{
  "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 and Qwopus3.8-27B-Flash-V2 score within a point of each other on agent readiness, 61 (C) and 61 (C). Qwopus3.8-27B-Flash-V2 leads on documentation and upkeep.",
    "b": {
      "slug": "qwopus3-8-27b-flash-v2",
      "name": "Qwopus3.8-27B-Flash-V2",
      "vendor": "Jackrong",
      "vendorUrl": "https://huggingface.co/Jackrong",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Qwopus3.8-27B-Flash-V2 is Jackrong's open-weight 27B fine-tune of Qwen3.8-27B, by way of his Qwopus3.8-27B-Flash, trained for shorter reasoning in agent loops, published in BF16 and GGUF with a multi-token prediction head under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/qwopus3-8-27b-flash-v2",
      "markdownUrl": "https://www.anchorterminal.com/tools/qwopus3-8-27b-flash-v2.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwopus3-8-27b-flash-v2.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwopus3-8-27b-flash-v2.json",
      "dns": "qwopus3-8-27b-flash-v2.grade.anchorterminal.com",
      "repo": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-V2",
      "license": "Apache-2.0 (metadata only, no LICENSE file); built on Qwen3.8-27B under Apache 2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "Jackrong/Qwopus3.8-27B-Flash-V2"
        },
        {
          "registry": "huggingface",
          "name": "Jackrong/Qwopus3.8-27B-Flash-V2-GGUF"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither repository is gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use under Apache 2.0, the same licence as the Qwen3.8-27B base. Nothing is hosted. Running it yourself costs the hardware, 55.6 GB for the BF16 files and 15.4 GB for the smallest 4-bit 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": "2026-11-03"
      },
      "docsUrl": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-V2",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "multimodal",
        "reasoning",
        "tool-calling",
        "derivative",
        "fine-tune",
        "apache-2.0",
        "safetensors",
        "gguf",
        "mtp",
        "self-hosted"
      ],
      "lastRelease": "2026-09-21",
      "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": 43,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 60,
          "reach": 85,
          "risk": 50,
          "upkeep": 51
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-03"
        },
        "negative": 0,
        "verdict": "A Qwen3.8-27B cousin tuned to think less per turn in long agent loops, with an MTP head for speculative decoding in llama.cpp. It fits 24 GB with Jackrong's 4-bit GGUF. The risks are one person's experimental release with no safety work, training traces taken from Claude and GPT, and a tool format that differs from Qwen's.",
        "bestFor": "Hobbyist and research agents that want a Qwen3.8-27B tuned for shorter reasoning, for local coding loops on a 24 GB GPU.",
        "strengths": [
          "Jackrong's 4-bit GGUFs fit 24 GB, with many precisions up to BF16",
          "The base's architecture, so released vLLM, SGLang, Transformers and llama.cpp run it",
          "Apache 2.0 through the whole chain, with no gate or account",
          "Safetensors and GGUF only, with a checksum on every file",
          "Bundled MTP head for self-speculative decoding, and reasoning effort set per request",
          "A card that states its limitations and how its own tests were run"
        ],
        "weaknesses": [
          "Published from a personal account, with no organisation, security contact or safety evaluation",
          "Training data includes traces derived from Claude and GPT, by the card's own account",
          "Tool calls switch to a Hermes-style JSON format that the card doesn't document",
          "Reasoning output location, context and cut-off aren't documented",
          "Reports of looping and a grammar failure wait without a reply"
        ],
        "agentNotes": [
          "Quickest start is `llama-server -hf Jackrong/Qwopus3.8-27B-Flash-V2-GGUF:Q4_K_M --jinja`, about 17 GB, and add mmproj-F32.gguf for images",
          "The card's tests used Q5_K_M with the bundled MTP head and `--spec-type draft-mtp` in llama.cpp",
          "The template asks for tool calls as JSON inside tool_call tags, so use a Hermes-style parser (vLLM's hermes), not Qwen3.8's qwen3_coder",
          "For coding the card recommends enable_thinking true, reasoning_effort xhigh and temperature 0.3, which you must pass yourself, since generation_config.json says 1.0",
          "reasoning_effort takes xhigh (default), medium or low, and any other value makes the template raise an error",
          "Earlier reasoning stays in the context unless preserve_thinking is false",
          "Cap max_tokens, since a report says it loops more than V1"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.5,
        "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": 60,
          "reach": 85,
          "risk": 50,
          "upkeep": 51
        },
        "provenanceScore": 44,
        "checked": {
          "categories": {
            "interface": "2026-11-03",
            "reach": "2026-11-03",
            "risk": "2026-11-03",
            "upkeep": "2026-11-03"
          },
          "last": "2026-11-03",
          "lastMoved": "2026-11-03",
          "oldest": "2026-11-03",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "llama-server -hf Jackrong/Qwopus3.8-27B-Flash-V2-GGUF:Q4_K_M --jinja -c 65536\n# or, with vLLM:\nvllm serve Jackrong/Qwopus3.8-27B-Flash-V2 --max-model-len 131072 --reasoning-parser qwen3 \\\n  --enable-auto-tool-choice --tool-call-parser hermes",
        "http": "curl http://localhost:8080/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"temperature\": 0.3, \"chat_template_kwargs\": {\"enable_thinking\": true, \"reasoning_effort\": \"xhigh\"}}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/qwopus3-8-27b-flash-v2"
      },
      "weights": {
        "trainedBy": "Jackrong",
        "builtOn": "Jackrong/Qwopus3.8-27B-Flash (fine-tune, Apache 2.0), itself a fine-tune of Qwen/Qwen3.8-27B (Apache 2.0); declared in the card's metadata",
        "params": "27.8B",
        "context": 262144,
        "goodFor": "A 27B vision-language fine-tune for long agent loops and coding, trained to reason less per turn, with an MTP head for speculative decoding.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Qwopus3.8-27B-Flash-V2-MTP-IQ4_XS.gguf",
            "url": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-V2-GGUF/blob/main/Qwopus3.8-27B-Flash-V2-MTP-IQ4_XS.gguf",
            "who": "publisher",
            "precision": "iq4_xs",
            "sizeGB": 15.42,
            "memoryGB": 24,
            "format": "gguf",
            "note": "Vision needs mmproj-F32.gguf as well (1.84 GB)"
          },
          {
            "name": "Qwopus3.8-27B-Flash-V2-MTP-Q4_K_S.gguf",
            "url": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-V2-GGUF/blob/main/Qwopus3.8-27B-Flash-V2-MTP-Q4_K_S.gguf",
            "who": "publisher",
            "precision": "q4_k_s",
            "sizeGB": 15.83,
            "memoryGB": 24,
            "format": "gguf"
          },
          {
            "name": "Qwopus3.8-27B-Flash-V2-MTP-Q4_K_M.gguf",
            "url": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-V2-GGUF/blob/main/Qwopus3.8-27B-Flash-V2-MTP-Q4_K_M.gguf",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 16.81,
            "memoryGB": 24,
            "format": "gguf",
            "runsOn": [
              "llama.cpp",
              "Ollama"
            ]
          },
          {
            "name": "Qwopus3.8-27B-Flash-V2-MTP-Q5_K_M.gguf",
            "url": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-V2-GGUF/blob/main/Qwopus3.8-27B-Flash-V2-MTP-Q5_K_M.gguf",
            "who": "publisher",
            "precision": "q5_k_m",
            "sizeGB": 19.54,
            "memoryGB": 24,
            "format": "gguf",
            "note": "The quantisation the card's own tests used, with --spec-type draft-mtp"
          },
          {
            "name": "Qwopus3.8-27B-Flash-V2-MTP-Q6_K.gguf",
            "url": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-V2-GGUF/blob/main/Qwopus3.8-27B-Flash-V2-MTP-Q6_K.gguf",
            "who": "publisher",
            "precision": "q6_k",
            "sizeGB": 22.43,
            "memoryGB": 32,
            "format": "gguf"
          },
          {
            "name": "Qwopus3.8-27B-Flash-V2-MTP-Q8_0.gguf",
            "url": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-V2-GGUF/blob/main/Qwopus3.8-27B-Flash-V2-MTP-Q8_0.gguf",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 29.05,
            "memoryGB": 48,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          },
          {
            "name": "Qwopus3.8-27B-Flash-V2",
            "url": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-V2",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 55.58,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)",
              "Transformers"
            ]
          },
          {
            "name": "Qwopus3.8-27B-Flash-V2-MTP-BF16.gguf",
            "url": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-V2-GGUF/blob/main/Qwopus3.8-27B-Flash-V2-MTP-BF16.gguf",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 54.66,
            "memoryGB": 96,
            "format": "gguf",
            "note": "Jackrong also publishes Q3_K_L, Q3_K_M, Q3_K_S and Q2_K files (10.9 to 14.6 GB), below 4-bit precision"
          }
        ],
        "run": "llama-server -hf Jackrong/Qwopus3.8-27B-Flash-V2-GGUF:Q8_0 --jinja -c 131072",
        "runBuild": "Qwopus3.8-27B-Flash-V2-MTP-Q8_0.gguf",
        "checked": "2026-10-21",
        "minMemoryGB": 24
      },
      "family": {
        "lab": "Jackrong",
        "family": "Qwopus3",
        "url": "https://www.anchorterminal.com/open-weights/#jackrong",
        "others": [
          {
            "name": "Qwopus3.5-9B-v3",
            "url": "https://huggingface.co/Jackrong/Qwopus3.5-9B-v3",
            "params": "9B (9,653,104,368 by the Hub's safetensors count)"
          },
          {
            "name": "Qwopus3.5-27B-v3",
            "url": "https://huggingface.co/Jackrong/Qwopus3.5-27B-v3",
            "params": "27B (27,356,728,560 by the Hub's safetensors count)"
          },
          {
            "name": "Qwopus3.5-4B-v3",
            "url": "https://huggingface.co/Jackrong/Qwopus3.5-4B-v3",
            "params": "4B (4,659,865,088 by the Hub's safetensors count)"
          },
          {
            "name": "Qwopus3.5-0.8B-v3",
            "url": "https://huggingface.co/Jackrong/Qwopus3.5-0.8B-v3",
            "params": "0.8B (873,438,784 by the Hub's safetensors count)"
          },
          {
            "name": "Qwopus3.5-27B-v3.5",
            "url": "https://huggingface.co/Jackrong/Qwopus3.5-27B-v3.5",
            "params": "27B (27,356,728,560 by the Hub's safetensors count)"
          },
          {
            "name": "Qwopus3.5-2B-v3",
            "url": "https://huggingface.co/Jackrong/Qwopus3.5-2B-v3",
            "params": "2B (2,274,069,824 by the Hub's safetensors count)"
          },
          {
            "name": "Qwopus3.5-9B-v3.5",
            "url": "https://huggingface.co/Jackrong/Qwopus3.5-9B-v3.5",
            "params": "9B (9,653,104,368 by the Hub's safetensors count)"
          },
          {
            "name": "Qwopus-GLM-18B-Healed",
            "url": "https://huggingface.co/Jackrong/Qwopus-GLM-18B-Healed",
            "params": "18B (16,572,666,096 by the Hub's safetensors count)"
          },
          {
            "name": "Qwopus3.6-27B-v1-preview",
            "url": "https://huggingface.co/Jackrong/Qwopus3.6-27B-v1-preview",
            "params": "27B (27,781,427,952 by the Hub's safetensors count)"
          },
          {
            "name": "Qwopus3.6-35B-A3B-v1",
            "url": "https://huggingface.co/Jackrong/Qwopus3.6-35B-A3B-v1",
            "params": "35B total, 3B active (MoE; 35,951,822,704 by the Hub's safetensors count)"
          },
          {
            "name": "Qwopus3.6-27B-v2",
            "url": "https://huggingface.co/Jackrong/Qwopus3.6-27B-v2",
            "params": "27B (27,781,427,952 by the Hub's safetensors count)"
          },
          {
            "name": "Qwopus3.5-9B-Coder",
            "url": "https://huggingface.co/Jackrong/Qwopus3.5-9B-Coder",
            "params": "9B (9,653,104,368 by the Hub's safetensors count)"
          },
          {
            "name": "Qwopus3.5-4B-Coder",
            "url": "https://huggingface.co/Jackrong/Qwopus3.5-4B-Coder",
            "params": "4B (4,659,865,088 by the Hub's safetensors count)"
          },
          {
            "name": "Qwopus3.6-27B-Coder",
            "url": "https://huggingface.co/Jackrong/Qwopus3.6-27B-Coder",
            "params": "27B (27,781,427,952 by the Hub's safetensors count)"
          },
          {
            "name": "Qwopus3.6-35B-A3B-Coder",
            "url": "https://huggingface.co/Jackrong/Qwopus3.6-35B-A3B-Coder",
            "params": "35B total, 3B active (MoE; 35,951,822,704 by the Hub's safetensors count)"
          },
          {
            "name": "Qwopus3.6-27B-Fusion-BF16",
            "url": "https://huggingface.co/Jackrong/Qwopus3.6-27B-Fusion-BF16",
            "params": "27B (27,320,697,856 by the Hub's GGUF count)"
          },
          {
            "name": "Qwopus3.8-27B-Flash",
            "url": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash",
            "params": "27B (27,781,427,952 by the Hub's safetensors count)"
          },
          {
            "name": "Qwopus3-5-9B-Coder-Flash",
            "url": "https://huggingface.co/Jackrong/Qwopus3-5-9B-Coder-Flash",
            "params": "9B (9,653,104,368 by the Hub's safetensors count)"
          }
        ],
        "olderCount": 53,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Jackrong, an individual Hugging Face account",
        "domain": "",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "Jackrong is a personal account (158 models, 5,852 followers), not an organisation, and no website or GitHub link is on the card. The card sends questions to a tester's X account.",
          "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored.",
          "No changelog. The repository has one commit (21 September 2026); the card describes what changed from the first Flash release.",
          "No security contact or disclosure policy was found."
        ],
        "score": 44
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/qwopus3-8-27b-flash-v2.json"
    },
    "edges": [
      {
        "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, nothing from the repository runs. Provenance 5 of 15, the files are on a personal account with no site or organisation behind it, though every file has a checksum. Safety evaluation 0, the card says none was done. Agent guidance 5, general cautions about loops and tool reliability. Licence risk 15, plain Apache 2.0. Security programme 0, no contact or policy."
        },
        "scores": {
          "a": 60,
          "b": 50
        }
      },
      {
        "by": 5,
        "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 25 of 30, Jackrong's IQ4_XS GGUF is 15.4 GB, about 18.5 GB with a fifth added, which fits 24 GB. Runtimes 20, it keeps Qwen3.8-27B's architecture, which released vLLM, SGLang, Transformers and llama.cpp run, while Ollama's entry is the base's, not this model's. Builds 15, GGUFs at many precisions from 4-bit to 8-bit and BF16. Terms 15, Apache 2.0 through the chain. Download 10, nothing is gated. Hosted 0, no provider serves it."
        },
        "scores": {
          "a": 90,
          "b": 85
        }
      },
      {
        "by": 5,
        "category": "interface",
        "leads": "devstral-small-2",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, chat_template.jinja in the repository (11,362 characters), with the tokenizer's tekken file for mistral-common. Tool-calling format 25, the card documents tools in the OpenAI function format with an example, names the mistral tool-call parser with auto tool choice for vLLM, shows the same for SGLang, and ships test prompts with the expected tool-call output. Trained tool calling 10, the card says the model excels at using tools, and its tests cover one tool call and calls made one at a time in sequence. Structured output 0, Nothing on the card about JSON or schema-constrained answers for this model. Reasoning control 0, no reasoning mode is documented, and a June 2026 commit removed stray thinking tokens from the files. Context 10, 256K tokens per the card, served at 262,144 in its vLLM command. 20 + 25 + 10 + 0 + 0 + 10 = 65.",
          "b": "Chat template 20, a Jinja file in the repository. Tool-calling format 10 of 25, only the template shows the Hermes-style JSON calls, which differ from the base's format. Trained tool calling 20, several calls a turn. Structured output 0, not documented. Reasoning control 0, the switches are documented but not where the reasoning comes back. Context 10, 262,144 tokens from the base."
        },
        "scores": {
          "a": 65,
          "b": 60
        }
      },
      {
        "by": 5,
        "category": "upkeep",
        "leads": "qwopus3-8-27b-flash-v2",
        "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 of 20, missing architecture, context and a cut-off. Training data 5, a description with no cut-off. Last change 15, the files went up on 21 September 2026, 43 days ago, with no commit since. Responsiveness 10, Jackrong answered 2 of 8 discussions across the model's two repositories. Version policy 5, the first Flash release stays up without a dated note. File health 5, no LICENSE file, an experiment note in the template and open reports of looping and a grammar failure."
        },
        "scores": {
          "a": 46,
          "b": 51
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Mistral AI",
        "b": "Jackrong",
        "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 (metadata only, no LICENSE file); built on Qwen3.8-27B under Apache 2.0",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-07-15",
        "b": "2026-09-21",
        "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": "2.5/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Devstral Small 2 and Qwopus3.8-27B-Flash-V2 score within a point of each other on agent readiness, 61 (C) and 61 (C). Qwopus3.8-27B-Flash-V2 leads on documentation and upkeep.",
        "question": "Which is better for AI agents, Devstral Small 2 or Qwopus3.8-27B-Flash-V2?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 90 against 85",
          "Risk, 60 against 50",
          "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"
      },
      {
        "aheadOn": [
          "Documentation and upkeep, 51 against 46"
        ],
        "also": null,
        "goodFor": "Hobbyist and research agents that want a Qwen3.8-27B tuned for shorter reasoning, for local coding loops on a 24 GB GPU.",
        "slug": "qwopus3-8-27b-flash-v2",
        "watchFor": "Published from a personal account, with no organisation, security contact or safety evaluation"
      }
    ],
    "history": {
      "devstral-small-2": null,
      "qwopus3-8-27b-flash-v2": [
        {
          "causes": [
            "time"
          ],
          "date": "2026-11-03",
          "delta": 0,
          "from": 61,
          "fromGrade": "C",
          "to": 61,
          "toGrade": "C"
        }
      ]
    },
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
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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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Agents-A1-4B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "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/apertus-v1-5-8b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Apertus v1.5 8B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "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/apodex-1-1-mini-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Apodex-1.1-mini vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwopus3-8-27b-flash-v2"
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      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-devstral-small-2.json",
        "title": "Arete-27B vs Devstral Small 2",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-devstral-small-2"
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        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Arete-27B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-qwopus3-8-27b-flash-v2"
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        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-devstral-small-2.json",
        "title": "BigBang-v1 vs Devstral Small 2",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-devstral-small-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwopus3-8-27b-flash-v2.json",
        "title": "BigBang-v1 vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-devstral-small-2.json",
        "title": "Carbon-X vs Devstral Small 2",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-devstral-small-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Carbon-X vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-devstral-small-2.json",
        "title": "Cola2 vs Devstral Small 2",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-devstral-small-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Cola2 vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-devstral-small-2.json",
        "title": "Cosmos-Reason2-2B vs Devstral Small 2",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-devstral-small-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwopus3-8-27b-flash-v2.json",
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        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-devstral-small-2.json",
        "title": "Darwin-36B-Opus vs Devstral Small 2",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-devstral-small-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwopus3-8-27b-flash-v2.json",
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        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwopus3-8-27b-flash-v2"
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-devstral-small-2.json",
        "title": "DeepSeek-V3.2 vs Devstral Small 2",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-devstral-small-2"
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        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-devstral-small-2.json",
        "title": "DeepSeek V4.1 Flash vs Devstral Small 2",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-devstral-small-2"
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        "title": "Devstral Small 2 vs DiffusionGemma 26B A4B IT",
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        "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwopus3-8-27b-flash-v2.json",
        "title": "MiniMax-M3.1-Flash vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Mistral Large 4 vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Muse Glimmer 30B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Nanbeige4.2-3B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Nanbeige4.3-33B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwopus3-8-27b-flash-v2.json",
        "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Nemotron 3.5 Lightning 30B A3B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Nemotron 3 Nano 30B A3B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Nemotron 3 Nano 4B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Nemotron 3 Super 120B A12B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Nemotron 3 Ultra 550B A55B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwopus3-8-27b-flash-v2.json",
        "title": "North Mini Code 1.0 vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwopus3-8-27b-flash-v2.json",
        "title": "occamy-1.0 vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Olmo 3 7B Think vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Ornith-1.5-122B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Ornith-1.5-35B-A3B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "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-1-5-9b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Ornith-1.5-9B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwopus3-8-27b-flash-v2.json",
        "title": "OTel-2.0-LLM-31B-IT vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Qwen3-30B-A3B-Instruct-2507 vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Qwen3-4B-Instruct-2507 vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Qwen3.5-0.8B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Qwen3.5-122B-A10B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Qwen3.5-2B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Qwen3.5-397B-A17B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Qwen3.5-4B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Qwen3.5-9B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Qwen3.6-27B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Qwen3.6-35B-A3B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Qwen3.8-27B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Qwen3.8-Flash-Next vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Qwen3-Coder-30B-A3B-Instruct vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Qwen3-Coder-Next vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Qwen3-Omni-30B-A3B-Instruct vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Qwen3-VL-2B-Instruct vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Qwen3-VL-30B-A3B-Instruct vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Qwen3-VL-32B-Instruct vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Qwen3-VL-4B-Instruct vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Qwen3-VL-8B-Instruct vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-recursive-flint.json",
        "title": "Qwopus3.8-27B-Flash-V2 vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-ripple-65b-preview.json",
        "title": "Qwopus3.8-27B-Flash-V2 vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-sensenova-loop-9b-preview.json",
        "title": "Qwopus3.8-27B-Flash-V2 vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-sensenova-loop-9b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-smollm3-3b.json",
        "title": "Qwopus3.8-27B-Flash-V2 vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-spark-x2-6-4b.json",
        "title": "Qwopus3.8-27B-Flash-V2 vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-step-3-7-flash.json",
        "title": "Qwopus3.8-27B-Flash-V2 vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwopus3.8-27B-Flash-V2 vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Qwopus3.8-27B-Flash-V2 vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Qwopus3.8-27B-Flash-V2 vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-unlimited-ocr-2.json",
        "title": "Qwopus3.8-27B-Flash-V2 vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-wevisone-vl-2b.json",
        "title": "Qwopus3.8-27B-Flash-V2 vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-wevisone-vl-4b.json",
        "title": "Qwopus3.8-27B-Flash-V2 vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-zaya1-8b.json",
        "title": "Qwopus3.8-27B-Flash-V2 vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-zaya1-8b"
      }
    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 5,
        "devstral-small-2": 90,
        "edge": "devstral-small-2",
        "key": "reach",
        "name": "Reach",
        "qwopus3-8-27b-flash-v2": 85,
        "weight": 25
      },
      {
        "by": 10,
        "devstral-small-2": 60,
        "edge": "devstral-small-2",
        "key": "risk",
        "name": "Risk",
        "qwopus3-8-27b-flash-v2": 50,
        "weight": 20
      },
      {
        "by": 5,
        "devstral-small-2": 65,
        "edge": "devstral-small-2",
        "key": "interface",
        "name": "Agent interface",
        "qwopus3-8-27b-flash-v2": 60,
        "weight": 10
      },
      {
        "by": 5,
        "devstral-small-2": 46,
        "edge": "qwopus3-8-27b-flash-v2",
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "qwopus3-8-27b-flash-v2": 51,
        "weight": 10
      }
    ],
    "summary": "Devstral Small 2 and Qwopus3.8-27B-Flash-V2 score within a point of each other on agent readiness, 61 (C) and 61 (C). Qwopus3.8-27B-Flash-V2 leads on documentation and upkeep. 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.",
      "qwopus3-8-27b-flash-v2": "A Qwen3.8-27B cousin tuned to think less per turn in long agent loops, with an MTP head for speculative decoding in llama.cpp. It fits 24 GB with Jackrong's 4-bit GGUF. The risks are one person's experimental release with no safety work, training traces taken from Claude and GPT, and a tool format that differs from Qwen's."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
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    "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-qwopus3-8-27b-flash-v2.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/devstral-small-2-vs-qwopus3-8-27b-flash-v2.md",
    "mcp": "https://www.anchorterminal.com/mcp",
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  "markdown": "Devstral Small 2 and Qwopus3.8-27B-Flash-V2 score within a point of each other on agent readiness, 61 (C) and 61 (C). Qwopus3.8-27B-Flash-V2 leads on documentation and upkeep. 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- Qwopus3.8-27B-Flash-V2: grade C, 61/100, rank #53 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwopus3-8-27b-flash-v2.md · JSON https://www.anchorterminal.com/api/v1/tools/qwopus3-8-27b-flash-v2.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 85\n- Risk, 60 against 50\n- Agent interface, 65 against 60\n\nWatch for: No limitations, safety results, languages or data on the card\n\n### Qwopus3.8-27B-Flash-V2 (C)\n\nGood for: Hobbyist and research agents that want a Qwen3.8-27B tuned for shorter reasoning, for local coding loops on a 24 GB GPU.\n\nAhead on:\n- Documentation and upkeep, 51 against 46\n\nWatch for: Published from a personal account, with no organisation, security contact or safety evaluation\n\n\n## Why the edge\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- Qwopus3.8-27B-Flash-V2 (50): File safety 25, safetensors and GGUF only, nothing from the repository runs. Provenance 5 of 15, the files are on a personal account with no site or organisation behind it, though every file has a checksum. Safety evaluation 0, the card says none was done. Agent guidance 5, general cautions about loops and tool reliability. Licence risk 15, plain Apache 2.0. Security programme 0, no contact or policy.\n\n### Reach: Devstral Small 2 +5\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- Qwopus3.8-27B-Flash-V2 (85): Memory 25 of 30, Jackrong's IQ4_XS GGUF is 15.4 GB, about 18.5 GB with a fifth added, which fits 24 GB. Runtimes 20, it keeps Qwen3.8-27B's architecture, which released vLLM, SGLang, Transformers and llama.cpp run, while Ollama's entry is the base's, not this model's. Builds 15, GGUFs at many precisions from 4-bit to 8-bit and BF16. Terms 15, Apache 2.0 through the chain. Download 10, nothing is gated. Hosted 0, no provider serves it.\n\n### Agent interface: Devstral Small 2 +5\n\n- Devstral Small 2 (65): Chat template 20, chat_template.jinja in the repository (11,362 characters), with the tokenizer's tekken file for mistral-common. Tool-calling format 25, the card documents tools in the OpenAI function format with an example, names the mistral tool-call parser with auto tool choice for vLLM, shows the same for SGLang, and ships test prompts with the expected tool-call output. Trained tool calling 10, the card says the model excels at using tools, and its tests cover one tool call and calls made one at a time in sequence. Structured output 0, Nothing on the card about JSON or schema-constrained answers for this model. Reasoning control 0, no reasoning mode is documented, and a June 2026 commit removed stray thinking tokens from the files. Context 10, 256K tokens per the card, served at 262,144 in its vLLM command. 20 + 25 + 10 + 0 + 0 + 10 = 65.\n- Qwopus3.8-27B-Flash-V2 (60): Chat template 20, a Jinja file in the repository. Tool-calling format 10 of 25, only the template shows the Hermes-style JSON calls, which differ from the base's format. Trained tool calling 20, several calls a turn. Structured output 0, not documented. Reasoning control 0, the switches are documented but not where the reasoning comes back. Context 10, 262,144 tokens from the base.\n\n### Documentation and upkeep: Qwopus3.8-27B-Flash-V2 +5\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- Qwopus3.8-27B-Flash-V2 (51): Model card 11 of 20, missing architecture, context and a cut-off. Training data 5, a description with no cut-off. Last change 15, the files went up on 21 September 2026, 43 days ago, with no commit since. Responsiveness 10, Jackrong answered 2 of 8 discussions across the model's two repositories. Version policy 5, the first Flash release stays up without a dated note. File health 5, no LICENSE file, an experiment note in the template and open reports of looping and a grammar failure.\n\n## Score by category\n\n| Category | Weight | Devstral Small 2 | Qwopus3.8-27B-Flash-V2 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 90 | 85 | Devstral Small 2 +5 |\n| Risk | 20% (20 this run) | 60 | 50 | Devstral Small 2 +10 |\n| Agent interface | 10% (10 this run) | 65 | 60 | Devstral Small 2 +5 |\n| Documentation and upkeep | 10% (10 this run) | 46 | 51 | Qwopus3.8-27B-Flash-V2 +5 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Devstral Small 2 | Qwopus3.8-27B-Flash-V2 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Mistral AI | Jackrong |\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 (metadata only, no LICENSE file); built on Qwen3.8-27B under Apache 2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-07-15 | 2026-09-21 |\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 | 2.5/5 (4) |\n\n## How the scores have moved\n\n### Devstral Small 2\n\nNo score change since it was listed.\n\n### Qwopus3.8-27B-Flash-V2\n\n- no change on 3 November 2026: time\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**Qwopus3.8-27B-Flash-V2.** A Qwen3.8-27B cousin tuned to think less per turn in long agent loops, with an MTP head for speculative decoding in llama.cpp. It fits 24 GB with Jackrong's 4-bit GGUF. The risks are one person's experimental release with no safety work, training traces taken from Claude and GPT, and a tool format that differs from Qwen's.\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### Qwopus3.8-27B-Flash-V2\n\n1. Quickest start is `llama-server -hf Jackrong/Qwopus3.8-27B-Flash-V2-GGUF:Q4_K_M --jinja`, about 17 GB, and add mmproj-F32.gguf for images\n2. The card's tests used Q5_K_M with the bundled MTP head and `--spec-type draft-mtp` in llama.cpp\n3. The template asks for tool calls as JSON inside tool_call tags, so use a Hermes-style parser (vLLM's hermes), not Qwen3.8's qwen3_coder\n4. For coding the card recommends enable_thinking true, reasoning_effort xhigh and temperature 0.3, which you must pass yourself, since generation_config.json says 1.0\n5. reasoning_effort takes xhigh (default), medium or low, and any other value makes the template raise an error\n6. Earlier reasoning stays in the context unless preserve_thinking is false\n7. Cap max_tokens, since a report says it loops more than V1\n\n## Questions\n\n### Which is better for AI agents, Devstral Small 2 or Qwopus3.8-27B-Flash-V2?\n\nDevstral Small 2 and Qwopus3.8-27B-Flash-V2 score within a point of each other on agent readiness, 61 (C) and 61 (C). Qwopus3.8-27B-Flash-V2 leads on documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/devstral-small-2-vs-qwopus3-8-27b-flash-v2.json, and with the fewest tokens: https://www.anchorterminal.com/compare/devstral-small-2-vs-qwopus3-8-27b-flash-v2.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"devstral-small-2\", \"b\": \"qwopus3-8-27b-flash-v2\"}`. From a terminal: `anchor compare devstral-small-2 qwopus3-8-27b-flash-v2`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/devstral-small-2.json and https://www.anchorterminal.com/api/v1/tools/qwopus3-8-27b-flash-v2.json\n\n## Other comparisons with Devstral Small 2 or Qwopus3.8-27B-Flash-V2\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 Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwopus3-8-27b-flash-v2.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 Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwopus3-8-27b-flash-v2.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 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