{ "data": { "a": { "slug": "devstral-small-2", "name": "Devstral Small 2", "vendor": "Mistral AI", "vendorUrl": "https://mistral.ai", "kind": "weights", "category": "open-weights", "summary": "Devstral Small 2 is Mistral's open-weight coding agent model, 24B parameters with image input, tool calling and a 256K context. Apache 2.0. It runs in vLLM, SGLang, Transformers, llama.cpp and Ollama, where the 4-bit build is 15 GB.", "url": "https://www.anchorterminal.com/tools/devstral-small-2", "markdownUrl": "https://www.anchorterminal.com/tools/devstral-small-2.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/devstral-small-2.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/devstral-small-2.json", "dns": "devstral-small-2.grade.anchorterminal.com", "repo": "https://huggingface.co/mistralai/Devstral-Small-2-24B-Instruct-2512", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "mistralai/Devstral-Small-2-24B-Instruct-2512" }, { "registry": "ollama", "name": "devstral-small-2" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use under Apache 2.0. Running it yourself takes 15 GB at Q4_K_M from Ollama's library, 26 GB in FP8 from Mistral, or 26 GB at Q8_0 from ggml-org. Mistral's API serves it too (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/mistralai/Devstral-Small-2-24B-Instruct-2512", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "coding", "agentic", "vision", "tool-calling", "long-context", "apache-2.0", "safetensors", "fp8", "gguf", "ollama", "self-hosted" ], "lastRelease": "2026-07-15", "graded": true, "anchor": { "graded": true, "score": 61, "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": 50, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 65, "reach": 90, "risk": 60, "upkeep": 46 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "The easy one to run: a 24B coding agent under Apache 2.0 that five runtimes carry, 15 GB at 4 bits in Ollama, with a 256K context, image input and well-documented tool calling. It's eleven months old, calls tools one at a time, has no reasoning mode, and its card says nothing on limits, safety or data.", "bestFor": "Developers who want a dependable local coding agent on one consumer GPU or a 32 GB Mac", "strengths": [ "Runs in vLLM, SGLang, Transformers, llama.cpp and Ollama", "15 GB at 4 bits from Ollama's library", "Apache 2.0, ungated, and on Mistral's API", "Tool calling documented, with test prompts and expected output", "A 256K context with image input" ], "weaknesses": [ "No limitations, safety results, languages or data on the card", "No reasoning mode", "Tool calls one at a time, by the card's own tests", "Released in December 2025, and newer coding models have passed it", "Mistral ships FP8 only. The 4-bit builds are other people's" ], "agentNotes": [ "`ollama run devstral-small-2` is the quickest start, at 15 GB", "On vLLM pass `--tool-call-parser mistral --enable-auto-tool-choice`", "Send the system prompt from CHAT_SYSTEM_PROMPT.txt in the repository. The card says results depend on it", "Use a low temperature. The repository's default is 0.15", "Plan for one tool call per turn", "The same model is on our Mistral API listing if you'd rather not host it" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 65, "reach": 90, "risk": 60, "upkeep": 46 }, "provenanceScore": 84, "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": "ollama run devstral-small-2\n# or: vllm serve mistralai/Devstral-Small-2-24B-Instruct-2512 --max-model-len 262144 --tool-call-parser mistral --enable-auto-tool-choice", "http": "# vLLM serves an OpenAI-compatible API at http://localhost:8000/v1, and Ollama at http://localhost:11434/v1.\n# Send the system prompt from CHAT_SYSTEM_PROMPT.txt and tools in the OpenAI function format" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/devstral-small-2" }, "sameCompany": [ "mistral-embeddings", "mistral-moderation", "mistral-voxtral-transcribe", "voxtral-mini-4b-realtime-2602", "mistral-ocr", "mistral-vibe-cli", "mistral-large-4" ], "sameModel": [ "mistral-api" ], "weights": { "trainedBy": "Mistral AI", "builtOn": "Mistral-Small-3.1-24B-Base-2503, per the metadata", "params": "24B", "context": 262144, "goodFor": "A coding agent on one 24 GB GPU or a 32 GB Mac: exploring a repository, editing files and calling tools, with screenshots and diagrams as input.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Ollama devstral-small-2 (Q4_K_M)", "url": "https://ollama.com/library/devstral-small-2", "who": "ollama", "precision": "q4_k_m", "sizeGB": 15, "memoryGB": 24, "format": "ollama", "runsOn": [ "Ollama" ], "note": "From Ollama's own library, 15 GB as its tags page lists it" }, { "name": "Devstral-Small-2-24B-Instruct-2512 (FP8 safetensors)", "url": "https://huggingface.co/mistralai/Devstral-Small-2-24B-Instruct-2512", "who": "publisher", "precision": "fp8", "sizeGB": 25.79, "memoryGB": 32, "format": "safetensors", "runsOn": [ "vLLM", "SGLang", "Transformers" ], "note": "Mistral's release precision. The repository holds it in two layouts" }, { "name": "ggml-org Devstral-Small-2-24B-Instruct-2512-GGUF (Q8_0)", "url": "https://huggingface.co/ggml-org/Devstral-Small-2-24B-Instruct-2512-GGUF", "who": "llama.cpp", "precision": "q8_0", "sizeGB": 25.94, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From ggml-org, the llama.cpp project's organisation. 25.06 GB of Q8_0 weights plus the 0.88 GB F16 projector, from the Hub's model information on 10 November 2026" }, { "name": "Ollama devstral-small-2 (Q8_0)", "url": "https://ollama.com/library/devstral-small-2", "who": "ollama", "precision": "q8_0", "sizeGB": 26, "memoryGB": 32, "format": "ollama", "runsOn": [ "Ollama" ], "note": "From Ollama's own library, 26 GB as its tags page lists it" } ], "run": "ollama run devstral-small-2", "runBuild": "Ollama devstral-small-2 (Q4_K_M)", "checked": "2026-11-10", "minMemoryGB": 24 }, "family": { "lab": "Mistral AI", "family": "Devstral", "url": "https://www.anchorterminal.com/open-weights/#mistral", "olderCount": 49 }, "area": "models", "provenance": { "legalEntity": "Mistral AI (RCS Paris 952 418 325)", "domain": "mistral.ai", "domainRegistered": "2019-05-15", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/mistralai/Devstral-Small-2-24B-Instruct-2512/commits/main", "securityTxt": "valid", "checked": "2026-11-10", "notes": [ "The files sit in Mistral's verified mistralai organisation. Legal entity, domain and security.txt are carried from our Mistral Large 4 listing.", "Nothing is hosted under this listing, so Apache 2.0 stands in for terms of service and no privacy policy is scored. The Mistral API that serves this model has its own listing.", "The changelog is the commit history, 25 commits from 9 December 2025 to 15 July 2026." ], "score": 84 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/devstral-small-2.json" }, "answer": "Devstral Small 2 scores 61 (C) on agent readiness against Qwen3-VL-8B-Instruct's 54.9 (C), and leads in 3 of 4 scored categories. Qwen3-VL-8B-Instruct leads on reach.", "b": { "slug": "qwen3-vl-8b-instruct", "name": "Qwen3-VL-8B-Instruct", "vendor": "Alibaba (Qwen team)", "vendorUrl": "https://qwen.ai", "kind": "weights", "category": "open-weights", "summary": "Qwen3-VL-8B-Instruct is Alibaba's open-weight dense vision-language model of about 8B parameters, taking text, images and video with a 262,144-token context, published on Hugging Face in BF16, FP8 and GGUF under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/qwen3-vl-8b-instruct", "markdownUrl": "https://www.anchorterminal.com/tools/qwen3-vl-8b-instruct.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen3-vl-8b-instruct.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen3-vl-8b-instruct.json", "dns": "qwen3-vl-8b-instruct.grade.anchorterminal.com", "repo": "https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Qwen/Qwen3-VL-8B-Instruct" }, { "registry": "huggingface", "name": "Qwen/Qwen3-VL-8B-Instruct-FP8" }, { "registry": "huggingface", "name": "Qwen/Qwen3-VL-8B-Instruct-GGUF" }, { "registry": "ollama", "name": "qwen3-vl:8b-instruct" } ], "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. Running it yourself costs the hardware, 17.5 GB for the BF16 files, 10.6 GB for Alibaba's FP8 build and 5.0 GB plus a 0.75 to 1.16 GB vision file for its Q4_K_M GGUF (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-VL-8B-Instruct", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "multimodal", "vision", "video", "tool-calling", "apache-2.0", "safetensors", "fp8", "gguf", "self-hosted" ], "lastRelease": "2025-11-01", "graded": true, "anchor": { "graded": true, "score": 54.9, "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": 93, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 28, "interface": 60, "reach": 100, "risk": 55, "upkeep": 31 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-11-04" }, "negative": 0, "verdict": "Qwen3-VL-8B-Instruct reads screenshots, documents, charts and video, and Alibaba's Q4_K_M GGUF needs about 6 GB. Transformers, vLLM, SGLang, llama.cpp and Ollama run it under plain Apache 2.0. It has no thinking mode, documents tool calls only in the template, and has no published safety work, so add guardrails before it drives a GUI.", "bestFor": "An agent with 8 GB or more to spare that needs a local model to read images, documents and video", "strengths": [ "About 6 GB of memory for Alibaba's own Q4_K_M GGUF, with FP8 and Q8_0 builds as well", "Runs in Transformers, vLLM, SGLang, llama.cpp and Ollama in released versions", "Apache 2.0 with no use policy, downloadable with no account", "262,144 tokens of native context over text, images and video", "Safetensors and GGUF only, with a chat template in the repository", "Also served by Featherless and Parasail for agents without the hardware" ], "weaknesses": [ "No safety evaluation, agent-use guidance or security contact", "Tool calling isn't documented beyond the template, and Alibaba names no parser", "No thinking mode in this edition and no documented JSON output", "No reply from Qwen to any of the 19 Hugging Face discussions that aren't pull requests, the newest from 23 August 2026", "The card leaves out limitations, languages, knowledge cut-off and memory needs" ], "agentNotes": [ "Start it with `llama-server -hf Qwen/Qwen3-VL-8B-Instruct-GGUF:Q4_K_M --jinja -c 32768`, or `ollama run qwen3-vl:8b-instruct`", "In Ollama the plain `qwen3-vl:8b` tag is the Thinking edition, so name `8b-instruct`", "With the GGUF, load the mmproj vision file too, or images won't be read", "Parse tool calls as JSON inside `\u003ctool_call\u003e` tags, or use SGLang's `--tool-call-parser qwen`", "Use the card's sampling, `temperature=0.7, top_p=0.8, top_k=20, presence_penalty=1.5` for vision and `temperature=1.0, top_p=1.0, top_k=40, presence_penalty=2.0` for text", "Validate JSON output yourself, since structured output isn't documented", "vLLM needs 0.11.0 or later and Transformers 4.57.0 or later" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 54.9 } ], "editorialScores": { "capability": 28, "interface": 60, "reach": 100, "risk": 55, "upkeep": 31 }, "provenanceScore": 54, "checked": { "categories": { "capability": "2026-11-04", "interface": "2026-11-04", "reach": "2026-11-04", "risk": "2026-11-04", "upkeep": "2026-11-04" }, "last": "2026-11-04", "oldest": "2026-11-04", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run qwen3-vl:8b-instruct\n# or with vLLM 0.11.0 or later, per Qwen's README:\nvllm serve Qwen/Qwen3-VL-8B-Instruct-FP8\n# or with SGLang, using the tool parser its cookbook names for Qwen3-VL:\npython -m sglang.launch_server --model-path Qwen/Qwen3-VL-8B-Instruct --tool-call-parser qwen", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"qwen3-vl:8b-instruct\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/qwen3-vl-8b-instruct" }, "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", "qwen3-5-0-8b", "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", "qwen3-8-flash-next" ], "weights": { "trainedBy": "Alibaba (Qwen team)", "params": "8.77B", "context": 262144, "goodFor": "Vision-language model for images, documents and video with text out, no thinking mode in this Instruct edition.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Qwen3VL-8B-Instruct-Q4_K_M", "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct-GGUF/blob/main/Qwen3VL-8B-Instruct-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 5, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Add mmproj-Qwen3VL-8B-Instruct-Q8_0.gguf (0.8 GB) for image input" }, { "name": "ollama qwen3-vl:8b-instruct", "url": "https://ollama.com/library/qwen3-vl:8b-instruct", "who": "ollama", "precision": "q4_k_m", "sizeGB": 6.2, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q4_K_M with its vision projector. qwen3-vl:8b, the library default, is the Thinking edition. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant" }, { "name": "Qwen3VL-8B-Instruct-Q8_0", "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct-GGUF/blob/main/Qwen3VL-8B-Instruct-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 8.7, "memoryGB": 12, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Add an mmproj file for image input" }, { "name": "Qwen3-VL-8B-Instruct-FP8", "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 10.6, "memoryGB": 16, "format": "safetensors" }, { "name": "Qwen3-VL-8B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct", "who": "publisher", "precision": "bf16", "sizeGB": 17.5, "memoryGB": 24, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] }, { "name": "Qwen3VL-8B-Instruct-F16", "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct-GGUF/blob/main/Qwen3VL-8B-Instruct-F16.gguf", "who": "publisher", "precision": "f16", "sizeGB": 16.4, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Add an mmproj file (0.8 or 1.2 GB) for image input" } ], "run": "llama-server -hf Qwen/Qwen3-VL-8B-Instruct-GGUF:Q4_K_M --jinja -c 32768", "runBuild": "Qwen3VL-8B-Instruct-Q4_K_M", "checked": "2026-11-04", "minMemoryGB": 8 }, "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-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": "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 (per the pilot's RDAP check of qwen.ai; this model's files name no copyright holder)", "domain": "qwen.ai", "domainRegistered": "2023-08-27", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The weights repository has no LICENSE file and the GitHub repository's Apache 2.0 LICENSE leaves the copyright line as the template. RDAP named Alibaba Cloud (Singapore) Private Limited as the registrant of qwen.ai at the pilot's check on 20 October 2026; rdap.org didn't answer with JSON on 21 October.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "No changelog. The GitHub README has a dated news list of releases, and the Hugging Face commit messages are README updates and four merged pull requests from 15 October 2025.", "qwen.ai/.well-known/security.txt redirects to the home page, so there is no security.txt." ], "score": 54 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-vl-8b-instruct.json", "live": { "slug": "qwen3-vl-8b-instruct", "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": 15, "category": "upkeep", "leads": "devstral-small-2", "name": "Documentation and upkeep", "reasons": { "a": "Model card 11, Intended use, the architecture, the parameters and the context are on the card. Training data 0, Nothing on the card about the training data. Last change 10, the last commit, on 15 July 2026, updated the files to match mistral-common, 118 days before this check and within 180. Responsiveness 10, Of the 23 most recent discussions that aren't pull requests (9 December 2025 to 6 November 2026), 11 have a reply from a mistralai member, which is some. Version policy 5, Devstral Small 1.1 stays in its own repository, which this card links as the predecessor, with no dated note on the older card that we read. File health 10, Config, template, tokenizer and card agree, and the June and July commits fixed stray tokens and brought the files in line with mistral-common. 11 + 0 + 10 + 10 + 5 + 10 = 46.", "b": "The card misses limitations, languages and a cut-off (11). Training data is described only in the technical report, with no cut-off (5). The last change to code that runs it was the qwen-vl-utils fix merged on 4 January 2026, and the weights haven't changed since November 2025; the Hub commits for all three repositories and the QwenLM/Qwen3-VL head (30 January 2026) were the same on 4 November (5). Of 34 Hugging Face discussions on 4 November, the 19 that aren't pull requests (newest 23 August 2026) have no reply from Qwen, and GitHub issues weren't counted (0). Earlier versions kept in their own repositories without notes (5). Two reports wait without a reply (5). 11+5+5+0+5+5 = 31." }, "scores": { "a": 46, "b": 31 } }, { "by": 10, "category": "reach", "leads": "qwen3-vl-8b-instruct", "name": "Reach", "reasons": { "a": "Memory 25, the smallest build at 4 bits from a runtime's own library is Ollama's devstral-small-2 at Q4_K_M, 15 GB, and 15 GB × 1.2 = 18 GB, within 24 GB. Mistral's own FP8 files are 25.79 GB. Runtimes 25, Released vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 have the mistral3 model, llama.cpp b12242 has the architecture with a GGUF from its own organisation, and Ollama's library has devstral-small-2. Builds 10, one quantised precision from Mistral, the FP8 weights it releases. Terms 15, Apache 2.0 in the card's metadata and text, an OSI licence with nothing added. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, Mistral's own API serves Devstral Small 2: the card says the model is available through its API and points there when a local runtime underperforms. 25 + 25 + 10 + 15 + 10 + 5 = 90.", "b": "Alibaba's own Q4_K_M GGUF is 5.03 GB, so with a fifth added it needs about 6 GB and fits in 16 GB (30). Transformers 4.57.0, vLLM, SGLang and llama.cpp run it in released versions, and Ollama v0.40.14 pins llama.cpp b11963, which has the qwen3vl architecture, so Ollama is credited through its pin; five runtimes reach the 25 cap. Alibaba publishes FP8 and GGUF builds at F16, Q8_0 and Q4_K_M (15). Apache 2.0 with nothing added (15). No gate and no account (10). Featherless is live in the Hub's provider mapping on 4 November, and Parasail served it per the 21 October check, not re-read, so it's hosted too (5). Novita's mapping still shows an error and isn't counted. 30+25+15+15+10+5 = 100." }, "scores": { "a": 90, "b": 100 } }, { "by": 5, "category": "risk", "leads": "devstral-small-2", "name": "Risk", "reasons": { "a": "File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, files in Mistral's verified mistralai organisation, which Mistral's site links to per our Mistral Large 4 listing (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on prompt injection, instruction priority or what the model refuses. Licence risk 15, Apache 2.0 has no use policy, threshold or change clause. Security programme 5, mistral.ai's security.txt gives a security contact and a submission policy, as read for the Mistral Large 4 listing (5). 25 + 15 + 0 + 0 + 15 + 5 = 60.", "b": "Safetensors and GGUF only and no code in the repositories, so the FP8 card's trust_remote_code flag runs nothing (25). Files in the Qwen organisation with a checksum on every file (15). No safety evaluation at all (0). No guidance for agent use, which matters for a model sold as a GUI agent (0). Apache 2.0 carries none of the licence risks (15). No security contact, disclosure policy or public advisories found for Qwen (0), and Alibaba's group-wide security centre wasn't checked. 25+15+0+0+15+0 = 55." }, "scores": { "a": 60, "b": 55 } }, { "by": 5, "category": "interface", "leads": "devstral-small-2", "name": "Agent interface", "reasons": { "a": "Chat template 20, chat_template.jinja in the repository (11,362 characters), with the tokenizer's tekken file for mistral-common. Tool-calling format 25, the card documents tools in the OpenAI function format with an example, names the mistral tool-call parser with auto tool choice for vLLM, shows the same for SGLang, and ships test prompts with the expected tool-call output. Trained tool calling 10, the card says the model excels at using tools, and its tests cover one tool call and calls made one at a time in sequence. Structured output 0, Nothing on the card about JSON or schema-constrained answers for this model. Reasoning control 0, no reasoning mode is documented, and a June 2026 commit removed stray thinking tokens from the files. Context 10, 256K tokens per the card, served at 262,144 in its vLLM command. 20 + 25 + 10 + 0 + 0 + 10 = 65.", "b": "A Jinja chat template in the repository (20). The tool-call format lives only in the template, Hermes-style JSON in tool_call tags, and Alibaba names no parser (10). Trained for function calling with several calls allowed in a turn (20). No documented structured output (0). No thinking mode in this edition, so reasoning control scores 0 by the rule. 262,144 tokens of native context (10). 20+10+20+0+0+10 = 60." }, "scores": { "a": 65, "b": 60 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Mistral AI", "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": "Apache-2.0", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-07-15", "b": "2025-11-01", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "none", "b": "2.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Devstral Small 2 scores 61 (C) on agent readiness against Qwen3-VL-8B-Instruct's 54.9 (C), and leads in 3 of 4 scored categories. Qwen3-VL-8B-Instruct leads on reach.", "question": "Which is better for AI agents, Devstral Small 2 or Qwen3-VL-8B-Instruct?" } ], "goodFor": [ { "aheadOn": [ "Risk, 60 against 55", "Agent interface, 65 against 60", "Documentation and upkeep, 46 against 31" ], "also": null, "goodFor": "Developers who want a dependable local coding agent on one consumer GPU or a 32 GB Mac", "slug": "devstral-small-2", "watchFor": "No limitations, safety results, languages or data on the card" }, { "aheadOn": [ "Reach, 100 against 90" ], "also": null, "goodFor": "An agent with 8 GB or more to spare that needs a local model to read images, documents and video", "slug": "qwen3-vl-8b-instruct", "watchFor": "No safety evaluation, agent-use guidance or security contact" } ], "history": null, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-devstral-small-2.json", "title": "Agents-A1-4B vs 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"https://www.anchorterminal.com/compare/kronos-vs-qwen3-vl-8b-instruct.json", "title": "Kronos vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-8b-instruct.json", "title": "Laguna M.1 vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-vl-8b-instruct.json", "title": "Laguna S 2.1 vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-8b-instruct.json", "title": "Laguna XS 2.1 vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-vl-8b-instruct.json", "title": "LBS-27B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-8b-instruct.json", "title": "LFM2.5-2.6B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-vl-8b-instruct.json", "title": "LFM3-1.7B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-vl-8b-instruct.json", "title": "LFM3-Audio-2B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-vl-8b-instruct.json", "title": "LFM3-Omni-2B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-8b-instruct.json", "title": "LFM3-VL-2B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-vl-8b-instruct.json", "title": "Ling-3.0-tiny vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-vl-8b-instruct.json", "title": "Ling-3.1-flash-1019 vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-8b-instruct.json", "title": "LongCat-2.5 vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwen3-vl-8b-instruct.json", "title": "MiMo-V2.6 Distill Qwen 9B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-vl-8b-instruct.json", "title": "MiMo-V2.6-Flash-RL vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-8b-instruct.json", "title": "MiniCPM-o 4.5 vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-vl-8b-instruct.json", "title": "MiniCPM-V 4.7 1B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-vl-8b-instruct.json", "title": "MiniCPM-V 4.7 35B-A3B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-vl-8b-instruct.json", "title": "MiniCPM5-2B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-vl-8b-instruct.json", "title": "MiniMax-M2.5 vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-8b-instruct.json", "title": "MiniMax-M2.7 vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-vl-8b-instruct.json", "title": "MiniMax-M3.1-Flash vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-vl-8b-instruct.json", "title": "Mistral Large 4 vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-8b-instruct.json", "title": "Muse Glimmer 30B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-vl-8b-instruct.json", "title": "Nanbeige4.2-3B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-vl-8b-instruct.json", "title": "Nanbeige4.3-33B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-vl-8b-instruct.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-vl-8b-instruct.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-vl-8b-instruct.json", "title": "Nemotron 3 Nano 30B A3B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-8b-instruct.json", "title": "Nemotron 3 Nano 4B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-vl-8b-instruct.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-vl-8b-instruct.json", "title": "Nemotron 3 Super 120B A12B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-vl-8b-instruct.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-vl-8b-instruct.json", "title": "North Mini Code 1.0 vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-8b-instruct.json", "title": "occamy-1.0 vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-8b-instruct.json", "title": "Olmo 3 7B Think vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-8b-instruct" }, { "json": 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}, { "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-vl-8b-instruct.json", "title": "Qwen3.8-27B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-vl-8b-instruct.json", "title": "Qwen3.8-Flash-Next vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwen3-vl-8b-instruct.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-qwen3-vl-8b-instruct.json", "title": "Qwen3-Coder-Next vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-qwen3-vl-8b-instruct.json", "title": "Qwen3-Omni-30B-A3B-Instruct vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-qwen3-vl-8b-instruct.json", "title": "Qwen3-VL-2B-Instruct vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-qwen3-vl-8b-instruct.json", "title": "Qwen3-VL-30B-A3B-Instruct vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-qwen3-vl-8b-instruct.json", "title": "Qwen3-VL-32B-Instruct vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-qwen3-vl-8b-instruct.json", "title": "Qwen3-VL-4B-Instruct vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3-VL-8B-Instruct vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-recursive-flint.json", "title": "Qwen3-VL-8B-Instruct vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-ripple-65b-preview.json", "title": "Qwen3-VL-8B-Instruct vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-sensenova-loop-9b-preview.json", "title": "Qwen3-VL-8B-Instruct vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-smollm3-3b.json", "title": "Qwen3-VL-8B-Instruct vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-spark-x2-6-4b.json", "title": "Qwen3-VL-8B-Instruct vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-step-3-7-flash.json", "title": "Qwen3-VL-8B-Instruct vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwen3-VL-8B-Instruct vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3-VL-8B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3-VL-8B-Instruct vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-unlimited-ocr-2.json", "title": "Qwen3-VL-8B-Instruct vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-wevisone-vl-2b.json", "title": "Qwen3-VL-8B-Instruct vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-wevisone-vl-4b.json", "title": "Qwen3-VL-8B-Instruct vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-zaya1-8b.json", "title": "Qwen3-VL-8B-Instruct vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 10, "devstral-small-2": 90, "edge": "qwen3-vl-8b-instruct", "key": "reach", "name": "Reach", "qwen3-vl-8b-instruct": 100, "weight": 25 }, { "by": 5, "devstral-small-2": 60, "edge": "devstral-small-2", "key": "risk", "name": "Risk", "qwen3-vl-8b-instruct": 55, "weight": 20 }, { "by": 5, "devstral-small-2": 65, "edge": "devstral-small-2", "key": "interface", "name": "Agent interface", "qwen3-vl-8b-instruct": 60, "weight": 10 }, { "by": 15, "devstral-small-2": 46, "edge": "devstral-small-2", "key": "upkeep", "name": "Documentation and upkeep", "qwen3-vl-8b-instruct": 31, "weight": 10 } ], "summary": "Devstral Small 2 scores 61 (C) on agent readiness against Qwen3-VL-8B-Instruct's 54.9 (C), and leads in 3 of 4 scored categories. Qwen3-VL-8B-Instruct leads on reach. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "devstral-small-2": "The easy one to run: a 24B coding agent under Apache 2.0 that five runtimes carry, 15 GB at 4 bits in Ollama, with a 256K context, image input and well-documented tool calling. It's eleven months old, calls tools one at a time, has no reasoning mode, and its card says nothing on limits, safety or data.", "qwen3-vl-8b-instruct": "Qwen3-VL-8B-Instruct reads screenshots, documents, charts and video, and Alibaba's Q4_K_M GGUF needs about 6 GB. Transformers, vLLM, SGLang, llama.cpp and Ollama run it under plain Apache 2.0. It has no thinking mode, documents tool calls only in the template, and has no published safety work, so add guardrails before it drives a GUI." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-vl-8b-instruct", "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-vl-8b-instruct.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-vl-8b-instruct.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-vl-8b-instruct.min.md" }, "markdown": "Devstral Small 2 scores 61 (C) on agent readiness against Qwen3-VL-8B-Instruct's 54.9 (C), and leads in 3 of 4 scored categories. Qwen3-VL-8B-Instruct leads on reach. Both do open-weight models you run yourself.\n\n- Devstral Small 2: grade C, 61/100, rank #60 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/devstral-small-2.md · JSON https://www.anchorterminal.com/api/v1/tools/devstral-small-2.json\n- Qwen3-VL-8B-Instruct: grade C, 54.9/100, rank #124 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-vl-8b-instruct.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-vl-8b-instruct.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### Devstral Small 2 (C)\n\nGood for: Developers who want a dependable local coding agent on one consumer GPU or a 32 GB Mac\n\nAhead on:\n- Risk, 60 against 55\n- Agent interface, 65 against 60\n- Documentation and upkeep, 46 against 31\n\nWatch for: No limitations, safety results, languages or data on the card\n\n### Qwen3-VL-8B-Instruct (C)\n\nGood for: An agent with 8 GB or more to spare that needs a local model to read images, documents and video\n\nAhead on:\n- Reach, 100 against 90\n\nWatch for: No safety evaluation, agent-use guidance or security contact\n\n\n## Why the edge\n\n### Documentation and upkeep: Devstral Small 2 +15\n\n- Devstral Small 2 (46): Model card 11, Intended use, the architecture, the parameters and the context are on the card. Training data 0, Nothing on the card about the training data. Last change 10, the last commit, on 15 July 2026, updated the files to match mistral-common, 118 days before this check and within 180. Responsiveness 10, Of the 23 most recent discussions that aren't pull requests (9 December 2025 to 6 November 2026), 11 have a reply from a mistralai member, which is some. Version policy 5, Devstral Small 1.1 stays in its own repository, which this card links as the predecessor, with no dated note on the older card that we read. File health 10, Config, template, tokenizer and card agree, and the June and July commits fixed stray tokens and brought the files in line with mistral-common. 11 + 0 + 10 + 10 + 5 + 10 = 46.\n- Qwen3-VL-8B-Instruct (31): The card misses limitations, languages and a cut-off (11). Training data is described only in the technical report, with no cut-off (5). The last change to code that runs it was the qwen-vl-utils fix merged on 4 January 2026, and the weights haven't changed since November 2025; the Hub commits for all three repositories and the QwenLM/Qwen3-VL head (30 January 2026) were the same on 4 November (5). Of 34 Hugging Face discussions on 4 November, the 19 that aren't pull requests (newest 23 August 2026) have no reply from Qwen, and GitHub issues weren't counted (0). Earlier versions kept in their own repositories without notes (5). Two reports wait without a reply (5). 11+5+5+0+5+5 = 31.\n\n### Reach: Qwen3-VL-8B-Instruct +10\n\n- Devstral Small 2 (90): Memory 25, the smallest build at 4 bits from a runtime's own library is Ollama's devstral-small-2 at Q4_K_M, 15 GB, and 15 GB × 1.2 = 18 GB, within 24 GB. Mistral's own FP8 files are 25.79 GB. Runtimes 25, Released vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 have the mistral3 model, llama.cpp b12242 has the architecture with a GGUF from its own organisation, and Ollama's library has devstral-small-2. Builds 10, one quantised precision from Mistral, the FP8 weights it releases. Terms 15, Apache 2.0 in the card's metadata and text, an OSI licence with nothing added. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, Mistral's own API serves Devstral Small 2: the card says the model is available through its API and points there when a local runtime underperforms. 25 + 25 + 10 + 15 + 10 + 5 = 90.\n- Qwen3-VL-8B-Instruct (100): Alibaba's own Q4_K_M GGUF is 5.03 GB, so with a fifth added it needs about 6 GB and fits in 16 GB (30). Transformers 4.57.0, vLLM, SGLang and llama.cpp run it in released versions, and Ollama v0.40.14 pins llama.cpp b11963, which has the qwen3vl architecture, so Ollama is credited through its pin; five runtimes reach the 25 cap. Alibaba publishes FP8 and GGUF builds at F16, Q8_0 and Q4_K_M (15). Apache 2.0 with nothing added (15). No gate and no account (10). Featherless is live in the Hub's provider mapping on 4 November, and Parasail served it per the 21 October check, not re-read, so it's hosted too (5). Novita's mapping still shows an error and isn't counted. 30+25+15+15+10+5 = 100.\n\n### Risk: Devstral Small 2 +5\n\n- Devstral Small 2 (60): File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, files in Mistral's verified mistralai organisation, which Mistral's site links to per our Mistral Large 4 listing (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on prompt injection, instruction priority or what the model refuses. Licence risk 15, Apache 2.0 has no use policy, threshold or change clause. Security programme 5, mistral.ai's security.txt gives a security contact and a submission policy, as read for the Mistral Large 4 listing (5). 25 + 15 + 0 + 0 + 15 + 5 = 60.\n- Qwen3-VL-8B-Instruct (55): Safetensors and GGUF only and no code in the repositories, so the FP8 card's trust_remote_code flag runs nothing (25). Files in the Qwen organisation with a checksum on every file (15). No safety evaluation at all (0). No guidance for agent use, which matters for a model sold as a GUI agent (0). Apache 2.0 carries none of the licence risks (15). No security contact, disclosure policy or public advisories found for Qwen (0), and Alibaba's group-wide security centre wasn't checked. 25+15+0+0+15+0 = 55.\n\n### Agent interface: Devstral Small 2 +5\n\n- Devstral Small 2 (65): Chat template 20, chat_template.jinja in the repository (11,362 characters), with the tokenizer's tekken file for mistral-common. Tool-calling format 25, the card documents tools in the OpenAI function format with an example, names the mistral tool-call parser with auto tool choice for vLLM, shows the same for SGLang, and ships test prompts with the expected tool-call output. Trained tool calling 10, the card says the model excels at using tools, and its tests cover one tool call and calls made one at a time in sequence. Structured output 0, Nothing on the card about JSON or schema-constrained answers for this model. Reasoning control 0, no reasoning mode is documented, and a June 2026 commit removed stray thinking tokens from the files. Context 10, 256K tokens per the card, served at 262,144 in its vLLM command. 20 + 25 + 10 + 0 + 0 + 10 = 65.\n- Qwen3-VL-8B-Instruct (60): A Jinja chat template in the repository (20). The tool-call format lives only in the template, Hermes-style JSON in tool_call tags, and Alibaba names no parser (10). Trained for function calling with several calls allowed in a turn (20). No documented structured output (0). No thinking mode in this edition, so reasoning control scores 0 by the rule. 262,144 tokens of native context (10). 20+10+20+0+0+10 = 60.\n\n## Score by category\n\n| Category | Weight | Devstral Small 2 | Qwen3-VL-8B-Instruct | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 90 | 100 | Qwen3-VL-8B-Instruct +10 |\n| Risk | 20% (20 this run) | 60 | 55 | Devstral Small 2 +5 |\n| Agent interface | 10% (10 this run) | 65 | 60 | Devstral Small 2 +5 |\n| Documentation and upkeep | 10% (10 this run) | 46 | 31 | Devstral Small 2 +15 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **54.9 · C** | |\n\n## Facts side by side\n\n| Fact | Devstral Small 2 | Qwen3-VL-8B-Instruct |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Mistral AI | 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 | Apache-2.0 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-07-15 | 2025-11-01 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | none | 2.8/5 (4) |\n\n## Verdicts\n\n**Devstral Small 2.** The easy one to run: a 24B coding agent under Apache 2.0 that five runtimes carry, 15 GB at 4 bits in Ollama, with a 256K context, image input and well-documented tool calling. It's eleven months old, calls tools one at a time, has no reasoning mode, and its card says nothing on limits, safety or data.\n\n**Qwen3-VL-8B-Instruct.** Qwen3-VL-8B-Instruct reads screenshots, documents, charts and video, and Alibaba's Q4_K_M GGUF needs about 6 GB. Transformers, vLLM, SGLang, llama.cpp and Ollama run it under plain Apache 2.0. It has no thinking mode, documents tool calls only in the template, and has no published safety work, so add guardrails before it drives a GUI.\n\n## Before you call either\n\n### Devstral Small 2\n\n1. `ollama run devstral-small-2` is the quickest start, at 15 GB\n2. On vLLM pass `--tool-call-parser mistral --enable-auto-tool-choice`\n3. Send the system prompt from CHAT_SYSTEM_PROMPT.txt in the repository. The card says results depend on it\n4. Use a low temperature. The repository's default is 0.15\n5. Plan for one tool call per turn\n6. The same model is on our Mistral API listing if you'd rather not host it\n\n### Qwen3-VL-8B-Instruct\n\n1. Start it with `llama-server -hf Qwen/Qwen3-VL-8B-Instruct-GGUF:Q4_K_M --jinja -c 32768`, or `ollama run qwen3-vl:8b-instruct`\n2. In Ollama the plain `qwen3-vl:8b` tag is the Thinking edition, so name `8b-instruct`\n3. With the GGUF, load the mmproj vision file too, or images won't be read\n4. Parse tool calls as JSON inside `\u003ctool_call\u003e` tags, or use SGLang's `--tool-call-parser qwen`\n5. Use the card's sampling, `temperature=0.7, top_p=0.8, top_k=20, presence_penalty=1.5` for vision and `temperature=1.0, top_p=1.0, top_k=40, presence_penalty=2.0` for text\n6. Validate JSON output yourself, since structured output isn't documented\n7. vLLM needs 0.11.0 or later and Transformers 4.57.0 or later\n\n## Questions\n\n### Which is better for AI agents, Devstral Small 2 or Qwen3-VL-8B-Instruct?\n\nDevstral Small 2 scores 61 (C) on agent readiness against Qwen3-VL-8B-Instruct's 54.9 (C), and leads in 3 of 4 scored categories. Qwen3-VL-8B-Instruct leads on reach.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-vl-8b-instruct.json, and with the fewest tokens: https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-vl-8b-instruct.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"devstral-small-2\", \"b\": \"qwen3-vl-8b-instruct\"}`. From a terminal: `anchor compare devstral-small-2 qwen3-vl-8b-instruct`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/devstral-small-2.json and https://www.anchorterminal.com/api/v1/tools/qwen3-vl-8b-instruct.json\n\n## Other comparisons with Devstral Small 2 or Qwen3-VL-8B-Instruct\n\n- [Agents-A1-4B vs Devstral Small 2](https://www.anchorterminal.com/compare/agents-a1-4b-vs-devstral-small-2.md)\n- [Agents-A1-4B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-vl-8b-instruct.md)\n- [Apertus v1.5 8B vs Devstral Small 2](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-devstral-small-2.md)\n- [Apertus v1.5 8B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-vl-8b-instruct.md)\n- [Apodex-1.1-mini vs Devstral Small 2](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-devstral-small-2.md)\n- [Apodex-1.1-mini vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-vl-8b-instruct.md)\n- [Arete-27B vs Devstral Small 2](https://www.anchorterminal.com/compare/arete-27b-vs-devstral-small-2.md)\n- [Arete-27B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-vl-8b-instruct.md)\n- [BigBang-v1 vs Devstral Small 2](https://www.anchorterminal.com/compare/bigbang-v1-vs-devstral-small-2.md)\n- [BigBang-v1 vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-vl-8b-instruct.md)\n- [Carbon-X vs Devstral Small 2](https://www.anchorterminal.com/compare/carbon-x-vs-devstral-small-2.md)\n- [Carbon-X vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-vl-8b-instruct.md)\n- [Cola2 vs Devstral Small 2](https://www.anchorterminal.com/compare/cola2-vs-devstral-small-2.md)\n- [Cola2 vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/cola2-vs-qwen3-vl-8b-instruct.md)\n- 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