{
  "data": {
    "a": {
      "slug": "gemma-4-31b-it",
      "name": "Gemma 4 31B IT",
      "vendor": "Google DeepMind",
      "vendorUrl": "https://gemma.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Gemma 4 31B IT is Google DeepMind's open-weight dense multimodal model of 30.7B parameters, taking text and images with a 256K-token context, published on Hugging Face in BF16 with 4-bit QAT builds under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/gemma-4-31b-it",
      "markdownUrl": "https://www.anchorterminal.com/tools/gemma-4-31b-it.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/gemma-4-31b-it.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/gemma-4-31b-it.json",
      "repo": "https://huggingface.co/google/gemma-4-31B-it",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "google/gemma-4-31B-it"
        },
        {
          "registry": "huggingface",
          "name": "google/gemma-4-31B-it-qat-q4_0-gguf"
        },
        {
          "registry": "huggingface",
          "name": "google/gemma-4-31B-it-qat-w4a16-ct"
        },
        {
          "registry": "ollama",
          "name": "gemma4:31b"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository and Google's QAT repositories are not gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it yourself costs the hardware, about 69.9 GB at BF16 or 17.5 GB at Q4_0 per Google's memory table. Google also hosts it on the Gemini API's free tier, a separate listing (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/gemma-4/",
      "llmsTxt": "https://gemma.ai/llms.txt",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "multimodal",
        "reasoning",
        "tool-calling",
        "apache-2.0",
        "safetensors",
        "gguf",
        "qat",
        "self-hosted"
      ],
      "lastRelease": "2026-07-20",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 74.1,
        "grade": "BB",
        "agentReady": true,
        "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": 1239,
        "categoryRank": 1,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "capability": 61,
          "interface": 85,
          "reach": 95,
          "risk": 70,
          "upkeep": 65
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A dense multimodal cousin with documented tool calling, optional thinking and a 256K context, for general agent work and image reading on one 24 GB GPU through Google's 4-bit build. It runs in every major runtime under plain Apache 2.0. The main risk is that Google publishes no safety results and has gone quiet on the repository since July.",
        "bestFor": "An agent with a 24 GB GPU or more that wants a dense multimodal cousin with tool calling under a plain licence",
        "strengths": [
          "Google's own 17.7 GB QAT Q4_0 GGUF fits a 24 GB GPU, and Ollama's gemma4:31b is 20 GB",
          "Released support in llama.cpp, Ollama, vLLM, SGLang and Transformers",
          "Apache 2.0 with nothing added, no gate, and hosted on Google's Gemini API too",
          "Tool calling documented by Google with a response schema Transformers parses, several calls per turn",
          "Thinking that can be switched on or off per request and 262,144 tokens of native context"
        ],
        "weaknesses": [
          "No safety evaluation results published",
          "No prompt-injection guidance and no security route tied to Gemma",
          "Structured output isn't documented by Google",
          "Training data described only in general terms",
          "Files unchanged since 20 July and no Google reply in the discussions since 30 July"
        ],
        "agentNotes": [
          "On the reference machine run `llama-server -hf google/gemma-4-31B-it-qat-q4_0-gguf --hf-file gemma-4-31B_q4_0-it.gguf --jinja -c 32768`, adding gemma-4-31B-it-mmproj.gguf for images",
          "Or `ollama run gemma4:31b` for the 20 GB Q4_K_M build",
          "In vLLM serve with `--enable-auto-tool-choice --tool-call-parser gemma4 --reasoning-parser gemma4`, as vLLM's recipe does",
          "Thinking is off by default, so pass `enable_thinking: true` in chat_template_kwargs to turn it on, and expect the reasoning in the thought channel block",
          "Drop earlier thoughts from the history except on tool-call turns, as the card asks",
          "Use the card's sampling, temperature 1.0, top_p 0.95, top_k 64",
          "Validate function names and arguments before running a call, and check JSON output yourself or use the runtime's guided decoding",
          "The w4a16 build relies on Marlin INT4 kernels that vLLM marks unsupported on AMD, so use the GGUF on the reference machine"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 3.8,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "BB",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 74.1
          }
        ],
        "editorialScores": {
          "capability": 61,
          "interface": 85,
          "reach": 95,
          "risk": 70,
          "upkeep": 65
        },
        "provenanceScore": 72,
        "checked": {
          "categories": {
            "capability": "2026-10-21",
            "interface": "2026-10-21",
            "reach": "2026-10-21",
            "risk": "2026-10-21",
            "upkeep": "2026-10-21"
          },
          "last": "2026-10-21",
          "oldest": "2026-10-21",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama run gemma4:31b\n# or, with vLLM's recipe:\nvllm serve google/gemma-4-31B-it --max-model-len 32768 --enable-auto-tool-choice \\\n  --reasoning-parser gemma4 --tool-call-parser gemma4",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"gemma4:31b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/gemma-4-31b-it"
      },
      "sameCompany": [
        "embeddinggemma-2",
        "embeddinggemma-300m",
        "diffusiongemma-26b-a4b-it",
        "gemma-4-12b-it",
        "gemma-4-26b-a4b-it",
        "gemma-4-e2b-it",
        "gemma-4-e4b-it"
      ],
      "sameModel": [
        "gemini-api"
      ],
      "weights": {
        "trainedBy": "Google DeepMind",
        "builtOn": "google/gemma-4-31B",
        "params": "30.7B",
        "context": 262144,
        "goodFor": "Dense general assistant and agent model with tool calling and optional thinking, taking text, images and video frames.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "gemma-4-31B_q4_0-it",
            "url": "https://huggingface.co/google/gemma-4-31B-it-qat-q4_0-gguf/blob/main/gemma-4-31B_q4_0-it.gguf",
            "who": "publisher",
            "precision": "q4_0",
            "sizeGB": 17.7,
            "memoryGB": 24,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)",
              "LM Studio"
            ],
            "note": "Quantisation-aware trained by Google. Add gemma-4-31B-it-mmproj.gguf (1.2 GB) for image input"
          },
          {
            "name": "ollama gemma4:31b",
            "url": "https://ollama.com/library/gemma4:31b",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 20,
            "memoryGB": 24,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Q4_K_M weights with a BF16 vision projector and a Q8_0 draft model (digest 17ba34c06c80). Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant"
          },
          {
            "name": "gemma-4-31B-it-qat-w4a16-ct",
            "url": "https://huggingface.co/google/gemma-4-31B-it-qat-w4a16-ct",
            "who": "publisher",
            "precision": "w4a16",
            "sizeGB": 23.3,
            "memoryGB": 32,
            "format": "safetensors",
            "note": "Quantisation-aware trained, compressed-tensors format for vLLM. vLLM's quantisation table marks its Marlin INT4 kernels unsupported on AMD"
          },
          {
            "name": "gemma-4-31B-it",
            "url": "https://huggingface.co/google/gemma-4-31B-it",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 62.5,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          }
        ],
        "run": "llama-server -hf google/gemma-4-31B-it-qat-q4_0-gguf --hf-file gemma-4-31B_q4_0-it.gguf --jinja -c 32768",
        "runBuild": "gemma-4-31B_q4_0-it",
        "checked": "2026-10-21",
        "minMemoryGB": 24
      },
      "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-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": "diffusiongemma-26B-A4B-it",
            "url": "https://huggingface.co/google/diffusiongemma-26B-A4B-it",
            "params": "25.2B total, 3.8B active",
            "listing": "https://www.anchorterminal.com/tools/diffusiongemma-26b-a4b-it"
          },
          {
            "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 (the registrant of gemma.ai; the card names Google DeepMind as author)",
        "domain": "gemma.ai",
        "domainRegistered": "2017-12-16",
        "domainNote": "RDAP shows gemma.ai registered in 2017 and transferred on 15 April 2025, so the registration date may predate Google's use of the domain.",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://gemma.ai/models/releases/",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "RDAP names Google LLC as registrant of gemma.ai, with MarkMonitor as registrar. Google's ai.google.dev pages point to gemma.ai as the current documentation.",
          "Nothing is hosted under this listing, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is Google's Gemma releases page, which dates each release (Gemma 4 on 31 March 2026, its MTP drafters on 16 April). The Hugging Face commit history's messages also say what changed.",
          "gemma.ai/.well-known/security.txt answered 404 on 21 October 2026. www.google.com has a security.txt pointing to Google's vulnerability reward programme, which isn't linked from the card or gemma.ai."
        ],
        "score": 72
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/gemma-4-31b-it.json",
      "live": {
        "slug": "gemma-4-31b-it",
        "securityTxt": {
          "url": "https://gemma.ai/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-10-30T15:37:47.24464859Z"
        },
        "llmsTxt": {
          "url": "https://gemma.ai/llms.txt",
          "ok": true,
          "status": 200,
          "checkedAt": "2026-10-30T15:07:33.295141895Z"
        },
        "domain": {
          "domain": "gemma.ai",
          "registered": "2017-12-16",
          "source": "https://rdap.identitydigital.services/rdap/domain/gemma.ai",
          "checkedAt": "2026-10-25T14:01:21.204554738Z"
        },
        "updatedAt": "2026-10-30T15:37:47.24464859Z"
      }
    },
    "answer": "Gemma 4 31B IT scores 74.1 (BB) on agent readiness against Mistral Large 4's 54.6 (C), and leads in 3 of 5 scored categories. Mistral Large 4 leads on risk and agent interface.",
    "b": {
      "slug": "mistral-large-4",
      "name": "Mistral Large 4",
      "vendor": "Mistral AI",
      "vendorUrl": "https://mistral.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Mistral Large 4 (\"Le Chonk\") is Mistral AI's open-weight mixture-of-experts model of about 1T parameters with 52B active, taking text and images, with three reasoning efforts and a 1M-token context, published in FP8 and NVFP4.",
      "url": "https://www.anchorterminal.com/tools/mistral-large-4",
      "markdownUrl": "https://www.anchorterminal.com/tools/mistral-large-4.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/mistral-large-4.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/mistral-large-4.json",
      "repo": "https://huggingface.co/mistralai/Mistral-Large-4-1T-A52B",
      "license": "Mistral Ecosystem Open-Weight License (modified MIT)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "mistralai/Mistral-Large-4-1T-A52B"
        },
        {
          "registry": "huggingface",
          "name": "mistralai/Mistral-Large-4-1T-A52B-NVFP4"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Both repositories report gated false (checked 2026-10-30).",
      "pricing": "free",
      "pricingNotes": "Free to download. The Mistral Ecosystem Open-Weight License allows commercial use, except that a licensee or affiliate that operates a Model-as-a-Service business needs a separate commercial licence from Mistral before any commercial use; internal use and use through Mistral's own products are exempt. Mistral's API serves it as mistral-large-4 at $0.70 input, $0.07 cached input and $2.10 output per million tokens (checked 2026-10-30).",
      "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 2026-10-30).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-30"
      },
      "docsUrl": "https://huggingface.co/mistralai/Mistral-Large-4-1T-A52B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "frontier",
        "reasoning",
        "tool-calling",
        "long-context",
        "multilingual",
        "safetensors",
        "fp8",
        "nvfp4",
        "self-hosted"
      ],
      "lastRelease": "2026-10-29",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 54.6,
        "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": 1239,
        "categoryRank": 72,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "capability": 32,
          "interface": 95,
          "reach": 50,
          "risk": 75,
          "upkeep": 64
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "medium",
          "date": "2026-10-30"
        },
        "negative": 0,
        "verdict": "A frontier-sized open cousin for coding agents and defensive security, with strict JSON Schema output, parallel tool calls and three reasoning efforts. It needs a multi-GPU server, about 732 GB for the smallest 4-bit build, and vLLM and SGLang run it only from special images. The main risk is a licence that bars Model-as-a-Service businesses without a separate deal.",
        "bestFor": "Operators with an 8-GPU Blackwell or H200 node who want a frontier-sized open model for coding agents and defensive security, with a hosted twin on Mistral's API.",
        "strengths": [
          "Safetensors only, loaded by Transformers' own code with a Jinja template, in Mistral's verified organisation with a hash on every file",
          "Tool calling documented end to end with a named parser, several calls a turn and strict JSON Schema output",
          "Reasoning effort budgeted per request, low, medium or high, with the reasoning returned apart from the answer",
          "FP8 and NVFP4 builds from Mistral, and Mistral's own API serves the same model for agents without the hardware",
          "First among open-weight models on Cognition's FrontierCode 2.0 leaderboard, run in Mistral's own harness"
        ],
        "weaknesses": [
          "The smallest 4-bit build needs about 732 GB, so nothing fits a single workstation or a 128 GB machine",
          "No vLLM or SGLang release supports it yet, only Docker images tagged mistral-large-4",
          "A Model-as-a-Service business needs a separate commercial licence from Mistral before any commercial use",
          "Safety results are Mistral's own with no settings to rerun them, and no limitations, training data sources or knowledge cut-off are published",
          "The 1M context comes from YaRN scaling with no stated trained length, and thinking can't be turned off"
        ],
        "agentNotes": [
          "Serve the NVFP4 build on Blackwell with `vllm/vllm-openai:mistral-large-4 --model mistralai/Mistral-Large-4-1T-A52B-NVFP4 --tensor-parallel-size 8 --kv-cache-dtype fp8_ds_mla --tool-call-parser mistral --reasoning-parser mistral --enable-auto-tool-choice`; released vLLM doesn't load it",
          "On H200 use the FP8 build with tensor parallelism of 16 or 8 by 2 pipelines, per vLLM's recipe",
          "Pass `reasoning_effort` low, medium or high (default high); any other value makes the template raise an error, and there is no off switch",
          "Read the reasoning from `message.reasoning`; `max_completion_tokens` counts reasoning tokens too",
          "Use temperature 1.0 and top_p 0.95 at every effort, per the card",
          "Ask for JSON with `response_format` type json_schema and strict true; the template passes the schema to the model",
          "The template rejects a tool message straight after a user message and a conversation that doesn't start with a user or system message",
          "Add `--speculative-config '{\"method\": \"dspark\", \"num_speculative_tokens\": 3}'` to use the embedded draft head",
          "If you'll offer it to others as a hosted model, read the licence first; Model-as-a-Service use needs Mistral's commercial licence"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "medium",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 54.6
          }
        ],
        "editorialScores": {
          "capability": 32,
          "interface": 95,
          "reach": 50,
          "risk": 75,
          "upkeep": 64
        },
        "provenanceScore": 84,
        "checked": {
          "categories": {
            "capability": "2026-10-30",
            "interface": "2026-10-30",
            "reach": "2026-10-30",
            "risk": "2026-10-30",
            "upkeep": "2026-10-30"
          },
          "last": "2026-10-30",
          "oldest": "2026-10-30",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "# NVFP4 on 4 or 8 Blackwell GPUs, with the card's vLLM image (no vLLM release supports it yet):\ndocker run --runtime nvidia --gpus all -v ~/.cache/huggingface:/root/.cache/huggingface -p 8000:8000 --ipc=host \\\n  vllm/vllm-openai:mistral-large-4 --model mistralai/Mistral-Large-4-1T-A52B-NVFP4 \\\n  --tensor-parallel-size 8 --kv-cache-dtype fp8_ds_mla --tool-call-parser mistral \\\n  --reasoning-parser mistral --enable-auto-tool-choice --gpu-memory-utilization 0.85",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"model\": \"mistralai/Mistral-Large-4-1T-A52B-NVFP4\", \"reasoning_effort\": \"medium\", \"temperature\": 1.0, \"top_p\": 0.95,\n       \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/mistral-large-4"
      },
      "sameCompany": [
        "mistral-embeddings",
        "mistral-moderation",
        "mistral-voxtral-transcribe",
        "voxtral-mini-4b-realtime-2602",
        "mistral-ocr",
        "mistral-vibe-cli"
      ],
      "sameModel": [
        "mistral-api"
      ],
      "weights": {
        "trainedBy": "Mistral AI",
        "params": "~1T total (1.05T per Mistral's docs), 52B active",
        "context": 1048576,
        "goodFor": "Frontier-sized multimodal model for coding agents, defensive security work and long documents, with tool calling, strict JSON Schema output and three reasoning efforts, for operators with a multi-GPU server.",
        "runtimes": [
          "llama.cpp",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Mistral-Large-4-1T-A52B-NVFP4",
            "url": "https://huggingface.co/mistralai/Mistral-Large-4-1T-A52B-NVFP4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 610,
            "memoryGB": 1024,
            "format": "safetensors",
            "runsOn": [
              "vLLM (mistral-large-4 image)",
              "SGLang (mistral-large-4 image)"
            ],
            "note": "NVFP4 experts with FP8 linears, for Blackwell GPUs; Mistral-format params.json only, no config.json, so not for Transformers"
          },
          {
            "name": "ggml-org Mistral-Large-4-1T-A52B-GGUF Q4_K_M",
            "url": "https://huggingface.co/ggml-org/Mistral-Large-4-1T-A52B-GGUF",
            "who": "ggml-org (llama.cpp)",
            "precision": "q4_k_m",
            "sizeGB": 634.8,
            "memoryGB": 1024,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "llama.cpp's own organisation, linked from Mistral's card. Also BF16, Q8_0, Q2_K (380.7 GB) and IQ2_XXS (297.7 GB), plus an NVFP4 GGUF of 619.4 GB in a second repository; the 2-bit files are below the 4-bit line and aren't counted. Vision projector 3.4 GB extra"
          },
          {
            "name": "Mistral-Large-4-1T-A52B",
            "url": "https://huggingface.co/mistralai/Mistral-Large-4-1T-A52B",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 1060.6,
            "memoryGB": 2048,
            "format": "safetensors",
            "runsOn": [
              "Transformers",
              "vLLM (mistral-large-4 image)",
              "SGLang (mistral-large-4 image)"
            ],
            "note": "Block-scaled FP8 (128 by 128), the release precision; vision tower, projector, output head and some attention projections kept in BF16. Includes the DSpark draft head"
          }
        ],
        "run": "docker run --runtime nvidia --gpus all -v ~/.cache/huggingface:/root/.cache/huggingface -p 8000:8000 --ipc=host vllm/vllm-openai:mistral-large-4 --model mistralai/Mistral-Large-4-1T-A52B-NVFP4 --tensor-parallel-size 8 --kv-cache-dtype fp8_ds_mla --tool-call-parser mistral --reasoning-parser mistral --enable-auto-tool-choice --gpu-memory-utilization 0.85",
        "runBuild": "Mistral-Large-4-1T-A52B-NVFP4",
        "checked": "2026-10-30",
        "minMemoryGB": 1024
      },
      "family": {
        "lab": "Mistral AI",
        "family": "Mistral Large 4",
        "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/Mistral-Large-4-1T-A52B/commits/main",
        "securityTxt": "valid",
        "checked": "2026-10-30",
        "notes": [
          "The files sit in Mistral's verified mistralai organisation, which the card, Mistral's announcement and its docs page for the model link to. Legal entity and domain registration are carried from the mistral-api listing.",
          "Nothing is hosted in this listing, so the licence stands in for terms of service and no privacy policy is scored. The hosted model is mistral-api.",
          "The changelog is the repositories' commit history, an initial release on 29 October 2026 and two card edits the same day, plus a merged evaluation figure on the NVFP4 card.",
          "mistral.ai/.well-known/security.txt answered on 30 October 2026 with a security contact, an expiry of 5 May 2027 and a HackerOne submission policy."
        ],
        "score": 84
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/mistral-large-4.json"
    },
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Google DeepMind",
        "b": "Mistral AI",
        "name": "Vendor"
      },
      {
        "a": "no (local only)",
        "b": "no (local only)",
        "name": "Hosted endpoint"
      },
      {
        "a": "",
        "b": "",
        "name": "Transports"
      },
      {
        "a": "None",
        "b": "None",
        "name": "Auth"
      },
      {
        "a": "Free",
        "b": "Free",
        "name": "Pricing"
      },
      {
        "a": "no",
        "b": "no",
        "name": "x402"
      },
      {
        "a": "Apache-2.0",
        "b": "Mistral Ecosystem Open-Weight License (modified MIT)",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "yes",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-07-20",
        "b": "2026-10-29",
        "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": "",
        "b": "",
        "name": "Customer content may train models"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict automated access"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict benchmarking"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms or service can change without notice"
      },
      {
        "a": "",
        "b": "",
        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "3.8/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Gemma 4 31B IT scores 74.1 (BB) on agent readiness against Mistral Large 4's 54.6 (C), and leads in 3 of 5 scored categories. Mistral Large 4 leads on risk and agent interface.",
        "question": "Which is better for AI agents, Gemma 4 31B IT or Mistral Large 4?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Capability, 61 against 32",
          "Reach, 95 against 50"
        ],
        "also": [
          "Agent-ready, a grade of BB or better"
        ],
        "goodFor": "An agent with a 24 GB GPU or more that wants a dense multimodal cousin with tool calling under a plain licence",
        "slug": "gemma-4-31b-it",
        "watchFor": "No safety evaluation results published"
      },
      {
        "aheadOn": [
          "Risk, 75 against 70",
          "Agent interface, 95 against 85"
        ],
        "also": null,
        "goodFor": "Operators with an 8-GPU Blackwell or H200 node who want a frontier-sized open model for coding agents and defensive security, with a hosted twin on Mistral's API.",
        "slug": "mistral-large-4",
        "watchFor": "The smallest 4-bit build needs about 732 GB, so nothing fits a single workstation or a 128 GB machine"
      }
    ],
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-31b-it.json",
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        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-31b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-mistral-large-4.json",
        "title": "Agents-A1-4B vs Mistral Large 4",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-mistral-large-4"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-31b-it.json",
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        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-31b-it"
      },
      {
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        "title": "Apertus v1.5 8B vs Mistral Large 4",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-mistral-large-4"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-31b-it.json",
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      },
      {
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        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-mistral-large-4"
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        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-31b-it"
      },
      {
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        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-mistral-large-4"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-gemma-4-31b-it.json",
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-gemma-4-31b-it"
      },
      {
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-mistral-large-4"
      },
      {
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        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-31b-it"
      },
      {
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        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-mistral-large-4"
      },
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      },
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        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-mistral-large-4"
      },
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        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-31b-it"
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        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-mistral-large-4"
      },
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      },
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      },
      {
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-inkling-small.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-inkling-small"
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      {
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        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-kimi-k3"
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      {
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      {
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-laguna-xs-2-1.json",
        "title": "Gemma 4 31B IT vs Laguna XS 2.1",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-laguna-xs-2-1"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-lbs-27b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-lbs-27b"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-lfm2-5-2-6b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-lfm2-5-2-6b"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ling-3-0-tiny.json",
        "title": "Gemma 4 31B IT vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ling-3-0-tiny"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-mimo-v2-6-distill-qwen-9b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-mimo-v2-6-distill-qwen-9b"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minicpm-o-4-5.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minicpm5-2b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minicpm5-2b"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minimax-m2-5.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minimax-m2-5"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minimax-m2-7.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minimax-m2-7"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minimax-m3-1-flash.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-muse-glimmer-30b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-muse-glimmer-30b"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nanbeige-4-3-33b.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-5-bolt-e4b-4gb.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-5-lightning-30b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-5-lightning-30b-a3b"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-nano-30b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-nano-4b.json",
        "title": "Gemma 4 31B IT vs Nemotron 3 Nano 4B",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-super-120b-a12b.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-ultra-550b-a55b.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-occamy-1-0.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-olmo-3-7b-think.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-397b.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-9b.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-30b-a3b-instruct-2507.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-30b-a3b-instruct-2507"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-4b-instruct-2507.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-0-8b.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-2b.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-397b-a17b.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-4b.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-9b.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-6-27b.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-6-35b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-8-27b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-8-27b"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-8-flash-next.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-coder-30b-a3b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-coder-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-coder-next.json",
        "title": "Gemma 4 31B IT vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-coder-next"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Gemma 4 31B IT vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-omni-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-2b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-2b-instruct"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-30b-a3b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-32b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-32b-instruct"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-4b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-4b-instruct"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-8b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-8b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Gemma 4 31B IT vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwopus3-8-27b-flash-v2"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-recursive-flint.json",
        "title": "Gemma 4 31B IT vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-recursive-flint"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-smollm3-3b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-smollm3-3b"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-spark-x2-6-4b.json",
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-swift-1-5-qwen3-8-27b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-swift-1-5-qwen3-8-27b"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-swift-1-5-qwen3-8-flash-next.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-swift-1-5-qwen3-8-flash-next"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Gemma 4 31B IT vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-thinkingcap-qwen3-8-27b"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-unlimited-ocr-2.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-unlimited-ocr-2"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-mistral-large-4.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-mistral-large-4"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-mistral-large-4.json",
        "title": "Gemma 4 E4B IT vs Mistral Large 4",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-mistral-large-4"
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      {
        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-mistral-large-4.json",
        "title": "GLM-4.5-Air vs Mistral Large 4",
        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-mistral-large-4"
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        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-mistral-large-4.json",
        "title": "GLM-4.7-Flash vs Mistral Large 4",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-mistral-large-4"
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        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-mistral-large-4.json",
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        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-mistral-large-4.json",
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        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-mistral-large-4"
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        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-mistral-large-4"
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        "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-mistral-large-4.json",
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        "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-mistral-large-4"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-mistral-large-4.json",
        "title": "LFM2.5-2.6B vs Mistral Large 4",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-mistral-large-4"
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      {
        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-mistral-large-4.json",
        "title": "Ling-3.0-tiny vs Mistral Large 4",
        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-mistral-large-4"
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      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-mistral-large-4.json",
        "title": "MiMo-V2.6 Distill Qwen 9B vs Mistral Large 4",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-mistral-large-4"
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        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-mistral-large-4.json",
        "title": "MiniCPM-o 4.5 vs Mistral Large 4",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-mistral-large-4"
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        "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-mistral-large-4.json",
        "title": "MiniCPM5-2B vs Mistral Large 4",
        "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-mistral-large-4"
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-mistral-large-4.json",
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        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-mistral-large-4"
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-mistral-large-4.json",
        "title": "MiniMax-M2.7 vs Mistral Large 4",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-mistral-large-4"
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      {
        "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-mistral-large-4.json",
        "title": "MiniMax-M3.1-Flash vs Mistral Large 4",
        "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-mistral-large-4"
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      {
        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-muse-glimmer-30b.json",
        "title": "Mistral Large 4 vs Muse Glimmer 30B",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-muse-glimmer-30b"
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      {
        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-nanbeige-4-3-33b.json",
        "title": "Mistral Large 4 vs Nanbeige4.3-33B",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-nanbeige-4-3-33b"
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      {
        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-5-bolt-e4b-4gb.json",
        "title": "Mistral Large 4 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-5-bolt-e4b-4gb"
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        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-5-lightning-30b-a3b.json",
        "title": "Mistral Large 4 vs Nemotron 3.5 Lightning 30B A3B",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-5-lightning-30b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-nano-30b-a3b.json",
        "title": "Mistral Large 4 vs Nemotron 3 Nano 30B A3B",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-nano-30b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-nano-4b.json",
        "title": "Mistral Large 4 vs Nemotron 3 Nano 4B",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-nano-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "Mistral Large 4 vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
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        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-super-120b-a12b.json",
        "title": "Mistral Large 4 vs Nemotron 3 Super 120B A12B",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-super-120b-a12b"
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        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-ultra-550b-a55b.json",
        "title": "Mistral Large 4 vs Nemotron 3 Ultra 550B A55B",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-ultra-550b-a55b"
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        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-occamy-1-0.json",
        "title": "Mistral Large 4 vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-occamy-1-0"
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        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-olmo-3-7b-think.json",
        "title": "Mistral Large 4 vs Olmo 3 7B Think",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-olmo-3-7b-think"
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        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-ornith-1-5-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-ornith-1-5-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-ornith-1-5-397b.json",
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        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-ornith-1-5-397b"
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        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-ornith-1-5-9b.json",
        "title": "Mistral Large 4 vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-ornith-1-5-9b"
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        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-30b-a3b-instruct-2507.json",
        "title": "Mistral Large 4 vs Qwen3-30B-A3B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-30b-a3b-instruct-2507"
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      {
        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-4b-instruct-2507.json",
        "title": "Mistral Large 4 vs Qwen3-4B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-4b-instruct-2507"
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      {
        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-5-0-8b.json",
        "title": "Mistral Large 4 vs Qwen3.5-0.8B",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-5-0-8b"
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      {
        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-5-122b-a10b.json",
        "title": "Mistral Large 4 vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-5-122b-a10b"
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      {
        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-5-2b.json",
        "title": "Mistral Large 4 vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-5-2b"
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      {
        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-5-397b-a17b.json",
        "title": "Mistral Large 4 vs Qwen3.5-397B-A17B",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-5-397b-a17b"
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      {
        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-5-4b.json",
        "title": "Mistral Large 4 vs Qwen3.5-4B",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-5-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-5-9b.json",
        "title": "Mistral Large 4 vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-5-9b"
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      {
        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-6-27b.json",
        "title": "Mistral Large 4 vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-6-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-6-35b-a3b.json",
        "title": "Mistral Large 4 vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-6-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-8-27b.json",
        "title": "Mistral Large 4 vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-8-flash-next.json",
        "title": "Mistral Large 4 vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-8-flash-next"
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      {
        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "Mistral Large 4 vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-coder-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-coder-next.json",
        "title": "Mistral Large 4 vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-coder-next"
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      {
        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Mistral Large 4 vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-omni-30b-a3b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-vl-2b-instruct.json",
        "title": "Mistral Large 4 vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-vl-2b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Mistral Large 4 vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-vl-30b-a3b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-vl-32b-instruct.json",
        "title": "Mistral Large 4 vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-vl-32b-instruct"
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        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-vl-4b-instruct.json",
        "title": "Mistral Large 4 vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-vl-4b-instruct"
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        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-vl-8b-instruct.json",
        "title": "Mistral Large 4 vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-vl-8b-instruct"
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        "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"
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      {
        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-recursive-flint.json",
        "title": "Mistral Large 4 vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-recursive-flint"
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      {
        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-smollm3-3b.json",
        "title": "Mistral Large 4 vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-smollm3-3b"
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      {
        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-spark-x2-6-4b.json",
        "title": "Mistral Large 4 vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-spark-x2-6-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Mistral Large 4 vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-swift-1-5-qwen3-8-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Mistral Large 4 vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-swift-1-5-qwen3-8-flash-next"
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        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Mistral Large 4 vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-thinkingcap-qwen3-8-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-unlimited-ocr-2.json",
        "title": "Mistral Large 4 vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-unlimited-ocr-2"
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    ],
    "scores": [
      {
        "by": 29,
        "edge": "gemma-4-31b-it",
        "gemma-4-31b-it": 61,
        "key": "capability",
        "mistral-large-4": 32,
        "name": "Capability",
        "weight": 35
      },
      {
        "by": 45,
        "edge": "gemma-4-31b-it",
        "gemma-4-31b-it": 95,
        "key": "reach",
        "mistral-large-4": 50,
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 5,
        "edge": "mistral-large-4",
        "gemma-4-31b-it": 70,
        "key": "risk",
        "mistral-large-4": 75,
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 10,
        "edge": "mistral-large-4",
        "gemma-4-31b-it": 85,
        "key": "interface",
        "mistral-large-4": 95,
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 1,
        "edge": "gemma-4-31b-it",
        "gemma-4-31b-it": 65,
        "key": "upkeep",
        "mistral-large-4": 64,
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "Gemma 4 31B IT scores 74.1 (BB) on agent readiness against Mistral Large 4's 54.6 (C), and leads in 3 of 5 scored categories. Mistral Large 4 leads on risk and agent interface. Both do open-weight models you run yourself.",
    "tutorials": [],
    "verdicts": {
      "gemma-4-31b-it": "A dense multimodal cousin with documented tool calling, optional thinking and a 256K context, for general agent work and image reading on one 24 GB GPU through Google's 4-bit build. It runs in every major runtime under plain Apache 2.0. The main risk is that Google publishes no safety results and has gone quiet on the repository since July.",
      "mistral-large-4": "A frontier-sized open cousin for coding agents and defensive security, with strict JSON Schema output, parallel tool calls and three reasoning efforts. It needs a multi-GPU server, about 732 GB for the smallest 4-bit build, and vLLM and SGLang run it only from special images. The main risk is a licence that bars Model-as-a-Service businesses without a separate deal."
    }
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  "links": {
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    "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-mistral-large-4.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
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  "markdown": "Gemma 4 31B IT scores 74.1 (BB) on agent readiness against Mistral Large 4's 54.6 (C), and leads in 3 of 5 scored categories. Mistral Large 4 leads on risk and agent interface. Both do open-weight models you run yourself.\n\n- Gemma 4 31B IT: grade BB, 74.1/100, rank #2 of 117 open-weight models. Markdown https://www.anchorterminal.com/tools/gemma-4-31b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/gemma-4-31b-it.json\n- Mistral Large 4: grade C, 54.6/100, rank #92 of 117 open-weight models. Markdown https://www.anchorterminal.com/tools/mistral-large-4.md · JSON https://www.anchorterminal.com/api/v1/tools/mistral-large-4.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 3588 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Gemma 4 31B IT (BB)\n\nGood for: An agent with a 24 GB GPU or more that wants a dense multimodal cousin with tool calling under a plain licence\n\nAhead on:\n- Capability, 61 against 32\n- Reach, 95 against 50\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n\nWatch for: No safety evaluation results published\n\n### Mistral Large 4 (C)\n\nGood for: Operators with an 8-GPU Blackwell or H200 node who want a frontier-sized open model for coding agents and defensive security, with a hosted twin on Mistral's API.\n\nAhead on:\n- Risk, 75 against 70\n- Agent interface, 95 against 85\n\nWatch for: The smallest 4-bit build needs about 732 GB, so nothing fits a single workstation or a 128 GB machine\n\n\n## Score by category\n\n| Category | Weight | Gemma 4 31B IT | Mistral Large 4 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 61 | 32 | Gemma 4 31B IT +29 |\n| Reach | 25% (25 this run) | 95 | 50 | Gemma 4 31B IT +45 |\n| Risk | 20% (20 this run) | 70 | 75 | Mistral Large 4 +5 |\n| Agent interface | 10% (10 this run) | 85 | 95 | Mistral Large 4 +10 |\n| Documentation and upkeep | 10% (10 this run) | 65 | 64 | Gemma 4 31B IT +1 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **74.1 · BB** | **54.6 · C** | |\n\n## Facts side by side\n\n| Fact | Gemma 4 31B IT | Mistral Large 4 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Google DeepMind | Mistral AI |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports |  |  |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 | Mistral Ecosystem Open-Weight License (modified MIT) |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-07-20 | 2026-10-29 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models |  |  |\n| Terms restrict automated access |  |  |\n| Terms restrict benchmarking |  |  |\n| Terms or service can change without notice |  |  |\n| Arbitration or class-action waiver |  |  |\n| Agent reviews | 3.8/5 (4) | none |\n\n## Verdicts\n\n**Gemma 4 31B IT.** A dense multimodal cousin with documented tool calling, optional thinking and a 256K context, for general agent work and image reading on one 24 GB GPU through Google's 4-bit build. It runs in every major runtime under plain Apache 2.0. The main risk is that Google publishes no safety results and has gone quiet on the repository since July.\n\n**Mistral Large 4.** A frontier-sized open cousin for coding agents and defensive security, with strict JSON Schema output, parallel tool calls and three reasoning efforts. It needs a multi-GPU server, about 732 GB for the smallest 4-bit build, and vLLM and SGLang run it only from special images. The main risk is a licence that bars Model-as-a-Service businesses without a separate deal.\n\n## Before you call either\n\n### Gemma 4 31B IT\n\n1. On the reference machine run `llama-server -hf google/gemma-4-31B-it-qat-q4_0-gguf --hf-file gemma-4-31B_q4_0-it.gguf --jinja -c 32768`, adding gemma-4-31B-it-mmproj.gguf for images\n2. Or `ollama run gemma4:31b` for the 20 GB Q4_K_M build\n3. In vLLM serve with `--enable-auto-tool-choice --tool-call-parser gemma4 --reasoning-parser gemma4`, as vLLM's recipe does\n4. Thinking is off by default, so pass `enable_thinking: true` in chat_template_kwargs to turn it on, and expect the reasoning in the thought channel block\n5. Drop earlier thoughts from the history except on tool-call turns, as the card asks\n6. Use the card's sampling, temperature 1.0, top_p 0.95, top_k 64\n7. Validate function names and arguments before running a call, and check JSON output yourself or use the runtime's guided decoding\n8. The w4a16 build relies on Marlin INT4 kernels that vLLM marks unsupported on AMD, so use the GGUF on the reference machine\n\n### Mistral Large 4\n\n1. Serve the NVFP4 build on Blackwell with `vllm/vllm-openai:mistral-large-4 --model mistralai/Mistral-Large-4-1T-A52B-NVFP4 --tensor-parallel-size 8 --kv-cache-dtype fp8_ds_mla --tool-call-parser mistral --reasoning-parser mistral --enable-auto-tool-choice`; released vLLM doesn't load it\n2. On H200 use the FP8 build with tensor parallelism of 16 or 8 by 2 pipelines, per vLLM's recipe\n3. Pass `reasoning_effort` low, medium or high (default high); any other value makes the template raise an error, and there is no off switch\n4. Read the reasoning from `message.reasoning`; `max_completion_tokens` counts reasoning tokens too\n5. Use temperature 1.0 and top_p 0.95 at every effort, per the card\n6. Ask for JSON with `response_format` type json_schema and strict true; the template passes the schema to the model\n7. The template rejects a tool message straight after a user message and a conversation that doesn't start with a user or system message\n8. Add `--speculative-config '{\"method\": \"dspark\", \"num_speculative_tokens\": 3}'` to use the embedded draft head\n9. If you'll offer it to others as a hosted model, read the licence first; Model-as-a-Service use needs Mistral's commercial licence\n\n## Questions\n\n### Which is better for AI agents, Gemma 4 31B IT or Mistral Large 4?\n\nGemma 4 31B IT scores 74.1 (BB) on agent readiness against Mistral Large 4's 54.6 (C), and leads in 3 of 5 scored categories. Mistral Large 4 leads on risk and agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-mistral-large-4.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-mistral-large-4.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"gemma-4-31b-it\", \"b\": \"mistral-large-4\"}`. 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        "name": "Gemma 4 31B IT vs Mistral Large 4",
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    "description": "Gemma 4 31B IT scores 74.1 (BB) to Mistral Large 4's 54.6 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.",
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    "h1": "Gemma 4 31B IT vs Mistral Large 4",
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    "path": "/compare/gemma-4-31b-it-vs-mistral-large-4",
    "published": "2026-10-01",
    "section": "tools",
    "title": "Gemma 4 31B IT vs Mistral Large 4 for AI agents (2026)",
    "toc": null,
    "updated": "2026-10-30",
    "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-mistral-large-4"
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