{ "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 InternVL3.5-GPT-OSS-20B-A4B-Preview's 53.2 (D), and leads in every scored category.", "b": { "slug": "internvl3-5-gpt-oss-20b-a4b-preview", "name": "InternVL3.5-GPT-OSS-20B-A4B-Preview", "vendor": "OpenGVLab (Shanghai AI Laboratory)", "vendorUrl": "https://github.com/OpenGVLab", "kind": "weights", "category": "open-weights", "summary": "InternVL3.5-GPT-OSS-20B-A4B-Preview is OpenGVLab's open-weight vision-language preview that joins OpenAI's gpt-oss-20b mixture-of-experts model with the InternViT-300M vision encoder, 21.2B parameters with about 4B active, taking images and video and answering in text, under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/internvl3-5-gpt-oss-20b-a4b-preview", "markdownUrl": "https://www.anchorterminal.com/tools/internvl3-5-gpt-oss-20b-a4b-preview.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/internvl3-5-gpt-oss-20b-a4b-preview.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/internvl3-5-gpt-oss-20b-a4b-preview.json", "dns": "internvl3-5-gpt-oss-20b-a4b-preview.grade.anchorterminal.com", "repo": "https://huggingface.co/OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF", "license": "Apache-2.0; a merge of openai/gpt-oss-20b (Apache 2.0 with OpenAI's usage policy) and OpenGVLab/InternViT-300M-448px-V2_5 (MIT)", "transports": [], "packages": [ { "registry": "huggingface", "name": "OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF" }, { "registry": "huggingface", "name": "OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither the Transformers-format repository nor the GitHub-format one is gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0, with gpt-oss-20b's Apache 2.0 and usage policy and InternViT's MIT licence carried with it. Running it costs the hardware, 42.5 GB of BF16 files in either format, since the gpt-oss experts were expanded from MXFP4 to BF16 (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/OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "vision", "video", "reasoning", "merge", "preview", "apache-2.0", "safetensors", "self-hosted" ], "lastRelease": "2025-09-08", "graded": true, "anchor": { "graded": true, "score": 53.2, "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": 101, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 45, "reach": 65, "risk": 55, "upkeep": 28 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-04" }, "negative": 0, "verdict": "An image and video reader on gpt-oss-20b with an optional thinking mode, a preview untouched for over a year. It needs about 51 GB in BF16, since OpenGVLab publishes no quantised build, and runs in Transformers and vLLM. The main risk is a chat template in the Transformers-format repository that doesn't match its tokenizer, with no safety results.", "bestFor": "Agents with 64 GB to spare that want an Apache-licensed image and video reader with optional thinking and can live with a stale preview.", "strengths": [ "Plain Apache 2.0 over Apache and MIT bases, with no gate", "Safetensors only and no repository code in the Transformers-format files", "Loads in released Transformers and vLLM, and the GitHub-format twin in SGLang, llama.cpp and Ollama through its llama.cpp pin", "Thinking turned on per request with a documented system prompt", "About 4B active parameters per token on a 21.2B mixture of experts" ], "weaknesses": [ "No quantised build from OpenGVLab, so about 51 GB at BF16", "Chat template uses markers its tokenizer lacks and differs from the twin's harmony template", "No change to the files since September 2025, and it's still a preview", "No safety results, agent guidance or security contact", "No documented tool calling or structured output, and trained at 32K tokens" ], "agentNotes": [ "Serve with `vllm serve OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF --max-model-len 32768`, which is our command; the card gives none for vLLM", "Check the rendered prompt before relying on it, since the shipped template's \u003c|im_start|\u003e markers aren't tokens in the gpt-oss tokenizer; the GitHub-format repository's harmony template is the alternative", "Use Transformers 4.55.0 or later, as the card requires for this size", "For thinking, send the card's thinking system prompt and parse the reasoning out of the \u003cthink\u003e tags, with temperature 0.6 and sampling on as the card advises", "Keep inputs within 32K tokens, the length it was trained at, whatever the config says" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 1.8, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "D", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 53.2 } ], "editorialScores": { "interface": 45, "reach": 65, "risk": 55, "upkeep": 28 }, "provenanceScore": 61, "checked": { "categories": { "interface": "2026-11-04", "reach": "2026-11-04", "risk": "2026-11-04", "upkeep": "2026-11-04" }, "last": "2026-11-04", "lastMoved": "2026-11-04", "oldest": "2026-11-04", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "vllm serve OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF --max-model-len 32768", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF\", \"messages\": [{\"role\": \"user\", \"content\": [{\"type\": \"image_url\", \"image_url\": {\"url\": \"https://example.com/chart.png\"}}, {\"type\": \"text\", \"text\": \"Describe this chart.\"}]}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/internvl3-5-gpt-oss-20b-a4b-preview" }, "weights": { "trainedBy": "OpenGVLab (Shanghai AI Laboratory)", "builtOn": "openai/gpt-oss-20b and OpenGVLab/InternViT-300M-448px-V2_5 (declared as a merge)", "params": "21.2B", "active": "4B", "context": 32768, "goodFor": "Vision-language preview for images, documents, charts and video with text out and an optional thinking mode, on the gpt-oss-20b mixture-of-experts backbone.", "runtimes": [ "Transformers", "vLLM", "SGLang", "llama.cpp", "Ollama" ], "builds": [ { "name": "InternVL3_5-GPT-OSS-20B-A4B-Preview-HF", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF", "who": "publisher", "precision": "bf16", "sizeGB": 42.5, "memoryGB": 64, "format": "safetensors", "runsOn": [ "Transformers", "vLLM" ], "note": "Transformers' native InternVL format, no repository code. The shipped chat template uses markers that aren't in the tokenizer" }, { "name": "InternVL3_5-GPT-OSS-20B-A4B-Preview", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview", "who": "publisher", "precision": "bf16", "sizeGB": 42.5, "memoryGB": 64, "format": "safetensors", "runsOn": [ "SGLang", "llama.cpp (after conversion)", "Ollama (through its llama.cpp pin, after conversion)", "Transformers (remote code)" ], "note": "The same weights in OpenGVLab's GitHub format (InternVLChatModel) with modeling code for trust_remote_code and gpt-oss's harmony chat template" } ], "run": "vllm serve OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF --max-model-len 32768", "runBuild": "InternVL3_5-GPT-OSS-20B-A4B-Preview-HF", "checked": "2026-11-04", "minMemoryGB": 64 }, "family": { "lab": "OpenGVLab (Shanghai AI Laboratory)", "family": "InternVL3 (3.0, 3.5)", "url": "https://www.anchorterminal.com/open-weights/#opengvlab", "others": [ { "name": "InternVL3_5-8B-Flash", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-8B-Flash", "params": "8B (Hub safetensors count 8.81B)" }, { "name": "InternVL3_5-4B-Flash", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-4B-Flash", "params": "4B (Hub safetensors count 4.91B)" }, { "name": "InternVL3_5-38B-Flash", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-38B-Flash", "params": "38B (Hub safetensors count 39.56B)" }, { "name": "InternVL3_5-30B-A3B-Flash", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-30B-A3B-Flash", "params": "30B total, 3B active (MoE) (Hub safetensors count 30.99B)" }, { "name": "InternVL3_5-2B-Flash", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-2B-Flash", "params": "2B (Hub safetensors count 2.49B)" }, { "name": "InternVL3_5-241B-A28B-Flash", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-241B-A28B-Flash", "params": "241B total, 28B active (MoE) (Hub safetensors count 241.71B)" }, { "name": "InternVL3_5-1B-Flash", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-1B-Flash", "params": "1B (Hub safetensors count 1.15B)" }, { "name": "InternVL3_5-14B-Flash", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-14B-Flash", "params": "14B (Hub safetensors count 15.50B)" }, { "name": "InternVL3_5-8B-Pretrained", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-8B-Pretrained", "params": "8B (Hub safetensors count 8.53B)" }, { "name": "InternVL3_5-8B-MPO", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-8B-MPO", "params": "8B (Hub safetensors count 8.53B)" }, { "name": "InternVL3_5-8B-Instruct", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-8B-Instruct", "params": "8B (Hub safetensors count 8.53B)" }, { "name": "InternVL3_5-8B", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-8B", "params": "8B (Hub safetensors count 8.53B)" }, { "name": "InternVL3_5-4B-Pretrained", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-4B-Pretrained", "params": "4B (Hub safetensors count 4.73B)" }, { "name": "InternVL3_5-4B-MPO", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-4B-MPO", "params": "4B (Hub safetensors count 4.73B)" }, { "name": "InternVL3_5-4B-Instruct", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-4B-Instruct", "params": "4B (Hub safetensors count 4.73B)" }, { "name": "InternVL3_5-4B", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-4B", "params": "4B (Hub safetensors count 4.73B)" }, { "name": "InternVL3_5-38B-Pretrained", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-38B-Pretrained", "params": "38B (Hub safetensors count 38.39B)" }, { "name": "InternVL3_5-38B-MPO", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-38B-MPO", "params": "38B (Hub safetensors count 38.39B)" }, { "name": "InternVL3_5-38B-Instruct", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-38B-Instruct", "params": "38B (Hub safetensors count 38.39B)" }, { "name": "InternVL3_5-38B", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-38B", "params": "38B (Hub safetensors count 38.39B)" }, { "name": "InternVL3_5-30B-A3B-Pretrained", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-30B-A3B-Pretrained", "params": "30B total, 3B active (MoE) (Hub safetensors count 30.85B)" }, { "name": "InternVL3_5-30B-A3B-MPO", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-30B-A3B-MPO", "params": "30B total, 3B active (MoE) (Hub safetensors count 30.85B)" }, { "name": "InternVL3_5-30B-A3B-Instruct", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-30B-A3B-Instruct", "params": "30B total, 3B active (MoE) (Hub safetensors count 30.85B)" }, { "name": "InternVL3_5-30B-A3B", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-30B-A3B", "params": "30B total, 3B active (MoE) (Hub safetensors count 30.85B)" }, { "name": "InternVL3_5-2B-Pretrained", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-2B-Pretrained", "params": "2B (Hub safetensors count 2.35B)" }, { "name": "InternVL3_5-2B-MPO", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-2B-MPO", "params": "2B (Hub safetensors count 2.35B)" }, { "name": "InternVL3_5-2B-Instruct", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-2B-Instruct", "params": "2B (Hub safetensors count 2.35B)" }, { "name": "InternVL3_5-2B", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-2B", "params": "2B (Hub safetensors count 2.35B)" }, { "name": "InternVL3_5-241B-A28B-Pretrained", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-241B-A28B-Pretrained", "params": "241B total, 28B active (MoE) (Hub safetensors count 240.70B)" }, { "name": "InternVL3_5-241B-A28B-MPO", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-241B-A28B-MPO", "params": "241B total, 28B active (MoE) (Hub safetensors count 240.70B)" }, { "name": "InternVL3_5-241B-A28B-Instruct", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-241B-A28B-Instruct", "params": "241B total, 28B active (MoE) (Hub safetensors count 240.70B)" }, { "name": "InternVL3_5-241B-A28B", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-241B-A28B", "params": "241B total, 28B active (MoE) (Hub safetensors count 240.70B)" }, { "name": "InternVL3_5-1B-Pretrained", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-1B-Pretrained", "params": "1B (Hub safetensors count 1.06B)" }, { "name": "InternVL3_5-1B-MPO", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-1B-MPO", "params": "1B (Hub safetensors count 1.06B)" }, { "name": "InternVL3_5-1B-Instruct", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-1B-Instruct", "params": "1B (Hub safetensors count 1.06B)" }, { "name": "InternVL3_5-1B", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-1B", "params": "1B (Hub safetensors count 1.06B)" }, { "name": "InternVL3_5-14B-Pretrained", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-14B-Pretrained", "params": "14B (Hub safetensors count 15.12B)" }, { "name": "InternVL3_5-14B-MPO", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-14B-MPO", "params": "14B (Hub safetensors count 15.12B)" }, { "name": "InternVL3_5-14B-Instruct", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-14B-Instruct", "params": "14B (Hub safetensors count 15.12B)" }, { "name": "InternVL3_5-14B", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-14B", "params": "14B (Hub safetensors count 15.12B)" }, { "name": "InternVL3-9B-Pretrained", "url": "https://huggingface.co/OpenGVLab/InternVL3-9B-Pretrained", "params": "9B (Hub safetensors count 9.14B)" }, { "name": "InternVL3-8B-Pretrained", "url": "https://huggingface.co/OpenGVLab/InternVL3-8B-Pretrained", "params": "8B (Hub safetensors count 7.94B)" }, { "name": "InternVL3-78B-Pretrained", "url": "https://huggingface.co/OpenGVLab/InternVL3-78B-Pretrained", "params": "78B (Hub safetensors count 78.41B)" }, { "name": "InternVL3-38B-Pretrained", "url": "https://huggingface.co/OpenGVLab/InternVL3-38B-Pretrained", "params": "38B (Hub safetensors count 38.39B)" }, { "name": "InternVL3-2B-Pretrained", "url": "https://huggingface.co/OpenGVLab/InternVL3-2B-Pretrained", "params": "2B (Hub safetensors count 2.09B)" }, { "name": "InternVL3-1B-Pretrained", "url": "https://huggingface.co/OpenGVLab/InternVL3-1B-Pretrained", "params": "1B (Hub safetensors count 938M)" }, { "name": "InternVL3-14B-Pretrained", "url": "https://huggingface.co/OpenGVLab/InternVL3-14B-Pretrained", "params": "14B (Hub safetensors count 15.12B)" }, { "name": "InternVL3-9B-Instruct", "url": "https://huggingface.co/OpenGVLab/InternVL3-9B-Instruct", "params": "9B (Hub safetensors count 9.14B)" }, { "name": "InternVL3-8B-Instruct", "url": "https://huggingface.co/OpenGVLab/InternVL3-8B-Instruct", "params": "8B (Hub safetensors count 7.94B)" }, { "name": "InternVL3-78B-Instruct", "url": "https://huggingface.co/OpenGVLab/InternVL3-78B-Instruct", "params": "78B (Hub safetensors count 78.41B)" }, { "name": "InternVL3-38B-Instruct", "url": "https://huggingface.co/OpenGVLab/InternVL3-38B-Instruct", "params": "38B (Hub safetensors count 38.39B)" }, { "name": "InternVL3-2B-Instruct", "url": "https://huggingface.co/OpenGVLab/InternVL3-2B-Instruct", "params": "2B (Hub safetensors count 2.09B)" }, { "name": "InternVL3-1B-Instruct", "url": "https://huggingface.co/OpenGVLab/InternVL3-1B-Instruct", "params": "1B (Hub safetensors count 938M)" }, { "name": "InternVL3-14B-Instruct", "url": "https://huggingface.co/OpenGVLab/InternVL3-14B-Instruct", "params": "14B (Hub safetensors count 15.12B)" }, { "name": "InternVL3-9B", "url": "https://huggingface.co/OpenGVLab/InternVL3-9B", "params": "9B (Hub safetensors count 9.14B)" }, { "name": "InternVL3-8B", "url": "https://huggingface.co/OpenGVLab/InternVL3-8B", "params": "8B (Hub safetensors count 7.94B)" }, { "name": "InternVL3-78B", "url": "https://huggingface.co/OpenGVLab/InternVL3-78B", "params": "78B (Hub safetensors count 78.41B)" }, { "name": "InternVL3-38B", "url": "https://huggingface.co/OpenGVLab/InternVL3-38B", "params": "38B (Hub safetensors count 38.39B)" }, { "name": "InternVL3-2B", "url": "https://huggingface.co/OpenGVLab/InternVL3-2B", "params": "2B (Hub safetensors count 2.09B)" }, { "name": "InternVL3-1B", "url": "https://huggingface.co/OpenGVLab/InternVL3-1B", "params": "1B (Hub safetensors count 938M)" }, { "name": "InternVL3-14B", "url": "https://huggingface.co/OpenGVLab/InternVL3-14B", "params": "14B (Hub safetensors count 15.12B)" } ], "olderCount": 41 }, "area": "models", "provenance": { "legalEntity": "Shanghai AI Laboratory (OpenGVLab), not named in the repository", "domain": "github.com/OpenGVLab", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF/commits/main", "securityTxt": "none", "checked": "2026-11-04", "notes": [ "OpenGVLab's GitHub organisation profile links its Hugging Face organisation. The repositories carry no LICENSE file or copyright line, and shlab.org.cn's registration date wasn't checked.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "The changelog is the commit history, five commits from 29 August to 8 September 2025, all titled Upload folder using huggingface_hub, which don't say what changed. Unchanged on 4 November 2026.", "www.shlab.org.cn and internvl.github.io answered 404 for security.txt on 21 October 2026, and OpenGVLab/InternVL has no security policy." ], "score": 61 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/internvl3-5-gpt-oss-20b-a4b-preview.json", "live": { "slug": "internvl3-5-gpt-oss-20b-a4b-preview", "domain": { "domain": "github.com/opengvlab", "checkedAt": "2026-11-08T15:08:02.394346699Z" }, "pages": [ { "url": "https://huggingface.co/OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-11-10T16:22:18.761399377Z", "changedAt": "2026-11-10T16:22:18.761399377Z", "fingerprint": "e63419d9bf08" } ], "updatedAt": "2026-11-10T16:22:18.761399377Z" } }, "edges": [ { "by": 25, "category": "reach", "leads": "devstral-small-2", "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": "Memory 15 of 30, the only builds are BF16 at 42.5 GB by the repository's safetensors index, or 51 GB with a fifth added, which fits 64 GB. Runtimes 25, Transformers and vLLM load this repository, and SGLang, llama.cpp and Ollama (through llama.cpp b11963, which Ollama v0.40.14 pins) run the same weights in their GitHub format. Builds 0, OpenGVLab publishes no quantised build. Terms 15, Apache 2.0 with nothing added by the bases. Download 10, not gated. Hosted 0, no provider serves it." }, "scores": { "a": 90, "b": 65 } }, { "by": 20, "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": "Chat template 20, a Jinja file in the repository, though it doesn't match the tokenizer. Tool-calling format 10 of 25, only the twin's inherited harmony template mentions tools. Trained tool calling 0, no evidence it was trained to call tools. Structured output 0. Reasoning control 10, a system prompt turns thinking on and the card says the reasoning comes back in think tags. Context 5, trained at 32K tokens." }, "scores": { "a": 65, "b": 45 } }, { "by": 18, "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": "Model card 8 of 20, the family card misses limitations, context, languages and a cut-off. Training data 5, described without a cut-off. Last change 0, the last commit to either repository is 8 September 2025 and to OpenGVLab's reference code 22 September 2025, over a year before 4 November 2026. Responsiveness 10, OpenGVLab answered 3 of 8 questions across the two repositories, none since September 2025. Version policy 5, a first release. File health 0, the template uses markers the tokenizer doesn't have and contradicts the twin's template, and the card names the wrong language model in its licence line." }, "scores": { "a": 46, "b": 28 } }, { "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": "File safety 25, safetensors only and no code in the repository. Provenance 15, the files are in the OpenGVLab organisation its GitHub links, with a checksum on every file. Safety evaluation 0 and agent guidance 0, nothing published. Licence risk 15, Apache 2.0 over Apache and MIT bases, with gpt-oss's usage policy asking only for lawful use. Security programme 0, no policy, security.txt or advisories." }, "scores": { "a": 60, "b": 55 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Mistral AI", "b": "OpenGVLab (Shanghai AI Laboratory)", "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; a merge of openai/gpt-oss-20b (Apache 2.0 with OpenAI's usage policy) and OpenGVLab/InternViT-300M-448px-V2_5 (MIT)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-07-15", "b": "2025-09-08", "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": "1.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Devstral Small 2 scores 61 (C) on agent readiness against InternVL3.5-GPT-OSS-20B-A4B-Preview's 53.2 (D), and leads in every scored category.", "question": "Which is better for AI agents, Devstral Small 2 or InternVL3.5-GPT-OSS-20B-A4B-Preview?" } ], "goodFor": [ { "aheadOn": [ "Reach, 90 against 65", "Risk, 60 against 55", "Agent interface, 65 against 45", "Documentation and upkeep, 46 against 28" ], "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": null, "also": null, "goodFor": "Agents with 64 GB to spare that want an Apache-licensed image and video reader with optional thinking and can live with a stale preview.", "slug": "internvl3-5-gpt-oss-20b-a4b-preview", "watchFor": "No quantised build from OpenGVLab, so about 51 GB at BF16" } ], "history": { "devstral-small-2": null, "internvl3-5-gpt-oss-20b-a4b-preview": [ { "causes": [ "correction" ], "date": "2026-11-04", "delta": 2, "from": 51.2, "fromGrade": "D", "to": 53.2, "toGrade": "D" } ] }, "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 Devstral Small 2", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-devstral-small-2" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-internvl3-5-gpt-oss-20b-a4b-preview.json", "title": "Agents-A1-4B vs InternVL3.5-GPT-OSS-20B-A4B-Preview", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-internvl3-5-gpt-oss-20b-a4b-preview" }, { "json": 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"title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-smollm3-3b.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-spark-x2-6-4b.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-step-3-7-flash.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-swift-1-5-qwen3-8-27b.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-swift-1-5-qwen3-8-flash-next.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-thinkingcap-qwen3-8-27b.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-unlimited-ocr-2.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-wevisone-vl-2b.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-wevisone-vl-4b.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-zaya1-8b.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 25, "devstral-small-2": 90, "edge": "devstral-small-2", "internvl3-5-gpt-oss-20b-a4b-preview": 65, "key": "reach", "name": "Reach", "weight": 25 }, { "by": 5, "devstral-small-2": 60, "edge": "devstral-small-2", "internvl3-5-gpt-oss-20b-a4b-preview": 55, "key": "risk", "name": "Risk", "weight": 20 }, { "by": 20, "devstral-small-2": 65, "edge": "devstral-small-2", "internvl3-5-gpt-oss-20b-a4b-preview": 45, "key": "interface", "name": "Agent interface", "weight": 10 }, { "by": 18, "devstral-small-2": 46, "edge": "devstral-small-2", "internvl3-5-gpt-oss-20b-a4b-preview": 28, "key": "upkeep", "name": "Documentation and upkeep", "weight": 10 } ], "summary": "Devstral Small 2 scores 61 (C) on agent readiness against InternVL3.5-GPT-OSS-20B-A4B-Preview's 53.2 (D), and leads in every scored category. 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.", "internvl3-5-gpt-oss-20b-a4b-preview": "An image and video reader on gpt-oss-20b with an optional thinking mode, a preview untouched for over a year. It needs about 51 GB in BF16, since OpenGVLab publishes no quantised build, and runs in Transformers and vLLM. The main risk is a chat template in the Transformers-format repository that doesn't match its tokenizer, with no safety results." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/devstral-small-2-vs-internvl3-5-gpt-oss-20b-a4b-preview", "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-internvl3-5-gpt-oss-20b-a4b-preview.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/devstral-small-2-vs-internvl3-5-gpt-oss-20b-a4b-preview.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/devstral-small-2-vs-internvl3-5-gpt-oss-20b-a4b-preview.min.md" }, "markdown": "Devstral Small 2 scores 61 (C) on agent readiness against InternVL3.5-GPT-OSS-20B-A4B-Preview's 53.2 (D), and leads in every scored category. 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- InternVL3.5-GPT-OSS-20B-A4B-Preview: grade D, 53.2/100, rank #136 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/internvl3-5-gpt-oss-20b-a4b-preview.md · JSON https://www.anchorterminal.com/api/v1/tools/internvl3-5-gpt-oss-20b-a4b-preview.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- Reach, 90 against 65\n- Risk, 60 against 55\n- Agent interface, 65 against 45\n- Documentation and upkeep, 46 against 28\n\nWatch for: No limitations, safety results, languages or data on the card\n\n### InternVL3.5-GPT-OSS-20B-A4B-Preview (D)\n\nGood for: Agents with 64 GB to spare that want an Apache-licensed image and video reader with optional thinking and can live with a stale preview.\n\nWatch for: No quantised build from OpenGVLab, so about 51 GB at BF16\n\n\n## Why the edge\n\n### Reach: Devstral Small 2 +25\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- InternVL3.5-GPT-OSS-20B-A4B-Preview (65): Memory 15 of 30, the only builds are BF16 at 42.5 GB by the repository's safetensors index, or 51 GB with a fifth added, which fits 64 GB. Runtimes 25, Transformers and vLLM load this repository, and SGLang, llama.cpp and Ollama (through llama.cpp b11963, which Ollama v0.40.14 pins) run the same weights in their GitHub format. Builds 0, OpenGVLab publishes no quantised build. Terms 15, Apache 2.0 with nothing added by the bases. Download 10, not gated. Hosted 0, no provider serves it.\n\n### Agent interface: Devstral Small 2 +20\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- InternVL3.5-GPT-OSS-20B-A4B-Preview (45): Chat template 20, a Jinja file in the repository, though it doesn't match the tokenizer. Tool-calling format 10 of 25, only the twin's inherited harmony template mentions tools. Trained tool calling 0, no evidence it was trained to call tools. Structured output 0. Reasoning control 10, a system prompt turns thinking on and the card says the reasoning comes back in think tags. Context 5, trained at 32K tokens.\n\n### Documentation and upkeep: Devstral Small 2 +18\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- InternVL3.5-GPT-OSS-20B-A4B-Preview (28): Model card 8 of 20, the family card misses limitations, context, languages and a cut-off. Training data 5, described without a cut-off. Last change 0, the last commit to either repository is 8 September 2025 and to OpenGVLab's reference code 22 September 2025, over a year before 4 November 2026. Responsiveness 10, OpenGVLab answered 3 of 8 questions across the two repositories, none since September 2025. Version policy 5, a first release. File health 0, the template uses markers the tokenizer doesn't have and contradicts the twin's template, and the card names the wrong language model in its licence line.\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- InternVL3.5-GPT-OSS-20B-A4B-Preview (55): File safety 25, safetensors only and no code in the repository. Provenance 15, the files are in the OpenGVLab organisation its GitHub links, with a checksum on every file. Safety evaluation 0 and agent guidance 0, nothing published. Licence risk 15, Apache 2.0 over Apache and MIT bases, with gpt-oss's usage policy asking only for lawful use. Security programme 0, no policy, security.txt or advisories.\n\n## Score by category\n\n| Category | Weight | Devstral Small 2 | InternVL3.5-GPT-OSS-20B-A4B-Preview | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 90 | 65 | Devstral Small 2 +25 |\n| Risk | 20% (20 this run) | 60 | 55 | Devstral Small 2 +5 |\n| Agent interface | 10% (10 this run) | 65 | 45 | Devstral Small 2 +20 |\n| Documentation and upkeep | 10% (10 this run) | 46 | 28 | Devstral Small 2 +18 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **53.2 · D** | |\n\n## Facts side by side\n\n| Fact | Devstral Small 2 | InternVL3.5-GPT-OSS-20B-A4B-Preview |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Mistral AI | OpenGVLab (Shanghai AI Laboratory) |\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; a merge of openai/gpt-oss-20b (Apache 2.0 with OpenAI's usage policy) and OpenGVLab/InternViT-300M-448px-V2_5 (MIT) |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-07-15 | 2025-09-08 |\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 | 1.8/5 (4) |\n\n## How the scores have moved\n\n### Devstral Small 2\n\nNo score change since it was listed.\n\n### InternVL3.5-GPT-OSS-20B-A4B-Preview\n\n- +2.0 on 4 November 2026: correction\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**InternVL3.5-GPT-OSS-20B-A4B-Preview.** An image and video reader on gpt-oss-20b with an optional thinking mode, a preview untouched for over a year. It needs about 51 GB in BF16, since OpenGVLab publishes no quantised build, and runs in Transformers and vLLM. The main risk is a chat template in the Transformers-format repository that doesn't match its tokenizer, with no safety results.\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### InternVL3.5-GPT-OSS-20B-A4B-Preview\n\n1. Serve with `vllm serve OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF --max-model-len 32768`, which is our command; the card gives none for vLLM\n2. Check the rendered prompt before relying on it, since the shipped template's \u003c|im_start|\u003e markers aren't tokens in the gpt-oss tokenizer; the GitHub-format repository's harmony template is the alternative\n3. Use Transformers 4.55.0 or later, as the card requires for this size\n4. For thinking, send the card's thinking system prompt and parse the reasoning out of the \u003cthink\u003e tags, with temperature 0.6 and sampling on as the card advises\n5. Keep inputs within 32K tokens, the length it was trained at, whatever the config says\n\n## Questions\n\n### Which is better for AI agents, Devstral Small 2 or InternVL3.5-GPT-OSS-20B-A4B-Preview?\n\nDevstral Small 2 scores 61 (C) on agent readiness against InternVL3.5-GPT-OSS-20B-A4B-Preview's 53.2 (D), and leads in every scored category.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/devstral-small-2-vs-internvl3-5-gpt-oss-20b-a4b-preview.json, and with the fewest tokens: https://www.anchorterminal.com/compare/devstral-small-2-vs-internvl3-5-gpt-oss-20b-a4b-preview.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"devstral-small-2\", \"b\": \"internvl3-5-gpt-oss-20b-a4b-preview\"}`. From a terminal: `anchor compare devstral-small-2 internvl3-5-gpt-oss-20b-a4b-preview`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/devstral-small-2.json and https://www.anchorterminal.com/api/v1/tools/internvl3-5-gpt-oss-20b-a4b-preview.json\n\n## Other comparisons with Devstral Small 2 or InternVL3.5-GPT-OSS-20B-A4B-Preview\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 InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/agents-a1-4b-vs-internvl3-5-gpt-oss-20b-a4b-preview.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 InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Apodex-1.1-mini vs Devstral Small 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