 15, the files are in the Qwen organisation that Qwen's GitHub links to, and every file has a checksum. Safety evaluation 0, none published on the card or in the technical report. Agent guidance 0, nothing on prompt injection, refusals or safeguards. Licence risk 5 of 15, 5 off for the field-of-use limit on model-as-a-service and coding or office assistant businesses and 5 off for the naming duty above a user or revenue threshold. Security programme 0, no security.txt, disclosure policy or security contact for Qwen was found, and Alibaba's group security centre couldn't be checked, so it's scored as absent.\n\n### Documentation and upkeep: LongCat-2.5 +10\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- Qwen3.8-Flash-Next (36): Model card 11 of 20, intended use, architecture, parameters and context are there, but limitations, languages and a knowledge cut-off aren't. Training data 0, not described beyond a token count relative to an earlier model. Last change 15, the FP8 config fix on 31 August 2026, 63 days before 2 November. Responsiveness 0, Qwen answered none of the 40 most recent questions and reports (29 to 79, 27 August to 26 October), its only reply came in the first week. Version policy 5, a first release with nothing replaced. File health 5, the files agree with the card, but a 19 October report about the template and a pull request to fix it still wait without a reply. 11 + 0 + 15 + 0 + 5 + 5 = 36.\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- Qwen3.8-Flash-Next (70): Chat template 20, a Jinja file in the repository. Tool-calling format 10 of 25, the format lives only in the template and the parser name only in serving commands, and Alibaba doesn't describe how tools are declared or returned. Trained tool calling 20, the template renders several calls in one turn. Structured output 0, not documented for the weights. Reasoning control 10, thinking turns off per request, reasoning_effort budgets it and the card shows the reasoning coming back in reasoning_content. Context 10, 262,144 tokens native.\n\n## Score by category\n\n| Category | Weight | LongCat-2.5 | Qwen3.8-Flash-Next | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 35 | 65 | Qwen3.8-Flash-Next +30 |\n| Risk | 20% (20 this run) | 60 | 45 | LongCat-2.5 +15 |\n| Agent interface | 10% (10 this run) | 75 | 70 | LongCat-2.5 +5 |\n| Documentation and upkeep | 10% (10 this run) | 46 | 36 | LongCat-2.5 +10 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **50.5 · D** | **61.8 · C** | |\n\n## Facts side by side\n\n| Fact | LongCat-2.5 | Qwen3.8-Flash-Next |\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 | Qwen Community License 1.0 (custom, not an OSI licence). Free of charge, the model name shown in products above 100M monthly users or US$20M monthly revenue, and a separate licence from Qwen before commercial use by a Model as a Service or AI Work Assistant business |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-11-10 | 2026-08-31 |\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 | 2/5 (4) |\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**Qwen3.8-Flash-Next.** A large sparse cousin with 6B active parameters for agentic coding and multimodal work, previewing Qwen's next architecture. It needs a 128 GB Mac for Ollama's MLX build or about 144 GB for the 4-bit GGUF, and Qwen Cloud serves it. The main risk is a licence that needs a separate deal for hosted-model and coding or office assistant businesses.\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### Qwen3.8-Flash-Next\n\n1. On a 128 GB Mac start it with `ollama run qwen3.8-flash-next:125b-mlx` (105 GB, NVFP4), on Linux the smallest Ollama build is `qwen3.8-flash-next:125b-a6b-q4_K_M` at 120 GB, which won't fit a 107.4 GB GPU share\n2. On a server, `vllm serve Qwen/Qwen3.8-Flash-Next-FP8 --tensor-parallel-size 4 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`\n3. Use the qwen3_coder tool parser and the qwen3 reasoning parser, as Qwen's GitHub README does\n4. Thinking is on by default, pass `chat_template_kwargs` with `enable_thinking` false to skip it, and read the reasoning from `reasoning_content`, even though SGLang's cookbook says thinking can't be turned off\n5. `reasoning_effort` takes xhigh (default), medium or low, and any other value makes the template raise an error\n6. Send at most one system message and put it first, since the template raises an error otherwise\n7. Sampling per the card is temperature 1.0 and top_p 0.95 when thinking, temperature 0.7, top_p 0.8 and presence_penalty 1.5 without\n8. vLLM keeps the 51B n-gram table in host memory on 80 GB GPUs by default, per its recipe\n9. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented\n10. Read the Qwen Community License before serving it to others as an API or inside a coding or office assistant product\n\n## Questions\n\n### Which is better for AI agents, LongCat-2.5 or Qwen3.8-Flash-Next?\n\nQwen3.8-Flash-Next scores 61.8 (C) 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-qwen3-8-flash-next.json, and with the fewest tokens: https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-8-flash-next.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"longcat-2-5\", \"b\": \"qwen3-8-flash-next\"}`. From a terminal: `anchor compare longcat-2-5 qwen3-8-flash-next`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/longcat-2-5.json and https://www.anchorterminal.com/api/v1/tools/qwen3-8-flash-next.json\n\n## Other comparisons with LongCat-2.5 or Qwen3.8-Flash-Next\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 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-8-flash-next.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 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-8-flash-next.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 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-8-flash-next.md)\n- [Arete-27B vs LongCat-2.5](https://www.anchorterminal.com/compare/arete-27b-vs-longcat-2-5.md)\n- [Arete-27B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-8-flash-next.md)\n- [BigBang-v1 vs LongCat-2.5](https://www.anchorterminal.com/compare/bigbang-v1-vs-longcat-2-5.md)\n- [BigBang-v1 vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-8-flash-next.md)\n- [Carbon-X vs LongCat-2.5](https://www.anchorterminal.com/compare/carbon-x-vs-longcat-2-5.md)\n- [Carbon-X vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-8-flash-next.md)\n- [Cola2 vs LongCat-2.5](https://www.anchorterminal.com/compare/cola2-vs-longcat-2-5.md)\n- [Cola2 vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/cola2-vs-qwen3-8-flash-next.md)\n- [Cosmos-Reason2-2B vs LongCat-2.5](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-longcat-2-5.md)\n- [Cosmos-Reason2-2B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-8-flash-next.md)\n- [Darwin-36B-Opus vs LongCat-2.5](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-longcat-2-5.md)\n- [Darwin-36B-Opus vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-8-flash-next.md)\n- [DeepSeek-V3.2 vs LongCat-2.5](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-longcat-2-5.md)\n- [DeepSeek-V3.2 vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-8-flash-next.md)\n- [DeepSeek V4.1 Flash vs LongCat-2.5](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-longcat-2-5.md)\n- [DeepSeek V4.1 Flash vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-8-flash-next.md)\n- [DeepSeek-V4-Flash-0731 vs LongCat-2.5](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-longcat-2-5.md)\n- [DeepSeek-V4-Flash-0731 vs 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