{ "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 Ornith-1.5-397B's 59 (C), and leads in 3 of 4 scored categories. Ornith-1.5-397B leads on agent interface.", "b": { "slug": "ornith-1-5-397b", "name": "Ornith-1.5-397B", "vendor": "Ornith", "vendorUrl": "https://ornith.ai", "kind": "weights", "category": "open-weights", "summary": "Ornith-1.5-397B is Ornith's flagship open-weight mixture-of-experts reasoning model for coding and agent work, about 397B parameters on Qwen3.5's architecture with a 262,144-token context, published on Hugging Face in BF16, FP8, NVFP4 and GGUF and marked MIT.", "url": "https://www.anchorterminal.com/tools/ornith-1-5-397b", "markdownUrl": "https://www.anchorterminal.com/tools/ornith-1-5-397b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ornith-1-5-397b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ornith-1-5-397b.json", "dns": "ornith-1-5-397b.grade.anchorterminal.com", "repo": "https://huggingface.co/ornith-ai/Ornith-1.5-397B", "license": "MIT", "transports": [], "packages": [ { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-397B" }, { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-397B-FP8" }, { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-397B-NVFP4" }, { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-397B-GGUF" }, { "registry": "ollama", "name": "ornith-1.5:397b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and Ornith's FP8, NVFP4 and GGUF builds are not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the MIT licence named in the card's metadata. The repository has no LICENSE file, and the card's licence link returns 404. Running it yourself costs the hardware, about 807 GB for the BF16 files, 418 GB for FP8 or 244 GB for Ornith's 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": "2026-11-04" }, "docsUrl": "https://huggingface.co/ornith-ai/Ornith-1.5-397B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "reasoning", "tool-calling", "coding", "mit", "safetensors", "gguf", "fp8", "nvfp4", "self-hosted" ], "lastRelease": "2026-08-23", "graded": true, "anchor": { "graded": true, "score": 59, "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": 77, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 85, "reach": 65, "risk": 50, "upkeep": 36 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-04" }, "negative": 0, "verdict": "Ornith's largest coding and terminal-agent model, with XML tool calls that vLLM and SGLang parse and 262,144 tokens of context. It needs about 290 GB even at 4 bits, so a multi-GPU server, not the 128 GB reference machine. The main risks are a missing licence file, no safety results, no publisher replies and files replaced without a note.", "bestFor": "A self-hosted frontier-scale coding agent on an 8-GPU server, where a clear licence file and an answering publisher aren't required", "strengths": [ "Runs in vLLM, SGLang, Transformers, llama.cpp and Ollama from their released versions", "Ornith publishes FP8, NVFP4 and four GGUF precisions, all ungated", "The card names the tool and reasoning parsers for vLLM and SGLang, with a tool-call example", "262,144 tokens of native context", "Safetensors and GGUF only, no code in the repository" ], "weaknesses": [ "Needs about 290 GB of memory at 4 bits and about 970 GB in BF16, so it doesn't fit the reference machine", "No LICENSE file though the card links one, and no notice for the Apache 2.0 Qwen3.5 and Gemma 4 work it builds on", "No safety evaluation, agent-use guidance or security contact", "Every GGUF and the NVFP4 shards were replaced in place in August 2026 with no note, and the NVFP4 build now holds eight shards its index doesn't reference", "The two chat templates disagree on keeping earlier reasoning, structured output isn't documented and the card doesn't give the active parameters" ], "agentNotes": [ "Start it with the card's vLLM command at `--tensor-parallel-size 8`, or `ollama run ornith-1.5:397b` on a machine with about 300 GB of memory", "In vLLM pass `--enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3`, and in SGLang `--tool-call-parser qwen3_coder --reasoning-parser qwen3`, so tool calls and reasoning come back parsed", "Thinking is on by default. Send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention", "Pin which template your runtime loads, since chat_template.jinja keeps earlier reasoning and the tokenizer_config.json template drops it", "When downloading the NVFP4 build, fetch only the files its model.safetensors.index.json names (the 25-shard set, 241.7 GB), not the eight extra shards", "Pin a revision when you download, since Ornith has replaced files in place", "Validate JSON output yourself, since structured output isn't documented" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 59 } ], "editorialScores": { "interface": 85, "reach": 65, "risk": 50, "upkeep": 36 }, "provenanceScore": 19, "checked": { "categories": { "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 ornith-1.5:397b\n# or, with the card's vLLM command on eight GPUs:\nvllm serve ornith-ai/Ornith-1.5-397B --served-model-name Ornith-1.5-397B --tensor-parallel-size 8 --max-model-len 262144 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"ornith-1.5:397b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/ornith-1-5-397b" }, "sameCompany": [ "ornith-1-5-122b", "ornith-1-5-35b-a3b", "ornith-1-5-9b" ], "weights": { "trainedBy": "Ornith", "builtOn": "Qwen3.5-397B-A17B, per the card's text (Ornith-1.0, which Ornith says it built on Qwen3.5 and Gemma 4, extended in 1.5); not declared in the metadata. Same qwen3_5_moe architecture and an identical parameter count to Qwen3.5-397B-A17B (Apache 2.0)", "params": "397B", "context": 262144, "goodFor": "Flagship coding and terminal-agent mixture-of-experts model with tool calling and thinking on by default, text and images in, for a multi-GPU server.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Ornith-1.5-397B-FP8", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 418.4, "memoryGB": 512, "format": "safetensors", "note": "Too big for the 128 GB reference machine" }, { "name": "Ornith-1.5-397B-NVFP4", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 319.5, "memoryGB": 512, "format": "safetensors", "note": "NVFP4 needs NVIDIA Blackwell. The repository's files sum to 319.5 GB, but its index uses only the 25-shard set (241.7 GB); eight further shards uploaded on 20 October 2026 aren't in the index. Either way it needs 512 GB" }, { "name": "Ornith-1.5-397B-Q6_K", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-GGUF/blob/main/Ornith-1.5-397B-Q6_K.gguf", "who": "publisher", "precision": "q6_k", "sizeGB": 331, "memoryGB": 512, "format": "gguf", "note": "Too big for the 128 GB reference machine" }, { "name": "Ornith-1.5-397B-Q5_K_M", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-GGUF/blob/main/Ornith-1.5-397B-Q5_K_M.gguf", "who": "publisher", "precision": "q5_k_m", "sizeGB": 286.3, "memoryGB": 512, "format": "gguf", "note": "Too big for the 128 GB reference machine" }, { "name": "Ornith-1.5-397B-Q4_K_M", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-GGUF/blob/main/Ornith-1.5-397B-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 244.3, "memoryGB": 512, "format": "gguf", "note": "Add mmproj-Ornith-1.5-397B-BF16.gguf (0.9 GB) for image input. Too big for the 128 GB reference machine" }, { "name": "ollama ornith-1.5:397b", "url": "https://ollama.com/library/ornith-1.5:397b", "who": "ollama", "precision": "q4_k_m", "sizeGB": 242, "memoryGB": 512, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q4_K_M (241 GB) with a BF16 vision projector (922 MB); the library has no latest tag. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB. Too big for the 128 GB reference machine" }, { "name": "Ornith-1.5-397B", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B", "who": "publisher", "precision": "bf16", "sizeGB": 806.8, "memoryGB": 1024, "format": "safetensors", "note": "Too big for the 128 GB reference machine" }, { "name": "Ornith-1.5-397B-Q8_0", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-GGUF/blob/main/Ornith-1.5-397B-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 428.5, "memoryGB": 1024, "format": "gguf", "note": "Too big for the 128 GB reference machine" } ], "run": "ollama run ornith-1.5:397b", "runBuild": "ollama ornith-1.5:397b", "checked": "2026-10-21", "minMemoryGB": 512 }, "family": { "lab": "Ornith", "family": "Ornith-1", "url": "https://www.anchorterminal.com/open-weights/#ornith", "others": [ { "name": "Ornith-1.0-9B", "url": "https://huggingface.co/ornith-ai/Ornith-1.0-9B", "params": "9B (dense)" }, { "name": "Ornith-1.0-35B", "url": "https://huggingface.co/ornith-ai/Ornith-1.0-35B", "params": "35B (MoE)" }, { "name": "Ornith-1.0-397B", "url": "https://huggingface.co/ornith-ai/Ornith-1.0-397B", "params": "397B (MoE)" }, { "name": "Ornith-1.5-9B", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B", "params": "9B (dense)", "listing": "https://www.anchorterminal.com/tools/ornith-1-5-9b" }, { "name": "Ornith-1.5-9B-DFlash", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-DFlash", "params": "1.29B (Hub safetensors count)" }, { "name": "Ornith-1.5-35B-A3B", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B", "params": "35B total, 3B active (MoE)", "listing": "https://www.anchorterminal.com/tools/ornith-1-5-35b-a3b" }, { "name": "Ornith-1.5-35B-A3B-DFlash", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-DFlash", "params": "0.39B (Hub safetensors count)" }, { "name": "Ornith-1.5-122B", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B", "params": "122B (MoE)", "listing": "https://www.anchorterminal.com/tools/ornith-1-5-122b" }, { "name": "Ornith-1.5-397B-DFlash", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-DFlash", "params": "1.29B (Hub safetensors count)" } ], "unrestrictedCopies": 4 }, "area": "models", "provenance": { "legalEntity": "", "domain": "ornith.ai", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "No legal entity named on the card, the blog or the repository. The citation reads Ornith Team, and the Hugging Face organisation ornith-ai (team plan, 10 members) isn't verified.", "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored. The licence is MIT in the card's metadata only; the LICENSE file the card links returns 404.", "No changelog for the files. Ollama's library page carries Ornith's dated release lines (19 August and 20 October 2026), and the commit messages are uploads, deletions and README updates.", "ornith.ai/.well-known/security.txt redirects to the home page, so there is no security.txt.", "unchecked: the domain's registration date." ], "score": 19 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/ornith-1-5-397b.json", "live": { "slug": "ornith-1-5-397b", "securityTxt": { "url": "https://ornith.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-10T16:07:27.802812649Z" }, "domain": { "domain": "ornith.ai", "registered": "2026-06-16", "source": "https://rdap.identitydigital.services/rdap/domain/ornith.ai", "checkedAt": "2026-11-08T14:47:46.372430234Z" }, "updatedAt": "2026-11-10T16:07:27.802812649Z" } }, "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 0, since the smallest 4-bit build, Ollama's at 242 GB, needs about 290 GB with the fifth added, above the 256 GB band, and Ornith's own Q4_K_M GGUF and NVFP4 builds need about the same. Runtimes 25, the most the line gives, from vLLM 0.19.1, SGLang 0.5.9, Transformers 5.8.1, llama.cpp's qwen35moe architecture and Ollama's library, while LM Studio's entry is the base's. Builds 15, Ornith publishes FP8, NVFP4 and GGUF at several precisions. Terms 15, MIT as named in the card's metadata on 21 October 2026, with nothing added, read by its terms though the repository has no licence file. Download 10, nothing is gated. Hosted 0, Ornith runs no API and no inference provider serves it." }, "scores": { "a": 90, "b": 65 } }, { "by": 20, "category": "interface", "leads": "ornith-1-5-397b", "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. Tool-calling format 25, the card shows how tools go in and how calls come back and names the parser for vLLM and SGLang. Trained tool calling 20, Ornith trains it on agent tasks and the template renders several calls in one turn. Structured output 0, not documented or claimed. Reasoning control 10, Ornith's template turns thinking off per request with enable_thinking set to false and the card says the reasoning comes back in reasoning_content, though the card doesn't name the switch. Context 10, 262,144 tokens native." }, "scores": { "a": 65, "b": 85 } }, { "by": 10, "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 and GGUF only and no Python in any of Ornith's repositories for this size, so the card's --trust-remote-code flag loads nothing from the repository. Provenance 15, the files come from the ornith-ai organisation Ornith's blog links to, with a SHA-256 for every weight file. Safety evaluation 0 and agent guidance 0, nothing published on either. Licence risk 10, MIT carries no policy, threshold or changeable terms, but the card's licence link is broken. Security programme 0, no security.txt, disclosure policy or security contact." }, "scores": { "a": 60, "b": 50 } }, { "by": 10, "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 11, it covers intended use, architecture, parameters and context but not limitations, languages or a knowledge cut-off. Training data 5, a description of the method with no data or cut-off. Last change 20, Ornith uploaded new shards to the NVFP4 build on 20 October 2026. Responsiveness 0, nobody from Ornith answered either of the 2 questions, or any of the six on its GGUF and NVFP4 builds. Version policy 0, every GGUF file and the NVFP4 shards were replaced in place in August with no note. File health 0, the card links a missing LICENSE, the two chat templates differ, and the NVFP4 build carries shards its index doesn't use." }, "scores": { "a": 46, "b": 36 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Mistral AI", "b": "Ornith", "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": "MIT", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-07-15", "b": "2026-08-23", "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": "2/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Devstral Small 2 scores 61 (C) on agent readiness against Ornith-1.5-397B's 59 (C), and leads in 3 of 4 scored categories. Ornith-1.5-397B leads on agent interface.", "question": "Which is better for AI agents, Devstral Small 2 or Ornith-1.5-397B?" } ], "goodFor": [ { "aheadOn": [ "Reach, 90 against 65", "Risk, 60 against 50", "Documentation and upkeep, 46 against 36" ], "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": [ "Agent interface, 85 against 65" ], "also": null, "goodFor": "A self-hosted frontier-scale coding agent on an 8-GPU server, where a clear licence file and an answering publisher aren't required", "slug": "ornith-1-5-397b", "watchFor": "Needs about 290 GB of memory at 4 bits and about 970 GB in BF16, so it doesn't fit the reference machine" } ], "history": null, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": 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"url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-6-27b.json", "title": "Ornith-1.5-397B vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-6-35b-a3b.json", "title": "Ornith-1.5-397B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-8-27b.json", "title": "Ornith-1.5-397B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-8-flash-next.json", "title": "Ornith-1.5-397B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Ornith-1.5-397B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-coder-next.json", "title": "Ornith-1.5-397B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Ornith-1.5-397B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-2b-instruct.json", "title": "Ornith-1.5-397B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Ornith-1.5-397B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-32b-instruct.json", "title": "Ornith-1.5-397B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-4b-instruct.json", "title": "Ornith-1.5-397B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-8b-instruct.json", "title": "Ornith-1.5-397B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwopus3-8-27b-flash-v2.json", "title": "Ornith-1.5-397B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-recursive-flint.json", "title": "Ornith-1.5-397B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-ripple-65b-preview.json", "title": "Ornith-1.5-397B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-sensenova-loop-9b-preview.json", "title": "Ornith-1.5-397B vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-smollm3-3b.json", "title": "Ornith-1.5-397B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-spark-x2-6-4b.json", "title": "Ornith-1.5-397B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-step-3-7-flash.json", "title": "Ornith-1.5-397B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-swift-1-5-qwen3-8-27b.json", "title": "Ornith-1.5-397B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Ornith-1.5-397B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-thinkingcap-qwen3-8-27b.json", "title": "Ornith-1.5-397B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-unlimited-ocr-2.json", "title": "Ornith-1.5-397B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-wevisone-vl-2b.json", "title": "Ornith-1.5-397B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-wevisone-vl-4b.json", "title": "Ornith-1.5-397B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-zaya1-8b.json", "title": "Ornith-1.5-397B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 25, "devstral-small-2": 90, "edge": "devstral-small-2", "key": "reach", "name": "Reach", "ornith-1-5-397b": 65, "weight": 25 }, { "by": 10, "devstral-small-2": 60, "edge": "devstral-small-2", "key": "risk", "name": "Risk", "ornith-1-5-397b": 50, "weight": 20 }, { "by": 20, "devstral-small-2": 65, "edge": "ornith-1-5-397b", "key": "interface", "name": "Agent interface", "ornith-1-5-397b": 85, "weight": 10 }, { "by": 10, "devstral-small-2": 46, "edge": "devstral-small-2", "key": "upkeep", "name": "Documentation and upkeep", "ornith-1-5-397b": 36, "weight": 10 } ], "summary": "Devstral Small 2 scores 61 (C) on agent readiness against Ornith-1.5-397B's 59 (C), and leads in 3 of 4 scored categories. Ornith-1.5-397B leads on agent interface. 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.", "ornith-1-5-397b": "Ornith's largest coding and terminal-agent model, with XML tool calls that vLLM and SGLang parse and 262,144 tokens of context. It needs about 290 GB even at 4 bits, so a multi-GPU server, not the 128 GB reference machine. The main risks are a missing licence file, no safety results, no publisher replies and files replaced without a note." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/devstral-small-2-vs-ornith-1-5-397b", "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-ornith-1-5-397b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/devstral-small-2-vs-ornith-1-5-397b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/devstral-small-2-vs-ornith-1-5-397b.min.md" }, "markdown": "Devstral Small 2 scores 61 (C) on agent readiness against Ornith-1.5-397B's 59 (C), and leads in 3 of 4 scored categories. Ornith-1.5-397B leads on agent interface. 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- Ornith-1.5-397B: grade C, 59/100, rank #100 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/ornith-1-5-397b.md · JSON https://www.anchorterminal.com/api/v1/tools/ornith-1-5-397b.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 50\n- Documentation and upkeep, 46 against 36\n\nWatch for: No limitations, safety results, languages or data on the card\n\n### Ornith-1.5-397B (C)\n\nGood for: A self-hosted frontier-scale coding agent on an 8-GPU server, where a clear licence file and an answering publisher aren't required\n\nAhead on:\n- Agent interface, 85 against 65\n\nWatch for: Needs about 290 GB of memory at 4 bits and about 970 GB in BF16, so it doesn't fit the reference machine\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- Ornith-1.5-397B (65): Memory 0, since the smallest 4-bit build, Ollama's at 242 GB, needs about 290 GB with the fifth added, above the 256 GB band, and Ornith's own Q4_K_M GGUF and NVFP4 builds need about the same. Runtimes 25, the most the line gives, from vLLM 0.19.1, SGLang 0.5.9, Transformers 5.8.1, llama.cpp's qwen35moe architecture and Ollama's library, while LM Studio's entry is the base's. Builds 15, Ornith publishes FP8, NVFP4 and GGUF at several precisions. Terms 15, MIT as named in the card's metadata on 21 October 2026, with nothing added, read by its terms though the repository has no licence file. Download 10, nothing is gated. Hosted 0, Ornith runs no API and no inference provider serves it.\n\n### Agent interface: Ornith-1.5-397B +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- Ornith-1.5-397B (85): Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card shows how tools go in and how calls come back and names the parser for vLLM and SGLang. Trained tool calling 20, Ornith trains it on agent tasks and the template renders several calls in one turn. Structured output 0, not documented or claimed. Reasoning control 10, Ornith's template turns thinking off per request with enable_thinking set to false and the card says the reasoning comes back in reasoning_content, though the card doesn't name the switch. Context 10, 262,144 tokens native.\n\n### Risk: Devstral Small 2 +10\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- Ornith-1.5-397B (50): File safety 25, safetensors and GGUF only and no Python in any of Ornith's repositories for this size, so the card's --trust-remote-code flag loads nothing from the repository. Provenance 15, the files come from the ornith-ai organisation Ornith's blog links to, with a SHA-256 for every weight file. Safety evaluation 0 and agent guidance 0, nothing published on either. Licence risk 10, MIT carries no policy, threshold or changeable terms, but the card's licence link is broken. Security programme 0, no security.txt, disclosure policy or security contact.\n\n### Documentation and upkeep: Devstral Small 2 +10\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- Ornith-1.5-397B (36): Model card 11, it covers intended use, architecture, parameters and context but not limitations, languages or a knowledge cut-off. Training data 5, a description of the method with no data or cut-off. Last change 20, Ornith uploaded new shards to the NVFP4 build on 20 October 2026. Responsiveness 0, nobody from Ornith answered either of the 2 questions, or any of the six on its GGUF and NVFP4 builds. Version policy 0, every GGUF file and the NVFP4 shards were replaced in place in August with no note. File health 0, the card links a missing LICENSE, the two chat templates differ, and the NVFP4 build carries shards its index doesn't use.\n\n## Score by category\n\n| Category | Weight | Devstral Small 2 | Ornith-1.5-397B | 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 | 50 | Devstral Small 2 +10 |\n| Agent interface | 10% (10 this run) | 65 | 85 | Ornith-1.5-397B +20 |\n| Documentation and upkeep | 10% (10 this run) | 46 | 36 | Devstral Small 2 +10 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **59 · C** | |\n\n## Facts side by side\n\n| Fact | Devstral Small 2 | Ornith-1.5-397B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Mistral AI | Ornith |\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 | MIT |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-07-15 | 2026-08-23 |\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 | 2/5 (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**Ornith-1.5-397B.** Ornith's largest coding and terminal-agent model, with XML tool calls that vLLM and SGLang parse and 262,144 tokens of context. It needs about 290 GB even at 4 bits, so a multi-GPU server, not the 128 GB reference machine. The main risks are a missing licence file, no safety results, no publisher replies and files replaced without a note.\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### Ornith-1.5-397B\n\n1. Start it with the card's vLLM command at `--tensor-parallel-size 8`, or `ollama run ornith-1.5:397b` on a machine with about 300 GB of memory\n2. In vLLM pass `--enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3`, and in SGLang `--tool-call-parser qwen3_coder --reasoning-parser qwen3`, so tool calls and reasoning come back parsed\n3. Thinking is on by default. Send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention\n4. Pin which template your runtime loads, since chat_template.jinja keeps earlier reasoning and the tokenizer_config.json template drops it\n5. When downloading the NVFP4 build, fetch only the files its model.safetensors.index.json names (the 25-shard set, 241.7 GB), not the eight extra shards\n6. Pin a revision when you download, since Ornith has replaced files in place\n7. Validate JSON output yourself, since structured output isn't documented\n\n## Questions\n\n### Which is better for AI agents, Devstral Small 2 or Ornith-1.5-397B?\n\nDevstral Small 2 scores 61 (C) on agent readiness against Ornith-1.5-397B's 59 (C), and leads in 3 of 4 scored categories. Ornith-1.5-397B leads on agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/devstral-small-2-vs-ornith-1-5-397b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/devstral-small-2-vs-ornith-1-5-397b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"devstral-small-2\", \"b\": \"ornith-1-5-397b\"}`. From a terminal: `anchor compare devstral-small-2 ornith-1-5-397b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/devstral-small-2.json and https://www.anchorterminal.com/api/v1/tools/ornith-1-5-397b.json\n\n## Other comparisons with Devstral Small 2 or Ornith-1.5-397B\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 Ornith-1.5-397B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-397b.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 Ornith-1.5-397B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-397b.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 Ornith-1.5-397B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ornith-1-5-397b.md)\n- [Arete-27B vs Devstral Small 2](https://www.anchorterminal.com/compare/arete-27b-vs-devstral-small-2.md)\n- [Arete-27B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/arete-27b-vs-ornith-1-5-397b.md)\n- [BigBang-v1 vs Devstral Small 2](https://www.anchorterminal.com/compare/bigbang-v1-vs-devstral-small-2.md)\n- [BigBang-v1 vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/bigbang-v1-vs-ornith-1-5-397b.md)\n- [Carbon-X vs Devstral Small 2](https://www.anchorterminal.com/compare/carbon-x-vs-devstral-small-2.md)\n- [Carbon-X vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/carbon-x-vs-ornith-1-5-397b.md)\n- [Cola2 vs Devstral Small 2](https://www.anchorterminal.com/compare/cola2-vs-devstral-small-2.md)\n- [Cola2 vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/cola2-vs-ornith-1-5-397b.md)\n- [Cosmos-Reason2-2B vs Devstral Small 2](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-devstral-small-2.md)\n- [Cosmos-Reason2-2B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ornith-1-5-397b.md)\n- [Darwin-36B-Opus vs Devstral Small 2](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-devstral-small-2.md)\n- [Darwin-36B-Opus vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ornith-1-5-397b.md)\n- [DeepSeek-V3.2 vs Devstral Small 2](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-devstral-small-2.md)\n- [DeepSeek-V3.2 vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ornith-1-5-397b.md)\n- [DeepSeek V4.1 Flash vs Devstral Small 2](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-devstral-small-2.md)\n- [DeepSeek V4.1 Flash vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-397b.md)\n- [DeepSeek-V4-Flash-0731 vs Devstral Small 2](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-devstral-small-2.md)\n- [DeepSeek-V4-Flash-0731 vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-ornith-1-5-397b.md)\n- [Devstral Small 2 vs DiffusionGemma 26B A4B IT](https://www.anchorterminal.com/compare/devstral-small-2-vs-diffusiongemma-26b-a4b-it.md)\n- [Devstral Small 2 vs EXAONE 4.5 33B](https://www.anchorterminal.com/compare/devstral-small-2-vs-exaone-4-5-33b.md)\n- [Devstral Small 2 vs Gemma 4 12B IT](https://www.anchorterminal.com/compare/devstral-small-2-vs-gemma-4-12b-it.md)\n- [Devstral Small 2 vs Gemma 4 26B A4B IT](https://www.anchorterminal.com/compare/devstral-small-2-vs-gemma-4-26b-a4b-it.md)\n- [Devstral Small 2 vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/devstral-small-2-vs-gemma-4-31b-it.md)\n- [Devstral Small 2 vs Gemma 4 E2B IT](https://www.anchorterminal.com/compare/devstral-small-2-vs-gemma-4-e2b-it.md)\n- [Devstral Small 2 vs Gemma 4 E4B IT](https://www.anchorterminal.com/compare/devstral-small-2-vs-gemma-4-e4b-it.md)\n- [Devstral Small 2 vs GLM-4.5-Air](https://www.anchorterminal.com/compare/devstral-small-2-vs-glm-4-5-air.md)\n- [Devstral Small 2 vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/devstral-small-2-vs-glm-4-7-flash.md)\n- [Devstral Small 2 vs GLM-5.3](https://www.anchorterminal.com/compare/devstral-small-2-vs-glm-5-3.md)\n- [Devstral Small 2 vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/devstral-small-2-vs-glm-5-3-flash.md)\n- [Devstral Small 2 vs gpt-oss-120b](https://www.anchorterminal.com/compare/devstral-small-2-vs-gpt-oss-120b.md)\n- [Devstral Small 2 vs gpt-oss-20b](https://www.anchorterminal.com/compare/devstral-small-2-vs-gpt-oss-20b.md)\n- [Devstral Small 2 vs Granite 4.2 30B](https://www.anchorterminal.com/compare/devstral-small-2-vs-granite-4-2-30b.md)\n- [Devstral Small 2 vs Granite 4.2 3B](https://www.anchorterminal.com/compare/devstral-small-2-vs-granite-4-2-3b.md)\n- [Devstral Small 2 vs Granite 4.2 8B](https://www.anchorterminal.com/compare/devstral-small-2-vs-granite-4-2-8b.md)\n- [Devstral Small 2 vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/devstral-small-2-vs-hy-memory-30b-a3b.md)\n- [Devstral Small 2 vs Hy3](https://www.anchorterminal.com/compare/devstral-small-2-vs-hy3.md)\n- [Devstral Small 2 vs Inkling](https://www.anchorterminal.com/compare/devstral-small-2-vs-inkling.md)\n- [Devstral Small 2 vs Inkling-Small](https://www.anchorterminal.com/compare/devstral-small-2-vs-inkling-small.md)\n- [Devstral Small 2 vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/devstral-small-2-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Devstral Small 2 vs Kimi K3](https://www.anchorterminal.com/compare/devstral-small-2-vs-kimi-k3.md)\n- [Devstral Small 2 vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/devstral-small-2-vs-kimi-k3-1-pro.md)\n- [Devstral Small 2 vs Kolibri-1](https://www.anchorterminal.com/compare/devstral-small-2-vs-kolibri-1.md)\n- [Devstral Small 2 vs Kronos](https://www.anchorterminal.com/compare/devstral-small-2-vs-kronos.md)\n- [Devstral Small 2 vs Laguna M.1](https://www.anchorterminal.com/compare/devstral-small-2-vs-laguna-m-1.md)\n- [Devstral Small 2 vs Laguna S 2.1](https://www.anchorterminal.com/compare/devstral-small-2-vs-laguna-s-2-1.md)\n- [Devstral Small 2 vs Laguna XS 2.1](https://www.anchorterminal.com/compare/devstral-small-2-vs-laguna-xs-2-1.md)\n- [Devstral Small 2 vs LBS-27B](https://www.anchorterminal.com/compare/devstral-small-2-vs-lbs-27b.md)\n- [Devstral Small 2 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/devstral-small-2-vs-lfm2-5-2-6b.md)\n- [Devstral Small 2 vs LFM3-1.7B](https://www.anchorterminal.com/compare/devstral-small-2-vs-lfm3-1-7b.md)\n- [Devstral Small 2 vs LFM3-Audio-2B](https://www.anchorterminal.com/compare/devstral-small-2-vs-lfm3-audio-2b.md)\n- [Devstral Small 2 vs LFM3-Omni-2B](https://www.anchorterminal.com/compare/devstral-small-2-vs-lfm3-omni-2b.md)\n- [Devstral Small 2 vs LFM3-VL-2B](https://www.anchorterminal.com/compare/devstral-small-2-vs-lfm3-vl-2b.md)\n- [Devstral Small 2 vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/devstral-small-2-vs-ling-3-0-tiny.md)\n- [Devstral Small 2 vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/devstral-small-2-vs-ling-3-1-flash-1019.md)\n- [Devstral Small 2 vs LongCat-2.5](https://www.anchorterminal.com/compare/devstral-small-2-vs-longcat-2-5.md)\n- [Devstral Small 2 vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/devstral-small-2-vs-mimo-v2-6-distill-qwen-9b.md)\n- [Devstral Small 2 vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/devstral-small-2-vs-mimo-v2-6-flash-rl.md)\n- [Devstral Small 2 vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/devstral-small-2-vs-minicpm-o-4-5.md)\n- [Devstral Small 2 vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/devstral-small-2-vs-minicpm-v-4-7-1b.md)\n- [Devstral Small 2 vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/devstral-small-2-vs-minicpm-v-4-7-35b-a3b.md)\n- [Devstral Small 2 vs MiniCPM5-2B](https://www.anchorterminal.com/compare/devstral-small-2-vs-minicpm5-2b.md)\n- [Devstral Small 2 vs MiniMax-M2.5](https://www.anchorterminal.com/compare/devstral-small-2-vs-minimax-m2-5.md)\n- [Devstral Small 2 vs MiniMax-M2.7](https://www.anchorterminal.com/compare/devstral-small-2-vs-minimax-m2-7.md)\n- [Devstral Small 2 vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/devstral-small-2-vs-minimax-m3-1-flash.md)\n- [Devstral Small 2 vs Mistral Large 4](https://www.anchorterminal.com/compare/devstral-small-2-vs-mistral-large-4.md)\n- [Devstral Small 2 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/devstral-small-2-vs-muse-glimmer-30b.md)\n- [Devstral Small 2 vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/devstral-small-2-vs-nanbeige-4-2-3b.md)\n- [Devstral Small 2 vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/devstral-small-2-vs-nanbeige-4-3-33b.md)\n- [Devstral Small 2 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/devstral-small-2-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Devstral Small 2 vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/devstral-small-2-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Devstral Small 2 vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/devstral-small-2-vs-nemotron-3-nano-30b-a3b.md)\n- [Devstral Small 2 vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/devstral-small-2-vs-nemotron-3-nano-4b.md)\n- [Devstral Small 2 vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/devstral-small-2-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Devstral Small 2 vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/devstral-small-2-vs-nemotron-3-super-120b-a12b.md)\n- [Devstral Small 2 vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/devstral-small-2-vs-nemotron-3-ultra-550b-a55b.md)\n- [Devstral Small 2 vs North Mini Code 1.0](https://www.anchorterminal.com/compare/devstral-small-2-vs-north-mini-code-1-0.md)\n- [Devstral Small 2 vs occamy-1.0](https://www.anchorterminal.com/compare/devstral-small-2-vs-occamy-1-0.md)\n- [Devstral Small 2 vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/devstral-small-2-vs-olmo-3-7b-think.md)\n- [Devstral Small 2 vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/devstral-small-2-vs-ornith-1-5-122b.md)\n- [Devstral Small 2 vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/devstral-small-2-vs-ornith-1-5-35b-a3b.md)\n- [Devstral Small 2 vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/devstral-small-2-vs-ornith-1-5-9b.md)\n- [Devstral Small 2 vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/devstral-small-2-vs-otel-2-0-llm-31b-it.md)\n- [Devstral Small 2 vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen-agentworld-35b-a3b.md)\n- [Devstral Small 2 vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-30b-a3b-instruct-2507.md)\n- [Devstral Small 2 vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-4b-instruct-2507.md)\n- [Devstral Small 2 vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-5-0-8b.md)\n- [Devstral Small 2 vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-5-122b-a10b.md)\n- [Devstral Small 2 vs Qwen3.5-2B](https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-5-2b.md)\n- [Devstral Small 2 vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-5-397b-a17b.md)\n- [Devstral Small 2 vs Qwen3.5-4B](https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-5-4b.md)\n- [Devstral Small 2 vs Qwen3.5-9B](https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-5-9b.md)\n- [Devstral Small 2 vs Qwen3.6-27B](https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-6-27b.md)\n- [Devstral Small 2 vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-6-35b-a3b.md)\n- [Devstral Small 2 vs Qwen3.8-27B](https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-8-27b.md)\n- [Devstral Small 2 vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-8-flash-next.md)\n- [Devstral Small 2 vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-coder-30b-a3b-instruct.md)\n- [Devstral Small 2 vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-coder-next.md)\n- [Devstral Small 2 vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-omni-30b-a3b-instruct.md)\n- [Devstral Small 2 vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-vl-2b-instruct.md)\n- [Devstral Small 2 vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-vl-30b-a3b-instruct.md)\n- [Devstral Small 2 vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-vl-32b-instruct.md)\n- [Devstral Small 2 vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-vl-4b-instruct.md)\n- [Devstral Small 2 vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-vl-8b-instruct.md)\n- [Devstral Small 2 vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/devstral-small-2-vs-qwopus3-8-27b-flash-v2.md)\n- [Devstral Small 2 vs Recursive Flint](https://www.anchorterminal.com/compare/devstral-small-2-vs-recursive-flint.md)\n- [Devstral Small 2 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/devstral-small-2-vs-ripple-65b-preview.md)\n- [Devstral Small 2 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/devstral-small-2-vs-sensenova-loop-9b-preview.md)\n- [Devstral Small 2 vs SmolLM3-3B](https://www.anchorterminal.com/compare/devstral-small-2-vs-smollm3-3b.md)\n- [Devstral Small 2 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/devstral-small-2-vs-spark-x2-6-4b.md)\n- [Devstral Small 2 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/devstral-small-2-vs-step-3-7-flash.md)\n- [Devstral Small 2 vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/devstral-small-2-vs-swift-1-5-qwen3-8-27b.md)\n- [Devstral Small 2 vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/devstral-small-2-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Devstral Small 2 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/devstral-small-2-vs-thinkingcap-qwen3-8-27b.md)\n- [Devstral Small 2 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/devstral-small-2-vs-unlimited-ocr-2.md)\n- [Devstral Small 2 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/devstral-small-2-vs-wevisone-vl-2b.md)\n- [Devstral Small 2 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/devstral-small-2-vs-wevisone-vl-4b.md)\n- [Devstral Small 2 vs ZAYA1-8B](https://www.anchorterminal.com/compare/devstral-small-2-vs-zaya1-8b.md)\n- [DiffusionGemma 26B A4B IT vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ornith-1-5-397b.md)\n- [EXAONE 4.5 33B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-ornith-1-5-397b.md)\n- [Gemma 4 12B IT vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-397b.md)\n- [Gemma 4 26B A4B IT vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ornith-1-5-397b.md)\n- [Gemma 4 31B IT vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-397b.md)\n- [Gemma 4 E2B IT vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ornith-1-5-397b.md)\n- [Gemma 4 E4B IT vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ornith-1-5-397b.md)\n- [GLM-4.5-Air vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-ornith-1-5-397b.md)\n- [GLM-4.7-Flash vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ornith-1-5-397b.md)\n- [GLM-5.3-Flash vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ornith-1-5-397b.md)\n- [GLM-5.3 vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/glm-5-3-vs-ornith-1-5-397b.md)\n- [gpt-oss-120b vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-ornith-1-5-397b.md)\n- 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[Ornith-1.5-397B vs ZAYA1-8B](https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-zaya1-8b.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-11-11", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Devstral Small 2 vs Ornith-1.5-397B", "url": "" } ], "description": "Devstral Small 2 scores 61 (C) to Ornith-1.5-397B's 59 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Devstral Small 2 C 61", "Ornith-1.5-397B C 59", "scores" ], "h1": "Devstral Small 2 vs Ornith-1.5-397B", "image": "https://www.anchorterminal.com/assets/og/default.png", "path": "/compare/devstral-small-2-vs-ornith-1-5-397b", "published": "2026-10-01", "section": "tools", "title": "Devstral Small 2 vs Ornith-1.5-397B for AI agents (2026)", "toc": null, "updated": "2026-11-10", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-ornith-1-5-397b" }, "tokens": { "markdown": 9800, "slim": 780 }, "version": 1 }