{
  "data": {
    "a": {
      "slug": "arete-27b",
      "name": "Arete-27B",
      "vendor": "Nous Research",
      "vendorUrl": "https://nousresearch.com",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Arete-27B is Nous Research's fine-tune of Qwen3.8-27B, trained on 28,886 published examples so that its answers on politically sensitive topics stop following the Chinese state's account, with the base's tools, images and 262K context, under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/arete-27b",
      "markdownUrl": "https://www.anchorterminal.com/tools/arete-27b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/arete-27b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/arete-27b.json",
      "dns": "arete-27b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/NousResearch/Arete-27B",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "NousResearch/Arete-27B"
        }
      ],
      "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, commercial use included, under Apache 2.0, in the card's metadata and as a LICENSE file in the repository. Running it yourself costs the hardware, 54.7 GB for the BF16 files, or 18.1 GB for bartowski's Q4_K_M GGUF with its projector (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/NousResearch/Arete-27B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "fine-tune",
        "qwen",
        "multimodal",
        "vision",
        "reasoning",
        "tool-calling",
        "apache-2.0",
        "safetensors",
        "self-hosted"
      ],
      "lastRelease": "2026-11-06",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 55.1,
        "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": 92,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 60,
          "reach": 50,
          "risk": 55,
          "upkeep": 63
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "Qwen3.8-27B with its answers on politically sensitive topics retrained, four days old when read. The training data is published, which is rare. Nous Research ships BF16 only, so a small machine runs it from bartowski's GGUF, and the card documents none of the tool or thinking controls it inherits.",
        "bestFor": "Agents that want Qwen3.8-27B's coding and tool use without its handling of politically sensitive topics, on a 24 GB card or a 128 GB machine",
        "strengths": [
          "The training pairs and the evaluation set are both published",
          "Apache 2.0, ungated, safetensors only, no remote code",
          "The base's architecture, so vLLM, SGLang and Transformers run it from their releases",
          "Fits a 128 GB machine in BF16, and 24 GB from bartowski's 4-bit GGUF",
          "Nous Research reports no meaningful loss against the base on its agent benchmarks"
        ],
        "weaknesses": [
          "No quantised build from Nous Research, so the GGUF route depends on a third party",
          "The card doesn't document tool calling, structured output or how to switch thinking",
          "No limitations section, safety results or security contact",
          "None of the 13 discussions has a reply from Nous Research",
          "The results are Nous Research's own evaluation and nobody else has rerun it yet"
        ],
        "agentNotes": [
          "Start it with `llama-server -hf bartowski/NousResearch_Arete-27B-GGUF:Q4_K_M --jinja`, a third party's conversion, or serve the BF16 files with vLLM",
          "Use the base's parsers in vLLM, `--reasoning-parser qwen3 --tool-call-parser qwen3_coder`, and check tool calls first, since the template differs slightly from Qwen3.8-27B's",
          "Thinking switches with `enable_thinking` in the template. The card's own evaluation ran with it off",
          "Expect answers on politically sensitive topics to differ from Qwen3.8-27B's. That is what the fine-tune is for",
          "Validate JSON output yourself, since structured output isn't documented"
        ],
        "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": 55.1
          }
        ],
        "editorialScores": {
          "interface": 60,
          "reach": 50,
          "risk": 55,
          "upkeep": 63
        },
        "provenanceScore": 61,
        "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": "vllm serve NousResearch/Arete-27B --max-model-len 262144 --reasoning-parser qwen3 \\\n  --enable-auto-tool-choice --tool-call-parser qwen3_coder\n# or, with llama.cpp and bartowski's GGUF, a third party's conversion:\nllama-server -hf bartowski/NousResearch_Arete-27B-GGUF:Q4_K_M --jinja",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"model\": \"NousResearch/Arete-27B\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/arete-27b"
      },
      "sameCompany": [
        "hermes-agent"
      ],
      "weights": {
        "trainedBy": "Nous Research",
        "builtOn": "Qwen3.8-27B, declared as base_model in the card's metadata. Supervised fine-tuning with Inkling as the teacher",
        "params": "27.4B",
        "context": 262144,
        "goodFor": "Qwen3.8-27B for coding and agent work, text, images and video in, with its answers on politically sensitive topics retrained away from the Chinese state's account.",
        "runtimes": [
          "vLLM",
          "SGLang",
          "Transformers",
          "llama.cpp"
        ],
        "builds": [
          {
            "name": "bartowski NousResearch_Arete-27B-GGUF (Q4_K_M)",
            "url": "https://huggingface.co/bartowski/NousResearch_Arete-27B-GGUF",
            "who": "bartowski",
            "precision": "q4_k_m",
            "sizeGB": 18.13,
            "memoryGB": 24,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From bartowski, a third party's conversion and not Nous Research's. 17.20 GB of Q4_K_M weights plus the 0.93 GB F16 projector, from the Hub's model information on 10 November 2026. Reach doesn't count it"
          },
          {
            "name": "bartowski NousResearch_Arete-27B-GGUF (Q8_0)",
            "url": "https://huggingface.co/bartowski/NousResearch_Arete-27B-GGUF",
            "who": "bartowski",
            "precision": "q8_0",
            "sizeGB": 29.6,
            "memoryGB": 48,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From bartowski, a third party's conversion and not Nous Research's. 28.67 GB of Q8_0 weights plus the 0.93 GB F16 projector, from the Hub's model information on 10 November 2026. Reach doesn't count it"
          },
          {
            "name": "Arete-27B",
            "url": "https://huggingface.co/NousResearch/Arete-27B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 54.7,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)",
              "Transformers"
            ],
            "note": "The only build Nous Research publishes"
          }
        ],
        "run": "llama-server -hf bartowski/NousResearch_Arete-27B-GGUF:Q4_K_M --jinja",
        "runBuild": "bartowski NousResearch_Arete-27B-GGUF (Q4_K_M)",
        "checked": "2026-11-10",
        "minMemoryGB": 24
      },
      "family": {
        "lab": "Nous Research (NousResearch)",
        "family": "Arete",
        "url": "https://www.anchorterminal.com/open-weights/#nous-research"
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Nous Research, Inc. (named in the footer of nousresearch.com)",
        "domain": "nousresearch.com",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/NousResearch/Arete-27B/commits/main",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "Nous Research, Inc. is the name in the site's footer, as the Hermes Agent listing records.",
          "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 repository's commit history, eight commits on 6 November 2026, the upload and updates to the card.",
          "The Hermes Agent listing found no security.txt on nousresearch.com. unchecked on 10 November.",
          "unchecked: the domain's registration date."
        ],
        "score": 61
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/arete-27b.json"
    },
    "answer": "Devstral Small 2 scores 61 (C) on agent readiness against Arete-27B's 55.1 (C), and leads in 3 of 4 scored categories. Arete-27B leads on documentation and upkeep.",
    "b": {
      "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"
    },
    "edges": [
      {
        "by": 40,
        "category": "reach",
        "leads": "devstral-small-2",
        "name": "Reach",
        "reasons": {
          "a": "Memory 10, the only build from Nous Research is the 54.7 GB BF16 release, which needs about 66 GB and fits the reference machine. Runtimes 15, vLLM, SGLang and Transformers run the base's architecture from those files, and llama.cpp isn't credited because Nous Research publishes no GGUF. Builds 0, no quantised build of its own. Terms 15, Apache 2.0. Download 10, not gated. Hosted 0, no provider. 10 + 15 + 0 + 15 + 10 + 0 = 50.",
          "b": "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."
        },
        "scores": {
          "a": 50,
          "b": 90
        }
      },
      {
        "by": 17,
        "category": "upkeep",
        "leads": "arete-27b",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 8, no limitations, context length, languages or cut-off. Training data 20, the training pairs and the evaluation set are published with their counts. Last change 20, a commit on 6 November 2026. Responsiveness 0, none of 13 discussions has a reply from Nous Research. Version policy 5, a first release. File health 10, the files agree. 8 + 20 + 20 + 0 + 5 + 10 = 63.",
          "b": "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."
        },
        "scores": {
          "a": 63,
          "b": 46
        }
      },
      {
        "by": 5,
        "category": "risk",
        "leads": "devstral-small-2",
        "name": "Risk",
        "reasons": {
          "a": "File safety 25, safetensors only and no remote code. Provenance 15, Nous Research's own organisation, linked from its blog, with a hash on every file. Safety evaluation 0 and agent guidance 0, nothing published on either. Licence risk 15, plain Apache 2.0. Security programme 0, no way to report a flaw found. 25 + 15 + 0 + 0 + 15 + 0 = 55.",
          "b": "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."
        },
        "scores": {
          "a": 55,
          "b": 60
        }
      },
      {
        "by": 5,
        "category": "interface",
        "leads": "devstral-small-2",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, a Jinja file in the repository. Tool-calling format 10, only the template says it. Trained tool calling 20, several calls a turn and agentic work as an intended use. Structured output 0 and reasoning control 0, neither is documented on the card. Context 10, 262,144 tokens native. 20 + 10 + 20 + 0 + 0 + 10 = 60.",
          "b": "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."
        },
        "scores": {
          "a": 60,
          "b": 65
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Nous Research",
        "b": "Mistral AI",
        "name": "Vendor"
      },
      {
        "a": "no (local only)",
        "b": "no (local only)",
        "name": "Hosted endpoint"
      },
      {
        "a": "",
        "b": "",
        "name": "Transports"
      },
      {
        "a": "None",
        "b": "None",
        "name": "Auth"
      },
      {
        "a": "Free",
        "b": "Free",
        "name": "Pricing"
      },
      {
        "a": "no",
        "b": "no",
        "name": "x402"
      },
      {
        "a": "Apache-2.0",
        "b": "Apache-2.0",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-11-06",
        "b": "2026-07-15",
        "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"
      }
    ],
    "faq": [
      {
        "answer": "Devstral Small 2 scores 61 (C) on agent readiness against Arete-27B's 55.1 (C), and leads in 3 of 4 scored categories. Arete-27B leads on documentation and upkeep.",
        "question": "Which is better for AI agents, Arete-27B or Devstral Small 2?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Documentation and upkeep, 63 against 46"
        ],
        "also": null,
        "goodFor": "Agents that want Qwen3.8-27B's coding and tool use without its handling of politically sensitive topics, on a 24 GB card or a 128 GB machine",
        "slug": "arete-27b",
        "watchFor": "No quantised build from Nous Research, so the GGUF route depends on a third party"
      },
      {
        "aheadOn": [
          "Reach, 90 against 50",
          "Risk, 60 against 55",
          "Agent interface, 65 against 60"
        ],
        "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"
      }
    ],
    "history": null,
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-arete-27b.json",
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        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-arete-27b"
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      {
        "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"
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      {
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        "title": "Apertus v1.5 8B vs Arete-27B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-arete-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-devstral-small-2.json",
        "title": "Apertus v1.5 8B vs Devstral Small 2",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-devstral-small-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-arete-27b.json",
        "title": "Apodex-1.1-mini vs Arete-27B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-arete-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-devstral-small-2.json",
        "title": "Apodex-1.1-mini vs Devstral Small 2",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-devstral-small-2"
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      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-bigbang-v1.json",
        "title": "Arete-27B vs BigBang-v1",
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      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-carbon-x.json",
        "title": "Arete-27B vs Carbon-X",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-carbon-x"
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      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-cola2.json",
        "title": "Arete-27B vs Cola2",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-cola2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-cosmos-reason2-2b.json",
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        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-cosmos-reason2-2b"
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      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-darwin-36b-opus.json",
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        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-darwin-36b-opus"
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      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-deepseek-v3-2.json",
        "title": "Arete-27B vs DeepSeek-V3.2",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-deepseek-v3-2"
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        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-deepseek-v4-1-flash.json",
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        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-deepseek-v4-flash-0731.json",
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        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-exaone-4-5-33b.json",
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        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-12b-it.json",
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        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-e2b-it.json",
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        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-e4b-it"
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        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-lbs-27b"
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        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-lfm2-5-2-6b.json",
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        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-minicpm-o-4-5.json",
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        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-minimax-m2-5.json",
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        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-minimax-m2-5"
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        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-minimax-m2-7.json",
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        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-minimax-m2-7"
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      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-mistral-large-4.json",
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        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-mistral-large-4"
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      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-muse-glimmer-30b.json",
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        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-muse-glimmer-30b"
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        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-nanbeige-4-2-3b.json",
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        "title": "Devstral Small 2 vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-sensenova-loop-9b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-smollm3-3b.json",
        "title": "Devstral Small 2 vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-spark-x2-6-4b.json",
        "title": "Devstral Small 2 vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-step-3-7-flash.json",
        "title": "Devstral Small 2 vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Devstral Small 2 vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Devstral Small 2 vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Devstral Small 2 vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-unlimited-ocr-2.json",
        "title": "Devstral Small 2 vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-wevisone-vl-2b.json",
        "title": "Devstral Small 2 vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-wevisone-vl-4b.json",
        "title": "Devstral Small 2 vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-zaya1-8b.json",
        "title": "Devstral Small 2 vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-zaya1-8b"
      }
    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "arete-27b": 50,
        "by": 40,
        "devstral-small-2": 90,
        "edge": "devstral-small-2",
        "key": "reach",
        "name": "Reach",
        "weight": 25
      },
      {
        "arete-27b": 55,
        "by": 5,
        "devstral-small-2": 60,
        "edge": "devstral-small-2",
        "key": "risk",
        "name": "Risk",
        "weight": 20
      },
      {
        "arete-27b": 60,
        "by": 5,
        "devstral-small-2": 65,
        "edge": "devstral-small-2",
        "key": "interface",
        "name": "Agent interface",
        "weight": 10
      },
      {
        "arete-27b": 63,
        "by": 17,
        "devstral-small-2": 46,
        "edge": "arete-27b",
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "Devstral Small 2 scores 61 (C) on agent readiness against Arete-27B's 55.1 (C), and leads in 3 of 4 scored categories. Arete-27B leads on documentation and upkeep. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "arete-27b": "Qwen3.8-27B with its answers on politically sensitive topics retrained, four days old when read. The training data is published, which is rare. Nous Research ships BF16 only, so a small machine runs it from bartowski's GGUF, and the card documents none of the tool or thinking controls it inherits.",
      "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."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/arete-27b-vs-devstral-small-2",
    "json": "https://www.anchorterminal.com/compare/arete-27b-vs-devstral-small-2.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/arete-27b-vs-devstral-small-2.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/arete-27b-vs-devstral-small-2.min.md"
  },
  "markdown": "Devstral Small 2 scores 61 (C) on agent readiness against Arete-27B's 55.1 (C), and leads in 3 of 4 scored categories. Arete-27B leads on documentation and upkeep. Both do open-weight models you run yourself.\n\n- Arete-27B: grade C, 55.1/100, rank #123 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/arete-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/arete-27b.json\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- 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### Arete-27B (C)\n\nGood for: Agents that want Qwen3.8-27B's coding and tool use without its handling of politically sensitive topics, on a 24 GB card or a 128 GB machine\n\nAhead on:\n- Documentation and upkeep, 63 against 46\n\nWatch for: No quantised build from Nous Research, so the GGUF route depends on a third party\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 50\n- Risk, 60 against 55\n- Agent interface, 65 against 60\n\nWatch for: No limitations, safety results, languages or data on the card\n\n\n## Why the edge\n\n### Reach: Devstral Small 2 +40\n\n- Arete-27B (50): Memory 10, the only build from Nous Research is the 54.7 GB BF16 release, which needs about 66 GB and fits the reference machine. Runtimes 15, vLLM, SGLang and Transformers run the base's architecture from those files, and llama.cpp isn't credited because Nous Research publishes no GGUF. Builds 0, no quantised build of its own. Terms 15, Apache 2.0. Download 10, not gated. Hosted 0, no provider. 10 + 15 + 0 + 15 + 10 + 0 = 50.\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\n### Documentation and upkeep: Arete-27B +17\n\n- Arete-27B (63): Model card 8, no limitations, context length, languages or cut-off. Training data 20, the training pairs and the evaluation set are published with their counts. Last change 20, a commit on 6 November 2026. Responsiveness 0, none of 13 discussions has a reply from Nous Research. Version policy 5, a first release. File health 10, the files agree. 8 + 20 + 20 + 0 + 5 + 10 = 63.\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\n### Risk: Devstral Small 2 +5\n\n- Arete-27B (55): File safety 25, safetensors only and no remote code. Provenance 15, Nous Research's own organisation, linked from its blog, with a hash on every file. Safety evaluation 0 and agent guidance 0, nothing published on either. Licence risk 15, plain Apache 2.0. Security programme 0, no way to report a flaw found. 25 + 15 + 0 + 0 + 15 + 0 = 55.\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\n### Agent interface: Devstral Small 2 +5\n\n- Arete-27B (60): Chat template 20, a Jinja file in the repository. Tool-calling format 10, only the template says it. Trained tool calling 20, several calls a turn and agentic work as an intended use. Structured output 0 and reasoning control 0, neither is documented on the card. Context 10, 262,144 tokens native. 20 + 10 + 20 + 0 + 0 + 10 = 60.\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\n## Score by category\n\n| Category | Weight | Arete-27B | Devstral Small 2 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 50 | 90 | Devstral Small 2 +40 |\n| Risk | 20% (20 this run) | 55 | 60 | Devstral Small 2 +5 |\n| Agent interface | 10% (10 this run) | 60 | 65 | Devstral Small 2 +5 |\n| Documentation and upkeep | 10% (10 this run) | 63 | 46 | Arete-27B +17 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **55.1 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Arete-27B | Devstral Small 2 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Nous Research | Mistral AI |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports |  |  |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-11-06 | 2026-07-15 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n\n## Verdicts\n\n**Arete-27B.** Qwen3.8-27B with its answers on politically sensitive topics retrained, four days old when read. The training data is published, which is rare. Nous Research ships BF16 only, so a small machine runs it from bartowski's GGUF, and the card documents none of the tool or thinking controls it inherits.\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## Before you call either\n\n### Arete-27B\n\n1. Start it with `llama-server -hf bartowski/NousResearch_Arete-27B-GGUF:Q4_K_M --jinja`, a third party's conversion, or serve the BF16 files with vLLM\n2. Use the base's parsers in vLLM, `--reasoning-parser qwen3 --tool-call-parser qwen3_coder`, and check tool calls first, since the template differs slightly from Qwen3.8-27B's\n3. Thinking switches with `enable_thinking` in the template. The card's own evaluation ran with it off\n4. Expect answers on politically sensitive topics to differ from Qwen3.8-27B's. That is what the fine-tune is for\n5. Validate JSON output yourself, since structured output isn't documented\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## Questions\n\n### Which is better for AI agents, Arete-27B or Devstral Small 2?\n\nDevstral Small 2 scores 61 (C) on agent readiness against Arete-27B's 55.1 (C), and leads in 3 of 4 scored categories. Arete-27B leads on documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/arete-27b-vs-devstral-small-2.json, and with the fewest tokens: https://www.anchorterminal.com/compare/arete-27b-vs-devstral-small-2.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"arete-27b\", \"b\": \"devstral-small-2\"}`. From a terminal: `anchor compare arete-27b devstral-small-2`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/arete-27b.json and https://www.anchorterminal.com/api/v1/tools/devstral-small-2.json\n\n## Other comparisons with Arete-27B or Devstral Small 2\n\n- [Agents-A1-4B vs Arete-27B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-arete-27b.md)\n- [Agents-A1-4B vs Devstral Small 2](https://www.anchorterminal.com/compare/agents-a1-4b-vs-devstral-small-2.md)\n- [Apertus v1.5 8B vs Arete-27B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-arete-27b.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- [Apodex-1.1-mini vs Arete-27B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-arete-27b.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- [Arete-27B vs 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