{
  "data": {
    "a": {
      "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",
      "dns": "mistral-large-4.grade.anchorterminal.com",
      "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": 1350,
        "categoryRank": 73,
        "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": "2026-11-04"
        },
        "negative": 0,
        "verdict": "A 1T-parameter mixture-of-experts model from Mistral 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 no released vLLM or SGLang loads it. The licence 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",
          "A technical report in the repository since 2 November 2026 covers architecture, pretraining mix, post-training and serving"
        ],
        "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 on 4 November 2026 (v0.33.0 and v0.5.23), 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 internal runs, and no limitations, training data sources or knowledge cut-off are published",
          "The 1M context comes from YaRN scaling over a 40K pretraining 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": "2026-11-04",
            "interface": "2026-11-04",
            "reach": "2026-11-04",
            "risk": "2026-11-04",
            "upkeep": "2026-11-04"
          },
          "last": "2026-11-04",
          "oldest": "2026-11-04",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "# 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": "2026-11-04",
        "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",
      "live": {
        "slug": "mistral-large-4",
        "securityTxt": {
          "url": "https://mistral.ai/.well-known/security.txt",
          "state": "valid",
          "expires": "2027-05-05T23:59:59.000Z",
          "checkedAt": "2026-11-04T15:54:06.899510271Z"
        },
        "domain": {
          "domain": "mistral.ai",
          "registered": "2019-05-15",
          "source": "https://rdap.identitydigital.services/rdap/domain/mistral.ai",
          "checkedAt": "2026-11-01T14:21:43.304217994Z"
        },
        "pages": [
          {
            "url": "https://huggingface.co/mistralai/Mistral-Large-4-1T-A52B/commits/main",
            "kind": "changelog",
            "status": 200,
            "checkedAt": "2026-11-04T08:26:12.228252145Z",
            "changedAt": "2026-11-04T08:26:12.228252145Z",
            "fingerprint": "b8d854176f41"
          }
        ],
        "updatedAt": "2026-11-04T15:54:06.899510271Z"
      }
    },
    "answer": "Mistral Large 4 scores 54.6 (C) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in every scored category.",
    "b": {
      "slug": "ripple-65b-preview",
      "name": "Ripple-65B Preview",
      "vendor": "Ricursive Intelligence",
      "vendorUrl": "https://www.ricursive.com/ripple",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Ricursive Intelligence's 65B dense model, pretrained from scratch and post-trained for agentic coding, on Hugging Face in BF16 and NVFP4 under Apache 2.0. Its custom architecture needs Ricursive's own model code to run.",
      "url": "https://www.anchorterminal.com/tools/ripple-65b-preview",
      "markdownUrl": "https://www.anchorterminal.com/tools/ripple-65b-preview.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ripple-65b-preview.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ripple-65b-preview.json",
      "dns": "ripple-65b-preview.grade.anchorterminal.com",
      "repo": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "ricursiveAI/Ripple-65B-Preview"
        },
        {
          "registry": "huggingface",
          "name": "ricursiveAI/Ripple-65B-Preview-NVFP4"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither the BF16 nor the NVFP4 repository is gated (checked 2026-11-04).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it yourself costs the hardware, 129.6 GB for the BF16 files and 57.2 GB for the NVFP4 build by the Hub's file pages. Ricursive's launch post names Together AI, GMI Cloud and OpenRouter as hosts, but Together's model page says coming soon (checked 2026-11-04).",
      "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 2026-11-04).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-04"
      },
      "docsUrl": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "reasoning",
        "tool-calling",
        "coding",
        "apache-2.0",
        "safetensors",
        "remote-code",
        "self-hosted",
        "new"
      ],
      "lastRelease": "2026-11-04",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 53.3,
        "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": 1350,
        "categoryRank": 78,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 75,
          "reach": 45,
          "risk": 55,
          "upkeep": 49
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-04"
        },
        "negative": 0,
        "verdict": "A dense 65B coding model from a new lab, pretrained from scratch and released today under Apache 2.0, that Ricursive says leads U.S. open-weight models on Terminal-Bench 2.1. Nobody has checked that yet, and the custom architecture runs only through Ricursive's own SGLang plugin on Blackwell GPUs, with a 57 GB NVFP4 build as the smallest.",
        "bestFor": "Teams with Blackwell GPUs or a DGX Spark who want an Apache 2.0 coding model to try for terminal and agentic coding work, and can run the publisher's code to serve it.",
        "strengths": [
          "Apache 2.0 with nothing added, ungated, from the organisation Ricursive's site links to",
          "A tools-aware chat template with several calls a turn, and a server that returns reasoning, content and tool calls separately",
          "Trained for long context at 2M-token sequences, with 1M stated",
          "Base model and a speculative-decoding draft published beside the instruct model",
          "Safetensors only, with a checksum on every file"
        ],
        "weaknesses": [
          "No released runtime runs the architecture, so running it means Ricursive's Python in SGLang 0.5.19 or Transformers' remote code",
          "The smallest build is 57.2 GB NVFP4, for NVIDIA Blackwell only, and there is no GGUF",
          "No independent benchmark yet, and the top-ranking claim rests on Ricursive's own Terminal-Bench 2.1 run",
          "Thinking can't be turned off or budgeted",
          "No safety guidance for agent use, no security contact and no knowledge cut-off"
        ],
        "agentNotes": [
          "Run Ricursive's launcher from inference/server with `uv run --script serve.py` on two B200 GPUs; it listens on 127.0.0.1:30000 with a 16K context by default",
          "The launcher turns on `--reasoning-parser ripple` and `--tool-call-parser ripple`; without them tool calls come back as raw text inside tool_call tags",
          "For `tool_choice: \"required\"` set `strict: true` and `additionalProperties: false` on each function, which the README says stopped extra argument fields",
          "Leave room in `max_tokens` for reasoning as well as the answer, since thinking is always on",
          "Use chat_template.jinja, not the template inside tokenizer_config.json, which has no tool support"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "D",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 53.3
          }
        ],
        "editorialScores": {
          "interface": 75,
          "reach": 45,
          "risk": 55,
          "upkeep": 49
        },
        "provenanceScore": 36,
        "checked": {
          "categories": {
            "interface": "2026-11-04",
            "reach": "2026-11-04",
            "risk": "2026-11-04",
            "upkeep": "2026-11-04"
          },
          "last": "2026-11-04",
          "oldest": "2026-11-04",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "hf download ricursiveAI/Ripple-65B-Preview --local-dir ./ripple-65b\nhf download ricursiveAI/Ripple-65B-Preview-DFlash2 --local-dir ./ripple-dflash2\ncd ripple-65b/inference/server\nCUDA_VISIBLE_DEVICES=0,1 uv run --script serve.py \\\n  --model-path ../.. \\\n  --speculative-draft-model-path ../../../ripple-dflash2",
        "http": "curl http://127.0.0.1:30000/v1/chat/completions \\\n  -H 'Content-Type: application/json' \\\n  -d '{\"model\": \"ripple\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello\"}], \"max_tokens\": 128}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/ripple-65b-preview"
      },
      "weights": {
        "trainedBy": "Ricursive Intelligence",
        "params": "65B",
        "context": 1048576,
        "goodFor": "A dense 65B model post-trained for agentic coding and terminal work, with several tool calls a turn and a long context, for teams with Blackwell GPUs or a DGX Spark.",
        "runtimes": [],
        "builds": [
          {
            "name": "Ripple-65B-Preview-NVFP4",
            "url": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview-NVFP4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 57.2,
            "memoryGB": 69,
            "format": "safetensors",
            "note": "NVFP4 MLP weights and activations with BF16 attention, 6 shards summed from the Hub's file page. For NVIDIA Blackwell, with Ricursive's DGX Spark loader. The checkpoint was replaced in place on release day"
          },
          {
            "name": "Ripple-65B-Preview",
            "url": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 129.6,
            "memoryGB": 160,
            "format": "safetensors",
            "note": "16 shards summed from the Hub's file page. Served with Ricursive's SGLang 0.5.19 plugin on two B200 GPUs"
          }
        ],
        "run": "CUDA_VISIBLE_DEVICES=0,1 uv run --script serve.py --model-path /path/to/Ripple-65B-Preview --speculative-draft-model-path /path/to/Ripple-65B-Preview-DFlash2",
        "runBuild": "Ripple-65B-Preview",
        "checked": "2026-11-04",
        "minMemoryGB": 69
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Ricursive Intelligence, Inc.",
        "domain": "ricursive.com",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview/blob/main/LICENSE",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-11-04",
        "notes": [
          "Every page on www.ricursive.com ends with \"© 2026 Ricursive Intelligence, Inc.\". The site links the Hugging Face organisation ricursiveAI (fullname Ricursive Intelligence), which publishes the four Ripple repositories. RDAP wasn't queried, by the run's rules.",
          "The Apache 2.0 LICENSE in the repository governs the weights and stands in for terms of service. The site's own legal pages (/legal and /legal/terms, titled Model Attribution Terms) render their text by script only and couldn't be read.",
          "Nothing is hosted by Ricursive, so no privacy policy or status page is scored.",
          "No changelog. The repositories' commit messages are uploads and one in-place swap of the NVFP4 checkpoint on release day.",
          "www.ricursive.com/.well-known/security.txt is 404 on 4 November 2026, and the only contact on the card and site is a general one."
        ],
        "score": 36
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/ripple-65b-preview.json"
    },
    "edges": [
      {
        "by": 20,
        "category": "risk",
        "leads": "mistral-large-4",
        "name": "Risk",
        "reasons": {
          "a": "File safety scores 25, safetensors only and loaded by Transformers' own code with a Jinja template. Provenance scores 15, Mistral's verified organisation with a hash on every file. Safety evaluation scores 10, refusal and prompt-injection results Mistral ran itself, listed in its 2 November technical report as internal, and red-teaming by outside firms with nothing published. Agent guidance scores 10 for a statement of what the refusals cover and a claim of trained injection resistance, with only general safeguards. Licence risk scores 10, 5 off for the Model-as-a-Service limit. The security programme scores 5 for security.txt with no public advisories. 25 + 15 + 10 + 10 + 10 + 5 = 75.",
          "b": "File safety 15, safetensors only, less 10 because the documented way to run it loads Ricursive's Python. Provenance 15, the files are in the organisation ricursive.com links to, with a checksum on every file. Safety evaluation 10, Ricursive's own FORTRESS and StrongREJECT results. Agent guidance 0, nothing on prompt injection, refusals or safeguards. Licence risk 15, Apache 2.0. Security programme 0, no way to report a flaw."
        },
        "scores": {
          "a": 75,
          "b": 55
        }
      },
      {
        "by": 20,
        "category": "interface",
        "leads": "mistral-large-4",
        "name": "Agent interface",
        "reasons": {
          "a": "The chat template is in the repository (20). The card documents how tools are declared and come back and names the parser (25), and the template renders several calls in one turn (20). Strict JSON Schema output is documented with how to ask for it (15). Reasoning effort is budgeted per request with the reasoning returned apart from the answer (10). Context scores 5, because the 1M window comes from YaRN scaling and the technical report gives 40K as the longest pretraining sequence. 20 + 25 + 20 + 15 + 10 + 5 = 95.",
          "b": "Chat template 20, chat_template.jinja in the repository. Tool-calling format 25, Ricursive's server README says how tools are declared and calls come back, with the ripple parser in SGLang 0.5.19. Trained tool calling 20, one or more calls a turn and agentic-coding RL. Structured output 0, not documented. Reasoning control 0, the template has no thinking switch or effort levels. Context 10, long-context training at 2M-token sequences for a stated 1M."
        },
        "scores": {
          "a": 95,
          "b": 75
        }
      },
      {
        "by": 15,
        "category": "upkeep",
        "leads": "mistral-large-4",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "The card covers intended use, architecture, parameters, context and languages, and misses limitations and a cut-off (14). Training data is described by its mix in the technical report, with no sources, cut-off or filtering (5). The files changed on 2 November 2026, when the report was added (20). A Mistral member replied in three of nine discussions (10). It's a first release with older Large models in their own repositories and no dated note (5), and the files agree with each other and the card (10). 14 + 5 + 20 + 10 + 5 + 10 = 64.",
          "b": "Model card 14 of 20, no limitations and no knowledge cut-off. Training data 5, a general description with token counts and no cut-off. Last change 20, released today. Responsiveness 0, no discussions yet, read as none answered. Version policy 5, a first release, with the NVFP4 checkpoint replaced in place on release day under a commit note. File health 5, tokenizer_config.json carries a second, tool-less chat template and a 131,072-token limit against the card's 1M."
        },
        "scores": {
          "a": 64,
          "b": 49
        }
      },
      {
        "by": 5,
        "category": "reach",
        "leads": "mistral-large-4",
        "name": "Reach",
        "reasons": {
          "a": "Memory scores 0, since the smallest 4-bit build, Mistral's NVFP4, is 610 GB, about 732 GB with a fifth added, far above 256 GB. Runtimes score 10, released Transformers and llama.cpp have the architecture, while vLLM v0.33.0 and SGLang v0.5.23 still lack it, the llama.cpp build Ollama 0.40.14 pins predates it, and LM Studio has no entry. Builds score 15 for Mistral's FP8 and NVFP4 releases. Terms score 10, a modified MIT licence that is free commercially except for Model-as-a-Service businesses. Download scores 10 with no gate, and hosted too scores 5 because Mistral's API serves these weights. 0 + 10 + 15 + 10 + 10 + 5 = 50.",
          "b": "Memory 10 of 30, the smallest 4-bit build is Ricursive's NVFP4 at 57.2 GB, 68.7 GB with a fifth added, which is over 64 GB and fits our reference machine. Runtimes 0, no released runtime runs the ricursive architecture with its own code; Ricursive's SGLang plugin and Transformers' remote code don't count. Builds 10, one quantised precision (NVFP4) from Ricursive. Terms 15, Apache 2.0 with nothing added. Download 10, not gated. Hosted 0, Ricursive serves no API, Hugging Face maps no provider, Together AI's page says coming soon, and GMI Cloud's and OpenRouter's pages weren't read."
        },
        "scores": {
          "a": 50,
          "b": 45
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Mistral AI",
        "b": "Ricursive Intelligence",
        "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": "Mistral Ecosystem Open-Weight License (modified MIT)",
        "b": "Apache-2.0",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-10-29",
        "b": "2026-11-04",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "not read yet",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      }
    ],
    "faq": [
      {
        "answer": "Mistral Large 4 scores 54.6 (C) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in every scored category.",
        "question": "Which is better for AI agents, Mistral Large 4 or Ripple-65B Preview?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 50 against 45",
          "Risk, 75 against 55",
          "Agent interface, 95 against 75",
          "Documentation and upkeep, 64 against 49"
        ],
        "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"
      },
      {
        "aheadOn": null,
        "also": null,
        "goodFor": "Teams with Blackwell GPUs or a DGX Spark who want an Apache 2.0 coding model to try for terminal and agentic coding work, and can run the publisher's code to serve it.",
        "slug": "ripple-65b-preview",
        "watchFor": "No released runtime runs the architecture, so running it means Ricursive's Python in SGLang 0.5.19 or Transformers' remote code"
      }
    ],
    "history": null,
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-mistral-large-4.json",
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      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ripple-65b-preview.json",
        "title": "Agents-A1-4B vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-mistral-large-4.json",
        "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/apertus-v1-5-8b-vs-ripple-65b-preview.json",
        "title": "Apertus v1.5 8B vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-mistral-large-4.json",
        "title": "Apodex-1.1-mini vs Mistral Large 4",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-mistral-large-4"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ripple-65b-preview.json",
        "title": "Apodex-1.1-mini vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-mistral-large-4.json",
        "title": "BigBang-v1 vs Mistral Large 4",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-mistral-large-4"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ripple-65b-preview.json",
        "title": "BigBang-v1 vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-mistral-large-4.json",
        "title": "Carbon-X vs Mistral Large 4",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-mistral-large-4"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-ripple-65b-preview.json",
        "title": "Carbon-X vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-mistral-large-4.json",
        "title": "Cosmos-Reason2-2B vs Mistral Large 4",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-mistral-large-4"
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      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ripple-65b-preview.json",
        "title": "Cosmos-Reason2-2B vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-mistral-large-4.json",
        "title": "Darwin-36B-Opus vs Mistral Large 4",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-mistral-large-4"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ripple-65b-preview.json",
        "title": "Darwin-36B-Opus vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-mistral-large-4.json",
        "title": "DeepSeek-V3.2 vs Mistral Large 4",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-mistral-large-4"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ripple-65b-preview.json",
        "title": "DeepSeek-V3.2 vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-mistral-large-4.json",
        "title": "DeepSeek V4.1 Flash vs Mistral Large 4",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-mistral-large-4"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ripple-65b-preview.json",
        "title": "DeepSeek V4.1 Flash vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-mistral-large-4.json",
        "title": "DeepSeek-V4-Flash-0731 vs Mistral Large 4",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-mistral-large-4"
      },
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-mistral-large-4.json",
        "title": "DiffusionGemma 26B A4B IT vs Mistral Large 4",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-mistral-large-4"
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        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-mistral-large-4.json",
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        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-ripple-65b-preview.json",
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        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-ripple-65b-preview.json",
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        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-ripple-65b-preview.json",
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-ripple-65b-preview.json",
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        "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-ripple-65b-preview.json",
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        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-ripple-65b-preview.json",
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        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-coder-next.json",
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    "panel": null,
    "scores": [
      {
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        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 5,
        "edge": "mistral-large-4",
        "key": "reach",
        "mistral-large-4": 50,
        "name": "Reach",
        "ripple-65b-preview": 45,
        "weight": 25
      },
      {
        "by": 20,
        "edge": "mistral-large-4",
        "key": "risk",
        "mistral-large-4": 75,
        "name": "Risk",
        "ripple-65b-preview": 55,
        "weight": 20
      },
      {
        "by": 20,
        "edge": "mistral-large-4",
        "key": "interface",
        "mistral-large-4": 95,
        "name": "Agent interface",
        "ripple-65b-preview": 75,
        "weight": 10
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      {
        "by": 15,
        "edge": "mistral-large-4",
        "key": "upkeep",
        "mistral-large-4": 64,
        "name": "Documentation and upkeep",
        "ripple-65b-preview": 49,
        "weight": 10
      }
    ],
    "summary": "Mistral Large 4 scores 54.6 (C) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in every scored category. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "mistral-large-4": "A 1T-parameter mixture-of-experts model from Mistral 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 no released vLLM or SGLang loads it. The licence bars Model-as-a-Service businesses without a separate deal.",
      "ripple-65b-preview": "A dense 65B coding model from a new lab, pretrained from scratch and released today under Apache 2.0, that Ricursive says leads U.S. open-weight models on Terminal-Bench 2.1. Nobody has checked that yet, and the custom architecture runs only through Ricursive's own SGLang plugin on Blackwell GPUs, with a 57 GB NVFP4 build as the smallest."
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  "markdown": "Mistral Large 4 scores 54.6 (C) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in every scored category. Both do open-weight models you run yourself.\n\n- Mistral Large 4: grade C, 54.6/100, rank #97 of 121 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- Ripple-65B Preview: grade D, 53.3/100, rank #103 of 121 open-weight models. Markdown https://www.anchorterminal.com/tools/ripple-65b-preview.md · JSON https://www.anchorterminal.com/api/v1/tools/ripple-65b-preview.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 3674 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\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- Reach, 50 against 45\n- Risk, 75 against 55\n- Agent interface, 95 against 75\n- Documentation and upkeep, 64 against 49\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### Ripple-65B Preview (D)\n\nGood for: Teams with Blackwell GPUs or a DGX Spark who want an Apache 2.0 coding model to try for terminal and agentic coding work, and can run the publisher's code to serve it.\n\nWatch for: No released runtime runs the architecture, so running it means Ricursive's Python in SGLang 0.5.19 or Transformers' remote code\n\n\n## Why the edge\n\n### Risk: Mistral Large 4 +20\n\n- Mistral Large 4 (75): File safety scores 25, safetensors only and loaded by Transformers' own code with a Jinja template. Provenance scores 15, Mistral's verified organisation with a hash on every file. Safety evaluation scores 10, refusal and prompt-injection results Mistral ran itself, listed in its 2 November technical report as internal, and red-teaming by outside firms with nothing published. Agent guidance scores 10 for a statement of what the refusals cover and a claim of trained injection resistance, with only general safeguards. Licence risk scores 10, 5 off for the Model-as-a-Service limit. The security programme scores 5 for security.txt with no public advisories. 25 + 15 + 10 + 10 + 10 + 5 = 75.\n- Ripple-65B Preview (55): File safety 15, safetensors only, less 10 because the documented way to run it loads Ricursive's Python. Provenance 15, the files are in the organisation ricursive.com links to, with a checksum on every file. Safety evaluation 10, Ricursive's own FORTRESS and StrongREJECT results. Agent guidance 0, nothing on prompt injection, refusals or safeguards. Licence risk 15, Apache 2.0. Security programme 0, no way to report a flaw.\n\n### Agent interface: Mistral Large 4 +20\n\n- Mistral Large 4 (95): The chat template is in the repository (20). The card documents how tools are declared and come back and names the parser (25), and the template renders several calls in one turn (20). Strict JSON Schema output is documented with how to ask for it (15). Reasoning effort is budgeted per request with the reasoning returned apart from the answer (10). Context scores 5, because the 1M window comes from YaRN scaling and the technical report gives 40K as the longest pretraining sequence. 20 + 25 + 20 + 15 + 10 + 5 = 95.\n- Ripple-65B Preview (75): Chat template 20, chat_template.jinja in the repository. Tool-calling format 25, Ricursive's server README says how tools are declared and calls come back, with the ripple parser in SGLang 0.5.19. Trained tool calling 20, one or more calls a turn and agentic-coding RL. Structured output 0, not documented. Reasoning control 0, the template has no thinking switch or effort levels. Context 10, long-context training at 2M-token sequences for a stated 1M.\n\n### Documentation and upkeep: Mistral Large 4 +15\n\n- Mistral Large 4 (64): The card covers intended use, architecture, parameters, context and languages, and misses limitations and a cut-off (14). Training data is described by its mix in the technical report, with no sources, cut-off or filtering (5). The files changed on 2 November 2026, when the report was added (20). A Mistral member replied in three of nine discussions (10). It's a first release with older Large models in their own repositories and no dated note (5), and the files agree with each other and the card (10). 14 + 5 + 20 + 10 + 5 + 10 = 64.\n- Ripple-65B Preview (49): Model card 14 of 20, no limitations and no knowledge cut-off. Training data 5, a general description with token counts and no cut-off. Last change 20, released today. Responsiveness 0, no discussions yet, read as none answered. Version policy 5, a first release, with the NVFP4 checkpoint replaced in place on release day under a commit note. File health 5, tokenizer_config.json carries a second, tool-less chat template and a 131,072-token limit against the card's 1M.\n\n### Reach: Mistral Large 4 +5\n\n- Mistral Large 4 (50): Memory scores 0, since the smallest 4-bit build, Mistral's NVFP4, is 610 GB, about 732 GB with a fifth added, far above 256 GB. Runtimes score 10, released Transformers and llama.cpp have the architecture, while vLLM v0.33.0 and SGLang v0.5.23 still lack it, the llama.cpp build Ollama 0.40.14 pins predates it, and LM Studio has no entry. Builds score 15 for Mistral's FP8 and NVFP4 releases. Terms score 10, a modified MIT licence that is free commercially except for Model-as-a-Service businesses. Download scores 10 with no gate, and hosted too scores 5 because Mistral's API serves these weights. 0 + 10 + 15 + 10 + 10 + 5 = 50.\n- Ripple-65B Preview (45): Memory 10 of 30, the smallest 4-bit build is Ricursive's NVFP4 at 57.2 GB, 68.7 GB with a fifth added, which is over 64 GB and fits our reference machine. Runtimes 0, no released runtime runs the ricursive architecture with its own code; Ricursive's SGLang plugin and Transformers' remote code don't count. Builds 10, one quantised precision (NVFP4) from Ricursive. Terms 15, Apache 2.0 with nothing added. Download 10, not gated. Hosted 0, Ricursive serves no API, Hugging Face maps no provider, Together AI's page says coming soon, and GMI Cloud's and OpenRouter's pages weren't read.\n\n## Score by category\n\n| Category | Weight | Mistral Large 4 | Ripple-65B Preview | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 50 | 45 | Mistral Large 4 +5 |\n| Risk | 20% (20 this run) | 75 | 55 | Mistral Large 4 +20 |\n| Agent interface | 10% (10 this run) | 95 | 75 | Mistral Large 4 +20 |\n| Documentation and upkeep | 10% (10 this run) | 64 | 49 | Mistral Large 4 +15 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **54.6 · C** | **53.3 · D** | |\n\n## Facts side by side\n\n| Fact | Mistral Large 4 | Ripple-65B Preview |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Mistral AI | Ricursive Intelligence |\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 | Mistral Ecosystem Open-Weight License (modified MIT) | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-29 | 2026-11-04 |\n| Terms last updated | no document linked | not read yet |\n| Privacy policy last updated | no document linked | no document linked |\n\n## Verdicts\n\n**Mistral Large 4.** A 1T-parameter mixture-of-experts model from Mistral 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 no released vLLM or SGLang loads it. The licence bars Model-as-a-Service businesses without a separate deal.\n\n**Ripple-65B Preview.** A dense 65B coding model from a new lab, pretrained from scratch and released today under Apache 2.0, that Ricursive says leads U.S. open-weight models on Terminal-Bench 2.1. Nobody has checked that yet, and the custom architecture runs only through Ricursive's own SGLang plugin on Blackwell GPUs, with a 57 GB NVFP4 build as the smallest.\n\n## Before you call either\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### Ripple-65B Preview\n\n1. Run Ricursive's launcher from inference/server with `uv run --script serve.py` on two B200 GPUs; it listens on 127.0.0.1:30000 with a 16K context by default\n2. The launcher turns on `--reasoning-parser ripple` and `--tool-call-parser ripple`; without them tool calls come back as raw text inside tool_call tags\n3. For `tool_choice: \"required\"` set `strict: true` and `additionalProperties: false` on each function, which the README says stopped extra argument fields\n4. Leave room in `max_tokens` for reasoning as well as the answer, since thinking is always on\n5. Use chat_template.jinja, not the template inside tokenizer_config.json, which has no tool support\n\n## Questions\n\n### Which is better for AI agents, Mistral Large 4 or Ripple-65B Preview?\n\nMistral Large 4 scores 54.6 (C) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in every scored category.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/mistral-large-4-vs-ripple-65b-preview.json, and with the fewest tokens: https://www.anchorterminal.com/compare/mistral-large-4-vs-ripple-65b-preview.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"mistral-large-4\", \"b\": \"ripple-65b-preview\"}`. From a terminal: `anchor compare mistral-large-4 ripple-65b-preview`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/mistral-large-4.json and https://www.anchorterminal.com/api/v1/tools/ripple-65b-preview.json\n\n## Other comparisons with Mistral Large 4 or Ripple-65B Preview\n\n- [Agents-A1-4B vs Mistral Large 4](https://www.anchorterminal.com/compare/agents-a1-4b-vs-mistral-large-4.md)\n- [Agents-A1-4B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/agents-a1-4b-vs-ripple-65b-preview.md)\n- [Apertus v1.5 8B vs Mistral Large 4](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-mistral-large-4.md)\n- [Apertus v1.5 8B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ripple-65b-preview.md)\n- [Apodex-1.1-mini vs Mistral Large 4](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-mistral-large-4.md)\n- [Apodex-1.1-mini vs Ripple-65B Preview](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ripple-65b-preview.md)\n- [BigBang-v1 vs Mistral Large 4](https://www.anchorterminal.com/compare/bigbang-v1-vs-mistral-large-4.md)\n- [BigBang-v1 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/bigbang-v1-vs-ripple-65b-preview.md)\n- [Carbon-X vs Mistral Large 4](https://www.anchorterminal.com/compare/carbon-x-vs-mistral-large-4.md)\n- [Carbon-X vs Ripple-65B Preview](https://www.anchorterminal.com/compare/carbon-x-vs-ripple-65b-preview.md)\n- [Cosmos-Reason2-2B vs Mistral Large 4](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-mistral-large-4.md)\n- [Cosmos-Reason2-2B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ripple-65b-preview.md)\n- [Darwin-36B-Opus vs Mistral Large 4](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-mistral-large-4.md)\n- [Darwin-36B-Opus vs Ripple-65B 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