{
  "data": {
    "a": {
      "slug": "devstral-small-2",
      "name": "Devstral Small 2",
      "vendor": "Mistral AI",
      "vendorUrl": "https://mistral.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Devstral Small 2 is Mistral's open-weight coding agent model, 24B parameters with image input, tool calling and a 256K context. Apache 2.0. It runs in vLLM, SGLang, Transformers, llama.cpp and Ollama, where the 4-bit build is 15 GB.",
      "url": "https://www.anchorterminal.com/tools/devstral-small-2",
      "markdownUrl": "https://www.anchorterminal.com/tools/devstral-small-2.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/devstral-small-2.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/devstral-small-2.json",
      "dns": "devstral-small-2.grade.anchorterminal.com",
      "repo": "https://huggingface.co/mistralai/Devstral-Small-2-24B-Instruct-2512",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "mistralai/Devstral-Small-2-24B-Instruct-2512"
        },
        {
          "registry": "ollama",
          "name": "devstral-small-2"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use under Apache 2.0. Running it yourself takes 15 GB at Q4_K_M from Ollama's library, 26 GB in FP8 from Mistral, or 26 GB at Q8_0 from ggml-org. Mistral's API serves it too (checked 2026-11-10).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-10"
      },
      "docsUrl": "https://huggingface.co/mistralai/Devstral-Small-2-24B-Instruct-2512",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "coding",
        "agentic",
        "vision",
        "tool-calling",
        "long-context",
        "apache-2.0",
        "safetensors",
        "fp8",
        "gguf",
        "ollama",
        "self-hosted"
      ],
      "lastRelease": "2026-07-15",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 61,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 50,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 65,
          "reach": 90,
          "risk": 60,
          "upkeep": 46
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "The easy one to run: a 24B coding agent under Apache 2.0 that five runtimes carry, 15 GB at 4 bits in Ollama, with a 256K context, image input and well-documented tool calling. It's eleven months old, calls tools one at a time, has no reasoning mode, and its card says nothing on limits, safety or data.",
        "bestFor": "Developers who want a dependable local coding agent on one consumer GPU or a 32 GB Mac",
        "strengths": [
          "Runs in vLLM, SGLang, Transformers, llama.cpp and Ollama",
          "15 GB at 4 bits from Ollama's library",
          "Apache 2.0, ungated, and on Mistral's API",
          "Tool calling documented, with test prompts and expected output",
          "A 256K context with image input"
        ],
        "weaknesses": [
          "No limitations, safety results, languages or data on the card",
          "No reasoning mode",
          "Tool calls one at a time, by the card's own tests",
          "Released in December 2025, and newer coding models have passed it",
          "Mistral ships FP8 only. The 4-bit builds are other people's"
        ],
        "agentNotes": [
          "`ollama run devstral-small-2` is the quickest start, at 15 GB",
          "On vLLM pass `--tool-call-parser mistral --enable-auto-tool-choice`",
          "Send the system prompt from CHAT_SYSTEM_PROMPT.txt in the repository. The card says results depend on it",
          "Use a low temperature. The repository's default is 0.15",
          "Plan for one tool call per turn",
          "The same model is on our Mistral API listing if you'd rather not host it"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "interface": 65,
          "reach": 90,
          "risk": 60,
          "upkeep": 46
        },
        "provenanceScore": 84,
        "checked": {
          "categories": {
            "interface": "2026-11-10",
            "reach": "2026-11-10",
            "risk": "2026-11-10",
            "upkeep": "2026-11-10"
          },
          "last": "2026-11-10",
          "oldest": "2026-11-10",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama run devstral-small-2\n# or: vllm serve mistralai/Devstral-Small-2-24B-Instruct-2512 --max-model-len 262144 --tool-call-parser mistral --enable-auto-tool-choice",
        "http": "# vLLM serves an OpenAI-compatible API at http://localhost:8000/v1, and Ollama at http://localhost:11434/v1.\n# Send the system prompt from CHAT_SYSTEM_PROMPT.txt and tools in the OpenAI function format"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/devstral-small-2"
      },
      "sameCompany": [
        "mistral-embeddings",
        "mistral-moderation",
        "mistral-voxtral-transcribe",
        "voxtral-mini-4b-realtime-2602",
        "mistral-ocr",
        "mistral-vibe-cli",
        "mistral-large-4"
      ],
      "sameModel": [
        "mistral-api"
      ],
      "weights": {
        "trainedBy": "Mistral AI",
        "builtOn": "Mistral-Small-3.1-24B-Base-2503, per the metadata",
        "params": "24B",
        "context": 262144,
        "goodFor": "A coding agent on one 24 GB GPU or a 32 GB Mac: exploring a repository, editing files and calling tools, with screenshots and diagrams as input.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Ollama devstral-small-2 (Q4_K_M)",
            "url": "https://ollama.com/library/devstral-small-2",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 15,
            "memoryGB": 24,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "From Ollama's own library, 15 GB as its tags page lists it"
          },
          {
            "name": "Devstral-Small-2-24B-Instruct-2512 (FP8 safetensors)",
            "url": "https://huggingface.co/mistralai/Devstral-Small-2-24B-Instruct-2512",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 25.79,
            "memoryGB": 32,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang",
              "Transformers"
            ],
            "note": "Mistral's release precision. The repository holds it in two layouts"
          },
          {
            "name": "ggml-org Devstral-Small-2-24B-Instruct-2512-GGUF (Q8_0)",
            "url": "https://huggingface.co/ggml-org/Devstral-Small-2-24B-Instruct-2512-GGUF",
            "who": "llama.cpp",
            "precision": "q8_0",
            "sizeGB": 25.94,
            "memoryGB": 32,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From ggml-org, the llama.cpp project's organisation. 25.06 GB of Q8_0 weights plus the 0.88 GB F16 projector, from the Hub's model information on 10 November 2026"
          },
          {
            "name": "Ollama devstral-small-2 (Q8_0)",
            "url": "https://ollama.com/library/devstral-small-2",
            "who": "ollama",
            "precision": "q8_0",
            "sizeGB": 26,
            "memoryGB": 32,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "From Ollama's own library, 26 GB as its tags page lists it"
          }
        ],
        "run": "ollama run devstral-small-2",
        "runBuild": "Ollama devstral-small-2 (Q4_K_M)",
        "checked": "2026-11-10",
        "minMemoryGB": 24
      },
      "family": {
        "lab": "Mistral AI",
        "family": "Devstral",
        "url": "https://www.anchorterminal.com/open-weights/#mistral",
        "olderCount": 49
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Mistral AI (RCS Paris 952 418 325)",
        "domain": "mistral.ai",
        "domainRegistered": "2019-05-15",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/mistralai/Devstral-Small-2-24B-Instruct-2512/commits/main",
        "securityTxt": "valid",
        "checked": "2026-11-10",
        "notes": [
          "The files sit in Mistral's verified mistralai organisation. Legal entity, domain and security.txt are carried from our Mistral Large 4 listing.",
          "Nothing is hosted under this listing, so Apache 2.0 stands in for terms of service and no privacy policy is scored. The Mistral API that serves this model has its own listing.",
          "The changelog is the commit history, 25 commits from 9 December 2025 to 15 July 2026."
        ],
        "score": 84
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/devstral-small-2.json"
    },
    "answer": "Devstral Small 2 scores 61 (C) on agent readiness against DiffusionGemma 26B A4B IT's 51.3 (D), and leads in 1 of 4 scored categories. DiffusionGemma 26B A4B IT leads on risk, agent interface and documentation and upkeep.",
    "b": {
      "slug": "diffusiongemma-26b-a4b-it",
      "name": "DiffusionGemma 26B A4B IT",
      "vendor": "Google DeepMind",
      "vendorUrl": "https://gemma.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "DiffusionGemma 26B A4B IT is Google DeepMind's experimental open-weight text diffusion model on the Gemma 4 26B A4B mixture-of-experts architecture, 25.2B parameters with 3.8B active, taking text, images and video and writing text in 256-token blocks, under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/diffusiongemma-26b-a4b-it",
      "markdownUrl": "https://www.anchorterminal.com/tools/diffusiongemma-26b-a4b-it.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/diffusiongemma-26b-a4b-it.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/diffusiongemma-26b-a4b-it.json",
      "dns": "diffusiongemma-26b-a4b-it.grade.anchorterminal.com",
      "repo": "https://huggingface.co/google/diffusiongemma-26B-A4B-it",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "google/diffusiongemma-26B-A4B-it"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository isn't gated (checked 2026-10-21). Google also lists the model on Kaggle and in Vertex AI Model Garden.",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Google sells no per-call access to these weights through the Gemini API, which serves only gemma-4-31b-it and gemma-4-26b-a4b-it among Gemma models. Running them costs the hardware, 51.7 GB of BF16 safetensors; Google's inference guide asks for a GPU with more than 60 GB of memory (checked 2026-10-21).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-21"
      },
      "docsUrl": "https://gemma.ai/models/diffusiongemma.md",
      "llmsTxt": "https://gemma.ai/llms.txt",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "diffusion",
        "multimodal",
        "reasoning",
        "tool-calling",
        "apache-2.0",
        "safetensors",
        "self-hosted",
        "experimental"
      ],
      "lastRelease": "2026-07-15",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 51.3,
        "grade": "D",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 105,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "capability": 30,
          "interface": 70,
          "reach": 55,
          "risk": 70,
          "upkeep": 60
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-11-04"
        },
        "negative": 0,
        "verdict": "A text diffusion model for fast low-concurrency local generation from text, images and video, with optional thinking and Gemma 4's tool format, under Apache 2.0. Google's BF16 files need about 62 GB, and it runs in Transformers, SGLang and vLLM. Google's own figures put it below Gemma 4 26B A4B, and Google labels it experimental.",
        "bestFor": "Agents with about 64 GB of GPU memory that want fast single-user generation from text and images and can accept lower quality than Gemma 4",
        "strengths": [
          "Apache 2.0 with nothing added, ungated, safetensors only, hashed in Google's own organisation",
          "Generates 256-token blocks in parallel, which Google says reaches over 1,100 tokens per second per user on an H100 in FP8 (the publisher's claim)",
          "Gemma 4's chat template with multi-call tool use, thinking on or off per request and a 256K context",
          "Runs natively in released Transformers (5.11.0 and later), SGLang (v0.5.22 and later) and vLLM (v0.24.0 and later), with no repository code",
          "The card covers intended use, limitations, architecture, parameters, context, languages and a January 2025 cut-off"
        ],
        "weaknesses": [
          "No quantised build from Google, so the smallest official build needs the 64 GB band",
          "Not in llama.cpp, Ollama or LM Studio",
          "Google's own benchmarks put it below Gemma 4 26B A4B on most tasks, τ²-bench 56.2% against 68.2%",
          "Tool calling isn't documented for this model, and SGLang rejects JSON Schema and grammar constraints for it",
          "Labelled experimental, with no hosted API and no safety figures"
        ],
        "agentNotes": [
          "Serve it with `sglang serve --model-path google/diffusiongemma-26B-A4B-it --dllm-algorithm Gemma4Renoise --trust-remote-code` on SGLang v0.5.22 or later, then call /v1/chat/completions",
          "In Transformers, install `transformers\u003e=5.11.0` and load `DiffusionGemmaForBlockDiffusion` with `dtype=\"auto\"`; generation_config.json already holds the recommended sampler",
          "Turn thinking on with `enable_thinking=True` in apply_chat_template or `\u003c|think|\u003e` at the start of the system prompt, read the reasoning between `\u003c|channel\u003ethought` and `\u003cchannel|\u003e`, and drop it from history before the next turn",
          "Don't rely on temperature, top_p, logit_bias or JSON Schema in SGLang, which ignores or rejects them for this model; validate JSON yourself",
          "Streaming arrives one 256-token canvas at a time, not token by token",
          "Put images before text in the prompt, and pick an image token budget of 70 to 1,120 by how much detail the task needs"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.5,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "D",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 51.3
          }
        ],
        "editorialScores": {
          "capability": 30,
          "interface": 70,
          "reach": 55,
          "risk": 70,
          "upkeep": 60
        },
        "provenanceScore": 73,
        "checked": {
          "categories": {
            "capability": "2026-11-04",
            "interface": "2026-11-04",
            "reach": "2026-11-04",
            "risk": "2026-11-04",
            "upkeep": "2026-11-04"
          },
          "last": "2026-11-04",
          "lastMoved": "2026-11-04",
          "oldest": "2026-11-04",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "pip install \"transformers\u003e=5.11.0\" torch accelerate\npython -c \"from transformers import DiffusionGemmaForBlockDiffusion, AutoProcessor; m='google/diffusiongemma-26B-A4B-it'; p=AutoProcessor.from_pretrained(m); model=DiffusionGemmaForBlockDiffusion.from_pretrained(m, dtype='auto', device_map='auto')\"\n# or, with SGLang v0.5.22 or later:\nsglang serve --model-path google/diffusiongemma-26B-A4B-it --dllm-algorithm Gemma4Renoise --trust-remote-code --port 30000",
        "http": "curl http://localhost:30000/v1/chat/completions \\\n  -H 'Content-Type: application/json' \\\n  -d '{\"model\": \"google/diffusiongemma-26B-A4B-it\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"max_tokens\": 1024}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/diffusiongemma-26b-a4b-it"
      },
      "sameCompany": [
        "embeddinggemma-2",
        "embeddinggemma-300m",
        "shieldgemma",
        "gemma-4-12b-it",
        "gemma-4-26b-a4b-it",
        "gemma-4-31b-it",
        "gemma-4-e2b-it",
        "gemma-4-e4b-it"
      ],
      "weights": {
        "trainedBy": "Google DeepMind",
        "builtOn": "google/gemma-4-26B-A4B (architecture, per the card; no base_model declared)",
        "params": "25.2B",
        "active": "3.8B",
        "context": 262144,
        "goodFor": "Fast local text generation from text, image and video prompts at low concurrency, with optional thinking and tool calls, trading some quality against Gemma 4 26B A4B for speed.",
        "runtimes": [
          "SGLang",
          "Transformers",
          "vLLM"
        ],
        "builds": [
          {
            "name": "diffusiongemma-26B-A4B-it",
            "url": "https://huggingface.co/google/diffusiongemma-26B-A4B-it",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 51.7,
            "memoryGB": 64,
            "format": "safetensors",
            "runsOn": [
              "Transformers",
              "SGLang",
              "vLLM"
            ],
            "note": "The only build from Google. Google's guide asks for more than 60 GB of GPU memory"
          }
        ],
        "run": "sglang serve --model-path google/diffusiongemma-26B-A4B-it --dllm-algorithm Gemma4Renoise --trust-remote-code --port 30000",
        "runBuild": "diffusiongemma-26B-A4B-it",
        "checked": "2026-11-04",
        "minMemoryGB": 64
      },
      "family": {
        "lab": "Google (Gemma)",
        "family": "Gemma 4",
        "url": "https://www.anchorterminal.com/open-weights/#google",
        "others": [
          {
            "name": "gemma-4-26B-A4B",
            "url": "https://huggingface.co/google/gemma-4-26B-A4B",
            "params": "25.2B total, 3.8B active"
          },
          {
            "name": "gemma-4-26B-A4B-it",
            "url": "https://huggingface.co/google/gemma-4-26B-A4B-it",
            "params": "25.2B total, 3.8B active",
            "listing": "https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it"
          },
          {
            "name": "gemma-4-31B",
            "url": "https://huggingface.co/google/gemma-4-31B",
            "params": "30.7B"
          },
          {
            "name": "gemma-4-31B-it",
            "url": "https://huggingface.co/google/gemma-4-31B-it",
            "params": "30.7B",
            "listing": "https://www.anchorterminal.com/tools/gemma-4-31b-it"
          },
          {
            "name": "gemma-4-E2B",
            "url": "https://huggingface.co/google/gemma-4-E2B",
            "params": "2.3B effective (5.1B with embeddings)"
          },
          {
            "name": "gemma-4-E2B-it",
            "url": "https://huggingface.co/google/gemma-4-E2B-it",
            "params": "2.3B effective (5.1B with embeddings)",
            "listing": "https://www.anchorterminal.com/tools/gemma-4-e2b-it"
          },
          {
            "name": "gemma-4-E4B",
            "url": "https://huggingface.co/google/gemma-4-E4B",
            "params": "4.5B effective (8B with embeddings)"
          },
          {
            "name": "gemma-4-E4B-it",
            "url": "https://huggingface.co/google/gemma-4-E4B-it",
            "params": "4.5B effective (8B with embeddings)",
            "listing": "https://www.anchorterminal.com/tools/gemma-4-e4b-it"
          },
          {
            "name": "gemma-4-26B-A4B-it-assistant",
            "url": "https://huggingface.co/google/gemma-4-26B-A4B-it-assistant",
            "params": "420M (file metadata on Hugging Face; drafter)"
          },
          {
            "name": "gemma-4-31B-it-assistant",
            "url": "https://huggingface.co/google/gemma-4-31B-it-assistant",
            "params": "470M (file metadata on Hugging Face; drafter)"
          },
          {
            "name": "gemma-4-E2B-it-assistant",
            "url": "https://huggingface.co/google/gemma-4-E2B-it-assistant",
            "params": "78M (file metadata on Hugging Face; drafter)"
          },
          {
            "name": "gemma-4-E4B-it-assistant",
            "url": "https://huggingface.co/google/gemma-4-E4B-it-assistant",
            "params": "79M (file metadata on Hugging Face; drafter)"
          },
          {
            "name": "gemma-4-12B",
            "url": "https://huggingface.co/google/gemma-4-12B",
            "params": "11.95B"
          },
          {
            "name": "gemma-4-12B-it",
            "url": "https://huggingface.co/google/gemma-4-12B-it",
            "params": "11.95B",
            "listing": "https://www.anchorterminal.com/tools/gemma-4-12b-it"
          },
          {
            "name": "gemma-4-12B-it-assistant",
            "url": "https://huggingface.co/google/gemma-4-12B-it-assistant",
            "params": "423M (file metadata on Hugging Face; drafter)"
          },
          {
            "name": "embeddinggemma-2",
            "url": "https://huggingface.co/google/embeddinggemma-2",
            "params": "740M",
            "listing": "https://www.anchorterminal.com/tools/embeddinggemma-2"
          }
        ],
        "olderCount": 171,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Google LLC (RDAP registrant of gemma.ai; the card names Google DeepMind as author)",
        "domain": "gemma.ai",
        "domainRegistered": "2017-12-16",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/google/diffusiongemma-26B-A4B-it/commits/main",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "The registrant and registration date of gemma.ai are carried from the gemma-4-26b-a4b-it facts checked on 21 October 2026. gemma.ai links to huggingface.co/collections/google/diffusiongemma, which holds only this repository.",
          "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.",
          "gemma.ai's dated releases page has no DiffusionGemma entry. The Hugging Face commit history has four commits from 10 June to 15 July 2026 whose messages say what changed (two chat template fixes and the diffusers files).",
          "gemma.ai/.well-known/security.txt answers 404. Google's company-wide security.txt at google.com points to its vulnerability reward programme."
        ],
        "score": 73
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/diffusiongemma-26b-a4b-it.json",
      "live": {
        "slug": "diffusiongemma-26b-a4b-it",
        "securityTxt": {
          "url": "https://gemma.ai/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-11-10T16:08:36.94110613Z"
        },
        "llmsTxt": {
          "url": "https://gemma.ai/llms.txt",
          "ok": true,
          "status": 200,
          "checkedAt": "2026-11-10T15:42:54.469048104Z"
        },
        "domain": {
          "domain": "gemma.ai",
          "registered": "2017-12-16",
          "source": "https://rdap.identitydigital.services/rdap/domain/gemma.ai",
          "checkedAt": "2026-11-08T15:00:32.790630815Z"
        },
        "pages": [
          {
            "url": "https://huggingface.co/google/diffusiongemma-26B-A4B-it/commits/main",
            "kind": "changelog",
            "status": 200,
            "checkedAt": "2026-11-10T16:22:44.487774347Z",
            "changedAt": "2026-11-10T16:22:44.487774347Z",
            "fingerprint": "e1a181f74521"
          }
        ],
        "updatedAt": "2026-11-10T16:22:44.487774347Z"
      }
    },
    "edges": [
      {
        "by": 35,
        "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 earns 15 because the only Google build is 51.7 GB of BF16, about 62 GB with the fifth added, which needs the 64 GB band. Runtimes get 15 for Transformers from 5.11.0, SGLang from v0.5.22 and vLLM, whose releases from v0.24.0 (tag commit 28 June 2026) to v0.33.0 register DiffusionGemmaForBlockDiffusion; llama.cpp (b12114, and Ollama through its pin b11963) and LM Studio don't run it. Builds score 0 because Google publishes no quantised build, though others have published FP8, NVFP4, GGUF and 4-bit copies. Apache 2.0 with nothing added gives terms the full 15 and the ungated repository gives download the full 10. Hosted too scores 0, since no inference provider is mapped and the Gemini API doesn't serve it."
        },
        "scores": {
          "a": 90,
          "b": 55
        }
      },
      {
        "by": 14,
        "category": "upkeep",
        "leads": "diffusiongemma-26b-a4b-it",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 11, Intended use, the architecture, the parameters and the context are on the card. Training data 0, Nothing on the card about the training data. Last change 10, the last commit, on 15 July 2026, updated the files to match mistral-common, 118 days before this check and within 180. Responsiveness 10, Of the 23 most recent discussions that aren't pull requests (9 December 2025 to 6 November 2026), 11 have a reply from a mistralai member, which is some. Version policy 5, Devstral Small 1.1 stays in its own repository, which this card links as the predecessor, with no dated note on the older card that we read. File health 10, Config, template, tokenizer and card agree, and the June and July commits fixed stray tokens and brought the files in line with mistral-common. 11 + 0 + 10 + 10 + 5 + 10 = 46.",
          "b": "The card covers all seven points (20), and the training data is a general description with a January 2025 cut-off (10). The files last changed on 15 July 2026, 112 days before this check (10). Google organisation members answered 10 of the 20 discussions (10). It's a first release (5). File health gets 5 because the diffusers scheduler config disagrees with the card's sampling settings, though the files on the documented path agree."
        },
        "scores": {
          "a": 46,
          "b": 60
        }
      },
      {
        "by": 10,
        "category": "risk",
        "leads": "diffusiongemma-26b-a4b-it",
        "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 gets the full 25, safetensors only and loaded by Transformers' own code and a Jinja template. Provenance gets 15 for files in Google's organisation with a hash for every file. Licence risk keeps 15 under plain Apache 2.0. Agent guidance gets 10 for the card's list of harm categories and its advice to add content-safety safeguards. Safety evaluation scores 0 because the card states improvements without figures. The security programme gets 5 for Google's security.txt, with no public advisories for Gemma."
        },
        "scores": {
          "a": 60,
          "b": 70
        }
      },
      {
        "by": 5,
        "category": "interface",
        "leads": "diffusiongemma-26b-a4b-it",
        "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": "The chat template is in the repository (20) and handles several tool calls in one turn, and the card names function calling as trained (20). The tool-calling format gets 10, since only the template and the shipped response schema say how tools work here and Google's tool-calling guide is for Gemma 4. Structured output scores 0, undocumented and rejected by SGLang. Thinking can be turned on or off per request with the reasoning in its own channel (10), and the context is 256K (10)."
        },
        "scores": {
          "a": 65,
          "b": 70
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Mistral AI",
        "b": "Google DeepMind",
        "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": "yes",
        "name": "llms.txt"
      },
      {
        "a": "2026-07-15",
        "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"
      },
      {
        "a": "none",
        "b": "2.5/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Devstral Small 2 scores 61 (C) on agent readiness against DiffusionGemma 26B A4B IT's 51.3 (D), and leads in 1 of 4 scored categories. DiffusionGemma 26B A4B IT leads on risk, agent interface and documentation and upkeep.",
        "question": "Which is better for AI agents, Devstral Small 2 or DiffusionGemma 26B A4B IT?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 90 against 55"
        ],
        "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": [
          "Risk, 70 against 60",
          "Agent interface, 70 against 65",
          "Documentation and upkeep, 60 against 46"
        ],
        "also": null,
        "goodFor": "Agents with about 64 GB of GPU memory that want fast single-user generation from text and images and can accept lower quality than Gemma 4",
        "slug": "diffusiongemma-26b-a4b-it",
        "watchFor": "No quantised build from Google, so the smallest official build needs the 64 GB band"
      }
    ],
    "history": {
      "devstral-small-2": null,
      "diffusiongemma-26b-a4b-it": [
        {
          "causes": [
            "correction"
          ],
          "date": "2026-11-04",
          "delta": 1.3,
          "from": 50,
          "fromGrade": "D",
          "to": 51.3,
          "toGrade": "D"
        }
      ]
    },
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
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      {
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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"
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        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-diffusiongemma-26b-a4b-it.json",
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        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-diffusiongemma-26b-a4b-it"
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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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        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-devstral-small-2"
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        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-devstral-small-2"
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        "title": "DiffusionGemma 26B A4B IT vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
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      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-nemotron-3-super-120b-a12b.json",
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-nemotron-3-super-120b-a12b"
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      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-nemotron-3-ultra-550b-a55b.json",
        "title": "DiffusionGemma 26B A4B IT vs Nemotron 3 Ultra 550B A55B",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-nemotron-3-ultra-550b-a55b"
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      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-north-mini-code-1-0.json",
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-north-mini-code-1-0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-occamy-1-0.json",
        "title": "DiffusionGemma 26B A4B IT vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-occamy-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-olmo-3-7b-think.json",
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-olmo-3-7b-think"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ornith-1-5-122b.json",
        "title": "DiffusionGemma 26B A4B IT vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ornith-1-5-122b"
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      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ornith-1-5-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ornith-1-5-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ornith-1-5-397b.json",
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      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ornith-1-5-9b.json",
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ornith-1-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-otel-2-0-llm-31b-it.json",
        "title": "DiffusionGemma 26B A4B IT vs OTel-2.0-LLM-31B-IT",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-otel-2-0-llm-31b-it"
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        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen-agentworld-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-30b-a3b-instruct-2507.json",
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-30b-a3b-instruct-2507"
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      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-4b-instruct-2507.json",
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-4b-instruct-2507"
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      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-5-0-8b.json",
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-5-0-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-5-122b-a10b.json",
        "title": "DiffusionGemma 26B A4B IT vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-5-122b-a10b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-5-2b.json",
        "title": "DiffusionGemma 26B A4B IT vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-5-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-5-397b-a17b.json",
        "title": "DiffusionGemma 26B A4B IT vs Qwen3.5-397B-A17B",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-5-397b-a17b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-5-4b.json",
        "title": "DiffusionGemma 26B A4B IT vs Qwen3.5-4B",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-5-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-5-9b.json",
        "title": "DiffusionGemma 26B A4B IT vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-6-27b.json",
        "title": "DiffusionGemma 26B A4B IT vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-6-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-6-35b-a3b.json",
        "title": "DiffusionGemma 26B A4B IT vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-6-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-8-27b.json",
        "title": "DiffusionGemma 26B A4B IT vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-8-flash-next.json",
        "title": "DiffusionGemma 26B A4B IT vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "DiffusionGemma 26B A4B IT vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-coder-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-coder-next.json",
        "title": "DiffusionGemma 26B A4B IT vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "DiffusionGemma 26B A4B IT vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-omni-30b-a3b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-vl-2b-instruct.json",
        "title": "DiffusionGemma 26B A4B IT vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-vl-2b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "DiffusionGemma 26B A4B IT vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-vl-30b-a3b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-vl-32b-instruct.json",
        "title": "DiffusionGemma 26B A4B IT vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-vl-4b-instruct.json",
        "title": "DiffusionGemma 26B A4B IT vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-vl-8b-instruct.json",
        "title": "DiffusionGemma 26B A4B IT vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwopus3-8-27b-flash-v2.json",
        "title": "DiffusionGemma 26B A4B IT vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-recursive-flint.json",
        "title": "DiffusionGemma 26B A4B IT vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ripple-65b-preview.json",
        "title": "DiffusionGemma 26B A4B IT vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-sensenova-loop-9b-preview.json",
        "title": "DiffusionGemma 26B A4B IT vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-sensenova-loop-9b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-smollm3-3b.json",
        "title": "DiffusionGemma 26B A4B IT vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-spark-x2-6-4b.json",
        "title": "DiffusionGemma 26B A4B IT vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-step-3-7-flash.json",
        "title": "DiffusionGemma 26B A4B IT vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-swift-1-5-qwen3-8-27b.json",
        "title": "DiffusionGemma 26B A4B IT vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "DiffusionGemma 26B A4B IT vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-swift-1-5-qwen3-8-flash-next"
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      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-thinkingcap-qwen3-8-27b.json",
        "title": "DiffusionGemma 26B A4B IT vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-unlimited-ocr-2.json",
        "title": "DiffusionGemma 26B A4B IT vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-wevisone-vl-2b.json",
        "title": "DiffusionGemma 26B A4B IT vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-wevisone-vl-4b.json",
        "title": "DiffusionGemma 26B A4B IT vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-zaya1-8b.json",
        "title": "DiffusionGemma 26B A4B IT vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-zaya1-8b"
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    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 35,
        "devstral-small-2": 90,
        "diffusiongemma-26b-a4b-it": 55,
        "edge": "devstral-small-2",
        "key": "reach",
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 10,
        "devstral-small-2": 60,
        "diffusiongemma-26b-a4b-it": 70,
        "edge": "diffusiongemma-26b-a4b-it",
        "key": "risk",
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 5,
        "devstral-small-2": 65,
        "diffusiongemma-26b-a4b-it": 70,
        "edge": "diffusiongemma-26b-a4b-it",
        "key": "interface",
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 14,
        "devstral-small-2": 46,
        "diffusiongemma-26b-a4b-it": 60,
        "edge": "diffusiongemma-26b-a4b-it",
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "Devstral Small 2 scores 61 (C) on agent readiness against DiffusionGemma 26B A4B IT's 51.3 (D), and leads in 1 of 4 scored categories. DiffusionGemma 26B A4B IT leads on risk, agent interface and 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.",
      "diffusiongemma-26b-a4b-it": "A text diffusion model for fast low-concurrency local generation from text, images and video, with optional thinking and Gemma 4's tool format, under Apache 2.0. Google's BF16 files need about 62 GB, and it runs in Transformers, SGLang and vLLM. Google's own figures put it below Gemma 4 26B A4B, and Google labels it experimental."
    }
  },
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  "links": {
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    "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-diffusiongemma-26b-a4b-it.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/devstral-small-2-vs-diffusiongemma-26b-a4b-it.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/devstral-small-2-vs-diffusiongemma-26b-a4b-it.min.md"
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  "markdown": "Devstral Small 2 scores 61 (C) on agent readiness against DiffusionGemma 26B A4B IT's 51.3 (D), and leads in 1 of 4 scored categories. DiffusionGemma 26B A4B IT leads on risk, agent interface and 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- DiffusionGemma 26B A4B IT: grade D, 51.3/100, rank #143 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/diffusiongemma-26b-a4b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/diffusiongemma-26b-a4b-it.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 55\n\nWatch for: No limitations, safety results, languages or data on the card\n\n### DiffusionGemma 26B A4B IT (D)\n\nGood for: Agents with about 64 GB of GPU memory that want fast single-user generation from text and images and can accept lower quality than Gemma 4\n\nAhead on:\n- Risk, 70 against 60\n- Agent interface, 70 against 65\n- Documentation and upkeep, 60 against 46\n\nWatch for: No quantised build from Google, so the smallest official build needs the 64 GB band\n\n\n## Why the edge\n\n### Reach: Devstral Small 2 +35\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- DiffusionGemma 26B A4B IT (55): Memory earns 15 because the only Google build is 51.7 GB of BF16, about 62 GB with the fifth added, which needs the 64 GB band. Runtimes get 15 for Transformers from 5.11.0, SGLang from v0.5.22 and vLLM, whose releases from v0.24.0 (tag commit 28 June 2026) to v0.33.0 register DiffusionGemmaForBlockDiffusion; llama.cpp (b12114, and Ollama through its pin b11963) and LM Studio don't run it. Builds score 0 because Google publishes no quantised build, though others have published FP8, NVFP4, GGUF and 4-bit copies. Apache 2.0 with nothing added gives terms the full 15 and the ungated repository gives download the full 10. Hosted too scores 0, since no inference provider is mapped and the Gemini API doesn't serve it.\n\n### Documentation and upkeep: DiffusionGemma 26B A4B IT +14\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- DiffusionGemma 26B A4B IT (60): The card covers all seven points (20), and the training data is a general description with a January 2025 cut-off (10). The files last changed on 15 July 2026, 112 days before this check (10). Google organisation members answered 10 of the 20 discussions (10). It's a first release (5). File health gets 5 because the diffusers scheduler config disagrees with the card's sampling settings, though the files on the documented path agree.\n\n### Risk: DiffusionGemma 26B A4B IT +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- DiffusionGemma 26B A4B IT (70): File safety gets the full 25, safetensors only and loaded by Transformers' own code and a Jinja template. Provenance gets 15 for files in Google's organisation with a hash for every file. Licence risk keeps 15 under plain Apache 2.0. Agent guidance gets 10 for the card's list of harm categories and its advice to add content-safety safeguards. Safety evaluation scores 0 because the card states improvements without figures. The security programme gets 5 for Google's security.txt, with no public advisories for Gemma.\n\n### Agent interface: DiffusionGemma 26B A4B IT +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- DiffusionGemma 26B A4B IT (70): The chat template is in the repository (20) and handles several tool calls in one turn, and the card names function calling as trained (20). The tool-calling format gets 10, since only the template and the shipped response schema say how tools work here and Google's tool-calling guide is for Gemma 4. Structured output scores 0, undocumented and rejected by SGLang. Thinking can be turned on or off per request with the reasoning in its own channel (10), and the context is 256K (10).\n\n## Score by category\n\n| Category | Weight | Devstral Small 2 | DiffusionGemma 26B A4B IT | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 90 | 55 | Devstral Small 2 +35 |\n| Risk | 20% (20 this run) | 60 | 70 | DiffusionGemma 26B A4B IT +10 |\n| Agent interface | 10% (10 this run) | 65 | 70 | DiffusionGemma 26B A4B IT +5 |\n| Documentation and upkeep | 10% (10 this run) | 46 | 60 | DiffusionGemma 26B A4B IT +14 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **51.3 · D** | |\n\n## Facts side by side\n\n| Fact | Devstral Small 2 | DiffusionGemma 26B A4B IT |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Mistral AI | Google DeepMind |\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 | yes |\n| Last release | 2026-07-15 | 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| 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### DiffusionGemma 26B A4B IT\n\n- +1.3 on 4 November 2026: correction\n\n## Verdicts\n\n**Devstral Small 2.** The easy one to run: a 24B coding agent under Apache 2.0 that five runtimes carry, 15 GB at 4 bits in Ollama, with a 256K context, image input and well-documented tool calling. It's eleven months old, calls tools one at a time, has no reasoning mode, and its card says nothing on limits, safety or data.\n\n**DiffusionGemma 26B A4B IT.** A text diffusion model for fast low-concurrency local generation from text, images and video, with optional thinking and Gemma 4's tool format, under Apache 2.0. Google's BF16 files need about 62 GB, and it runs in Transformers, SGLang and vLLM. Google's own figures put it below Gemma 4 26B A4B, and Google labels it experimental.\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### DiffusionGemma 26B A4B IT\n\n1. Serve it with `sglang serve --model-path google/diffusiongemma-26B-A4B-it --dllm-algorithm Gemma4Renoise --trust-remote-code` on SGLang v0.5.22 or later, then call /v1/chat/completions\n2. In Transformers, install `transformers\u003e=5.11.0` and load `DiffusionGemmaForBlockDiffusion` with `dtype=\"auto\"`; generation_config.json already holds the recommended sampler\n3. Turn thinking on with `enable_thinking=True` in apply_chat_template or `\u003c|think|\u003e` at the start of the system prompt, read the reasoning between `\u003c|channel\u003ethought` and `\u003cchannel|\u003e`, and drop it from history before the next turn\n4. Don't rely on temperature, top_p, logit_bias or JSON Schema in SGLang, which ignores or rejects them for this model; validate JSON yourself\n5. Streaming arrives one 256-token canvas at a time, not token by token\n6. Put images before text in the prompt, and pick an image token budget of 70 to 1,120 by how much detail the task needs\n\n## Questions\n\n### Which is better for AI agents, Devstral Small 2 or DiffusionGemma 26B A4B IT?\n\nDevstral Small 2 scores 61 (C) on agent readiness against DiffusionGemma 26B A4B IT's 51.3 (D), and leads in 1 of 4 scored categories. DiffusionGemma 26B A4B IT leads on risk, agent interface and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/devstral-small-2-vs-diffusiongemma-26b-a4b-it.json, and with the fewest tokens: https://www.anchorterminal.com/compare/devstral-small-2-vs-diffusiongemma-26b-a4b-it.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"devstral-small-2\", \"b\": \"diffusiongemma-26b-a4b-it\"}`. 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