{
  "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": 1518,
        "categoryRank": 95,
        "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",
        "devstral-small-2"
      ],
      "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": "2026-11-10T16:09:22.417413189Z"
        },
        "domain": {
          "domain": "mistral.ai",
          "registered": "2019-05-15",
          "source": "https://rdap.identitydigital.services/rdap/domain/mistral.ai",
          "checkedAt": "2026-11-08T14:56:43.249208963Z"
        },
        "pages": [
          {
            "url": "https://huggingface.co/mistralai/Mistral-Large-4-1T-A52B/commits/main",
            "kind": "changelog",
            "status": 200,
            "checkedAt": "2026-11-10T16:23:10.57278799Z",
            "changedAt": "2026-11-10T16:23:10.57278799Z",
            "fingerprint": "08ae42b78905"
          }
        ],
        "updatedAt": "2026-11-10T16:23:10.57278799Z"
      }
    },
    "answer": "North Mini Code 1.0 scores 61 (C) on agent readiness against Mistral Large 4's 54.6 (C), and leads in 1 of 4 scored categories. Mistral Large 4 leads on risk, agent interface and documentation and upkeep.",
    "b": {
      "slug": "north-mini-code-1-0",
      "name": "North Mini Code 1.0",
      "vendor": "Cohere",
      "vendorUrl": "https://cohere.com",
      "kind": "weights",
      "category": "open-weights",
      "summary": "North Mini Code 1.0 is Cohere's open-weight coding agent model, a mixture of experts of 30B parameters with 3B active, trained for tool use, with interleaved thinking and a 256K context. Apache 2.0. Cohere's 4-bit build is 19 GB.",
      "url": "https://www.anchorterminal.com/tools/north-mini-code-1-0",
      "markdownUrl": "https://www.anchorterminal.com/tools/north-mini-code-1-0.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/north-mini-code-1-0.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/north-mini-code-1-0.json",
      "dns": "north-mini-code-1-0.grade.anchorterminal.com",
      "repo": "https://huggingface.co/CohereLabs/North-Mini-Code-1.0",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "CohereLabs/North-Mini-Code-1.0"
        },
        {
          "registry": "huggingface",
          "name": "CohereLabs/North-Mini-Code-1.0-fp8"
        },
        {
          "registry": "huggingface",
          "name": "CohereLabs/North-Mini-Code-1.0-w4a16"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of the repositories is gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use under Apache 2.0. The card calls it a research release. Running it yourself takes 19.4 GB at 4 bits (w4a16), 32.1 GB in FP8 or 61.0 GB in BF16 (checked 2026-11-10).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-10"
      },
      "docsUrl": "https://huggingface.co/CohereLabs/North-Mini-Code-1.0",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "coding",
        "agentic",
        "reasoning",
        "tool-calling",
        "long-context",
        "apache-2.0",
        "safetensors",
        "fp8",
        "4-bit",
        "self-hosted"
      ],
      "lastRelease": "2026-07-29",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 61,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 63,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 85,
          "reach": 85,
          "risk": 60,
          "upkeep": 36
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "A coding agent model that fits one 24 GB GPU at 4 bits, trained for tool use, with a 256K context, Apache 2.0 and four released runtimes. It's a research release and reads like one: nothing on limitations, safety or data, the card's run instructions are out of date, and Cohere hasn't answered a question on the repository.",
        "bestFor": "Developers who want a coding agent on their own GPU with a permissive licence",
        "strengths": [
          "Trained for tool use in coding agents, with parsers in released vLLM",
          "19 GB at 4 bits from Cohere, and a 256K context",
          "Apache 2.0, ungated",
          "Four released runtimes carry the architecture",
          "Tool use documented with a worked example"
        ],
        "weaknesses": [
          "No limitations, safety results, languages or data description on the card",
          "No reply from Cohere on the repository's discussions",
          "The card's instructions still point at unreleased vLLM and Transformers",
          "No GGUF from Cohere",
          "Text only",
          "No hosted API named"
        ],
        "agentNotes": [
          "Serve it with vLLM 0.33.0 or later and `--tool-call-parser cohere_command4 --reasoning-parser cohere_command4 --enable-auto-tool-choice`, with `cohere_melody` installed",
          "Pass the model's thinking back with its tool calls on every turn. The card says results drop without it",
          "Describe tools in JSON schema and append tool results as a dictionary with the tool role",
          "Use temperature 1.0 and top_p 0.95",
          "For llama.cpp the GGUF files are Unsloth's or bartowski's conversions, not Cohere's",
          "It's text only. Don't send images"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "interface": 85,
          "reach": 85,
          "risk": 60,
          "upkeep": 36
        },
        "provenanceScore": 75,
        "checked": {
          "categories": {
            "interface": "2026-11-10",
            "reach": "2026-11-10",
            "risk": "2026-11-10",
            "upkeep": "2026-11-10"
          },
          "last": "2026-11-10",
          "oldest": "2026-11-10",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "pip install \"vllm\u003e=0.33.0\" \"cohere_melody\u003e=0.9.0\"\nvllm serve CohereLabs/North-Mini-Code-1.0 -tp 2 --max-model-len 320000 --tool-call-parser cohere_command4 --reasoning-parser cohere_command4 --enable-auto-tool-choice",
        "http": "# vLLM serves an OpenAI-compatible API at http://localhost:8000/v1. Use temperature 1.0 and top_p 0.95,\n# and send the model's earlier thinking back with each turn"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/north-mini-code-1-0"
      },
      "sameCompany": [
        "cohere-chat",
        "cohere-embed",
        "cohere-transcribe-03-2026",
        "cohere-north"
      ],
      "weights": {
        "trainedBy": "Cohere and Cohere Labs",
        "params": "30B",
        "active": "3B",
        "context": 256000,
        "goodFor": "A local coding agent: repository work, terminal tasks and long tool-calling sessions, on one 24 GB GPU at 4 bits.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "North-Mini-Code-1.0-w4a16",
            "url": "https://huggingface.co/CohereLabs/North-Mini-Code-1.0-w4a16",
            "who": "publisher",
            "precision": "w4a16",
            "sizeGB": 19.38,
            "memoryGB": 24,
            "format": "safetensors",
            "runsOn": [
              "vLLM"
            ],
            "note": "Cohere's 4-bit weight build"
          },
          {
            "name": "unsloth North-Mini-Code-1.0-GGUF (UD-Q4_K_M)",
            "url": "https://huggingface.co/unsloth/North-Mini-Code-1.0-GGUF",
            "who": "unsloth",
            "precision": "q4_k_m",
            "sizeGB": 19.2,
            "memoryGB": 24,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From Unsloth, a third party's conversion and not Cohere's. 19.20 GB of UD-Q4_K_M weights, from the Hub's model information on 10 November 2026. Reach doesn't count it"
          },
          {
            "name": "North-Mini-Code-1.0-fp8",
            "url": "https://huggingface.co/CohereLabs/North-Mini-Code-1.0-fp8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 32.06,
            "memoryGB": 48,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang"
            ],
            "note": "Cohere's FP8 build"
          },
          {
            "name": "unsloth North-Mini-Code-1.0-GGUF (Q8_0)",
            "url": "https://huggingface.co/unsloth/North-Mini-Code-1.0-GGUF",
            "who": "unsloth",
            "precision": "q8_0",
            "sizeGB": 32.44,
            "memoryGB": 48,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From Unsloth, a third party's conversion and not Cohere's. 32.44 GB of Q8_0 weights, from the Hub's model information on 10 November 2026. Reach doesn't count it"
          },
          {
            "name": "North-Mini-Code-1.0 (BF16 safetensors)",
            "url": "https://huggingface.co/CohereLabs/North-Mini-Code-1.0",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 60.97,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang",
              "Transformers"
            ],
            "note": "49 files"
          }
        ],
        "run": "vllm serve CohereLabs/North-Mini-Code-1.0-w4a16 --tool-call-parser cohere_command4 --reasoning-parser cohere_command4 --enable-auto-tool-choice",
        "runBuild": "North-Mini-Code-1.0-w4a16",
        "checked": "2026-11-10",
        "minMemoryGB": 24
      },
      "family": {
        "lab": "Cohere (Cohere Labs)",
        "family": "North Mini Code",
        "url": "https://www.anchorterminal.com/open-weights/#cohere",
        "olderCount": 29
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Cohere Inc.",
        "domain": "cohere.com",
        "domainRegistered": "2000-03-07",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/CohereLabs/North-Mini-Code-1.0/commits/main",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "The card says Developed by Cohere and Cohere Labs. Legal entity, domain and security facts are carried from our other Cohere listings.",
          "Nothing is hosted under this listing, so Apache 2.0 stands in for terms of service and no privacy policy is scored.",
          "The changelog is the commit history, 8 commits from 5 to 15 June 2026."
        ],
        "score": 75
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/north-mini-code-1-0.json"
    },
    "edges": [
      {
        "by": 35,
        "category": "reach",
        "leads": "north-mini-code-1-0",
        "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 25, the smallest build from Cohere is the w4a16 repository, 19.38 GB, and 19.38 GB × 1.2 = 23.3 GB, within 24 GB. Unsloth's GGUF isn't read for this line. Runtimes 20, Released vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 have the cohere2_moe model, and llama.cpp b12242 has the cohere2moe architecture. Builds 15, Quantised builds from Cohere at two precisions, FP8 and 4-bit weights (w4a16), each in its own repository. Terms 15, Apache 2.0 in the metadata and on the card, an OSI licence with nothing added. Download 10, the Hub API reports gated false for the model and its builds on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 25 + 20 + 15 + 15 + 10 + 0 = 85."
        },
        "scores": {
          "a": 50,
          "b": 85
        }
      },
      {
        "by": 28,
        "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 11, Intended use, the architecture, the parameters and the context are on the card. Training data 0, the card gives the training method and nothing about the data. Last change 10, the main repository last changed on 15 June 2026 (outdated chat templates removed), 148 days before this check, and the draft model's repository on 29 July, 104 days. Responsiveness 0, none of the 11 most recent discussions that aren't pull requests (7 June to 6 November 2026) has a reply from a CohereLabs member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, A first release, with each build in its own repository. File health 10, Config, template and card agree on the architecture, and the June commit that removed broken templates says so. 11 + 0 + 10 + 0 + 5 + 10 = 36."
        },
        "scores": {
          "a": 64,
          "b": 36
        }
      },
      {
        "by": 15,
        "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 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in Cohere's verified CohereLabs organisation, which cohere.com links to per our other Cohere listings (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on prompt injection, instruction priority or what the model refuses. Licence risk 15, Apache 2.0 has no use policy, threshold or change clause. Security programme 5, Cohere's trust centre holds a responsible disclosure policy with a bug bounty, as recorded on our Cohere listings (5). 25 + 15 + 0 + 0 + 15 + 5 = 60."
        },
        "scores": {
          "a": 75,
          "b": 60
        }
      },
      {
        "by": 10,
        "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 (12,397 characters). Tool-calling format 25, the card documents how tools are declared (JSON schema through the chat template), how the call and its thinking go back into the history and how results are appended, and names the cohere_command4 tool-call parser for vLLM, which released vLLM has. Trained tool calling 20, the card says the model was specifically trained with tool-use capabilities for agentic coding, with reinforcement learning on verifiable tasks, and the template renders every call in a turn with thinking between them. Structured output 0, JSON schema is for describing tools. Reasoning control 10, the template has a reasoning_effort setting, and the card names the cohere_command4 reasoning parser, which returns thinking apart from the answer. Context 10, 256K tokens per the card, with 64K of output, and 500,000 positions in config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85."
        },
        "scores": {
          "a": 95,
          "b": 85
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Mistral AI",
        "b": "Cohere",
        "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": "2026-07-29",
        "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"
      }
    ],
    "faq": [
      {
        "answer": "North Mini Code 1.0 scores 61 (C) on agent readiness against Mistral Large 4's 54.6 (C), and leads in 1 of 4 scored categories. Mistral Large 4 leads on risk, agent interface and documentation and upkeep.",
        "question": "Which is better for AI agents, Mistral Large 4 or North Mini Code 1.0?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Risk, 75 against 60",
          "Agent interface, 95 against 85",
          "Documentation and upkeep, 64 against 36"
        ],
        "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": [
          "Reach, 85 against 50"
        ],
        "also": null,
        "goodFor": "Developers who want a coding agent on their own GPU with a permissive licence",
        "slug": "north-mini-code-1-0",
        "watchFor": "No limitations, safety results, languages or data description on the card"
      }
    ],
    "history": null,
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
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        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-north-mini-code-1-0"
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        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-mistral-large-4"
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        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-mistral-large-4.json",
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        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-north-mini-code-1-0"
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        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-mistral-large-4"
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        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-north-mini-code-1-0"
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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/carbon-x-vs-north-mini-code-1-0"
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        "json": "https://www.anchorterminal.com/compare/cola2-vs-mistral-large-4.json",
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        "url": "https://www.anchorterminal.com/compare/cola2-vs-mistral-large-4"
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        "url": "https://www.anchorterminal.com/compare/cola2-vs-north-mini-code-1-0"
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        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-mistral-large-4"
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        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-5-bolt-e4b-4gb.json",
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        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-5-lightning-30b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-5-lightning-30b-a3b"
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        "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",
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        "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",
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        "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",
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        "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",
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        "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-1-5-122b.json",
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        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-ornith-1-5-122b"
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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",
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        "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-otel-2-0-llm-31b-it.json",
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        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-otel-2-0-llm-31b-it"
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        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen-agentworld-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen-agentworld-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-30b-a3b-instruct-2507.json",
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        "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",
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        "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",
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        "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",
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        "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",
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        "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",
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        "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",
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        "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",
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        "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",
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        "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",
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        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-8-27b"
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        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-8-flash-next.json",
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        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-coder-30b-a3b-instruct.json",
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        "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",
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        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-omni-30b-a3b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-vl-32b-instruct"
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-north-mini-code-1-0"
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-north-mini-code-1-0"
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-north-mini-code-1-0"
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        "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-occamy-1-0"
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        "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-ornith-1-5-35b-a3b"
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    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 35,
        "edge": "north-mini-code-1-0",
        "key": "reach",
        "mistral-large-4": 50,
        "name": "Reach",
        "north-mini-code-1-0": 85,
        "weight": 25
      },
      {
        "by": 15,
        "edge": "mistral-large-4",
        "key": "risk",
        "mistral-large-4": 75,
        "name": "Risk",
        "north-mini-code-1-0": 60,
        "weight": 20
      },
      {
        "by": 10,
        "edge": "mistral-large-4",
        "key": "interface",
        "mistral-large-4": 95,
        "name": "Agent interface",
        "north-mini-code-1-0": 85,
        "weight": 10
      },
      {
        "by": 28,
        "edge": "mistral-large-4",
        "key": "upkeep",
        "mistral-large-4": 64,
        "name": "Documentation and upkeep",
        "north-mini-code-1-0": 36,
        "weight": 10
      }
    ],
    "summary": "North Mini Code 1.0 scores 61 (C) on agent readiness against Mistral Large 4's 54.6 (C), and leads in 1 of 4 scored categories. Mistral Large 4 leads on risk, agent interface and documentation and upkeep. 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.",
      "north-mini-code-1-0": "A coding agent model that fits one 24 GB GPU at 4 bits, trained for tool use, with a 256K context, Apache 2.0 and four released runtimes. It's a research release and reads like one: nothing on limitations, safety or data, the card's run instructions are out of date, and Cohere hasn't answered a question on the repository."
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  "markdown": "North Mini Code 1.0 scores 61 (C) on agent readiness against Mistral Large 4's 54.6 (C), and leads in 1 of 4 scored categories. Mistral Large 4 leads on risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself.\n\n- Mistral Large 4: grade C, 54.6/100, rank #128 of 160 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- North Mini Code 1.0: grade C, 61/100, rank #80 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/north-mini-code-1-0.md · JSON https://www.anchorterminal.com/api/v1/tools/north-mini-code-1-0.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### 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 60\n- Agent interface, 95 against 85\n- Documentation and upkeep, 64 against 36\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### North Mini Code 1.0 (C)\n\nGood for: Developers who want a coding agent on their own GPU with a permissive licence\n\nAhead on:\n- Reach, 85 against 50\n\nWatch for: No limitations, safety results, languages or data description on the card\n\n\n## Why the edge\n\n### Reach: North Mini Code 1.0 +35\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- North Mini Code 1.0 (85): Memory 25, the smallest build from Cohere is the w4a16 repository, 19.38 GB, and 19.38 GB × 1.2 = 23.3 GB, within 24 GB. Unsloth's GGUF isn't read for this line. Runtimes 20, Released vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 have the cohere2_moe model, and llama.cpp b12242 has the cohere2moe architecture. Builds 15, Quantised builds from Cohere at two precisions, FP8 and 4-bit weights (w4a16), each in its own repository. Terms 15, Apache 2.0 in the metadata and on the card, an OSI licence with nothing added. Download 10, the Hub API reports gated false for the model and its builds on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 25 + 20 + 15 + 15 + 10 + 0 = 85.\n\n### Documentation and upkeep: Mistral Large 4 +28\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- North Mini Code 1.0 (36): Model card 11, Intended use, the architecture, the parameters and the context are on the card. Training data 0, the card gives the training method and nothing about the data. Last change 10, the main repository last changed on 15 June 2026 (outdated chat templates removed), 148 days before this check, and the draft model's repository on 29 July, 104 days. Responsiveness 0, none of the 11 most recent discussions that aren't pull requests (7 June to 6 November 2026) has a reply from a CohereLabs member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, A first release, with each build in its own repository. File health 10, Config, template and card agree on the architecture, and the June commit that removed broken templates says so. 11 + 0 + 10 + 0 + 5 + 10 = 36.\n\n### Risk: Mistral Large 4 +15\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- North Mini Code 1.0 (60): File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in Cohere's verified CohereLabs organisation, which cohere.com links to per our other Cohere listings (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on prompt injection, instruction priority or what the model refuses. Licence risk 15, Apache 2.0 has no use policy, threshold or change clause. Security programme 5, Cohere's trust centre holds a responsible disclosure policy with a bug bounty, as recorded on our Cohere listings (5). 25 + 15 + 0 + 0 + 15 + 5 = 60.\n\n### Agent interface: Mistral Large 4 +10\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- North Mini Code 1.0 (85): Chat template 20, chat_template.jinja in the repository (12,397 characters). Tool-calling format 25, the card documents how tools are declared (JSON schema through the chat template), how the call and its thinking go back into the history and how results are appended, and names the cohere_command4 tool-call parser for vLLM, which released vLLM has. Trained tool calling 20, the card says the model was specifically trained with tool-use capabilities for agentic coding, with reinforcement learning on verifiable tasks, and the template renders every call in a turn with thinking between them. Structured output 0, JSON schema is for describing tools. Reasoning control 10, the template has a reasoning_effort setting, and the card names the cohere_command4 reasoning parser, which returns thinking apart from the answer. Context 10, 256K tokens per the card, with 64K of output, and 500,000 positions in config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85.\n\n## Score by category\n\n| Category | Weight | Mistral Large 4 | North Mini Code 1.0 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 50 | 85 | North Mini Code 1.0 +35 |\n| Risk | 20% (20 this run) | 75 | 60 | Mistral Large 4 +15 |\n| Agent interface | 10% (10 this run) | 95 | 85 | Mistral Large 4 +10 |\n| Documentation and upkeep | 10% (10 this run) | 64 | 36 | Mistral Large 4 +28 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **54.6 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Mistral Large 4 | North Mini Code 1.0 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Mistral AI | Cohere |\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 | 2026-07-29 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n\n## Verdicts\n\n**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**North Mini Code 1.0.** A coding agent model that fits one 24 GB GPU at 4 bits, trained for tool use, with a 256K context, Apache 2.0 and four released runtimes. It's a research release and reads like one: nothing on limitations, safety or data, the card's run instructions are out of date, and Cohere hasn't answered a question on the repository.\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### North Mini Code 1.0\n\n1. Serve it with vLLM 0.33.0 or later and `--tool-call-parser cohere_command4 --reasoning-parser cohere_command4 --enable-auto-tool-choice`, with `cohere_melody` installed\n2. Pass the model's thinking back with its tool calls on every turn. The card says results drop without it\n3. Describe tools in JSON schema and append tool results as a dictionary with the tool role\n4. Use temperature 1.0 and top_p 0.95\n5. For llama.cpp the GGUF files are Unsloth's or bartowski's conversions, not Cohere's\n6. It's text only. Don't send images\n\n## Questions\n\n### Which is better for AI agents, Mistral Large 4 or North Mini Code 1.0?\n\nNorth Mini Code 1.0 scores 61 (C) on agent readiness against Mistral Large 4's 54.6 (C), and leads in 1 of 4 scored categories. Mistral Large 4 leads on risk, agent interface and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/mistral-large-4-vs-north-mini-code-1-0.json, and with the fewest tokens: https://www.anchorterminal.com/compare/mistral-large-4-vs-north-mini-code-1-0.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"mistral-large-4\", \"b\": \"north-mini-code-1-0\"}`. From a terminal: `anchor compare mistral-large-4 north-mini-code-1-0`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/mistral-large-4.json and https://www.anchorterminal.com/api/v1/tools/north-mini-code-1-0.json\n\n## Other comparisons with Mistral Large 4 or North Mini Code 1.0\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 North Mini Code 1.0](https://www.anchorterminal.com/compare/agents-a1-4b-vs-north-mini-code-1-0.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 North Mini Code 1.0](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-north-mini-code-1-0.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 North Mini Code 1.0](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-north-mini-code-1-0.md)\n- [Arete-27B vs Mistral Large 4](https://www.anchorterminal.com/compare/arete-27b-vs-mistral-large-4.md)\n- [Arete-27B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/arete-27b-vs-north-mini-code-1-0.md)\n- [BigBang-v1 vs Mistral Large 4](https://www.anchorterminal.com/compare/bigbang-v1-vs-mistral-large-4.md)\n- [BigBang-v1 vs North Mini Code 1.0](https://www.anchorterminal.com/compare/bigbang-v1-vs-north-mini-code-1-0.md)\n- [Carbon-X vs Mistral Large 4](https://www.anchorterminal.com/compare/carbon-x-vs-mistral-large-4.md)\n- [Carbon-X vs North Mini Code 1.0](https://www.anchorterminal.com/compare/carbon-x-vs-north-mini-code-1-0.md)\n- [Cola2 vs Mistral Large 4](https://www.anchorterminal.com/compare/cola2-vs-mistral-large-4.md)\n- [Cola2 vs North Mini Code 1.0](https://www.anchorterminal.com/compare/cola2-vs-north-mini-code-1-0.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 North Mini Code 1.0](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-north-mini-code-1-0.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 North Mini Code 1.0](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-north-mini-code-1-0.md)\n- [DeepSeek-V3.2 vs Mistral Large 4](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-mistral-large-4.md)\n- [DeepSeek-V3.2 vs North Mini Code 1.0](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-north-mini-code-1-0.md)\n- [DeepSeek V4.1 Flash vs Mistral Large 4](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-mistral-large-4.md)\n- [DeepSeek V4.1 Flash vs North Mini Code 1.0](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-north-mini-code-1-0.md)\n- [DeepSeek-V4-Flash-0731 vs Mistral Large 4](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-mistral-large-4.md)\n- [DeepSeek-V4-Flash-0731 vs North Mini Code 1.0](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-north-mini-code-1-0.md)\n- [Devstral Small 2 vs Mistral Large 4](https://www.anchorterminal.com/compare/devstral-small-2-vs-mistral-large-4.md)\n- [Devstral Small 2 vs North Mini Code 1.0](https://www.anchorterminal.com/compare/devstral-small-2-vs-north-mini-code-1-0.md)\n- [DiffusionGemma 26B A4B IT vs Mistral Large 4](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-mistral-large-4.md)\n- [DiffusionGemma 26B A4B IT vs North Mini Code 1.0](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-north-mini-code-1-0.md)\n- [EXAONE 4.5 33B vs Mistral Large 4](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-mistral-large-4.md)\n- [EXAONE 4.5 33B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-north-mini-code-1-0.md)\n- [Gemma 4 12B IT vs Mistral Large 4](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-mistral-large-4.md)\n- [Gemma 4 12B IT vs North Mini Code 1.0](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-north-mini-code-1-0.md)\n- [Gemma 4 26B A4B IT vs Mistral Large 4](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-mistral-large-4.md)\n- [Gemma 4 26B A4B IT vs North Mini Code 1.0](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-north-mini-code-1-0.md)\n- [Gemma 4 31B IT vs Mistral Large 4](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-mistral-large-4.md)\n- [Gemma 4 31B IT vs North Mini Code 1.0](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-north-mini-code-1-0.md)\n- [Gemma 4 E2B IT vs Mistral Large 4](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-mistral-large-4.md)\n- [Gemma 4 E2B IT vs North Mini Code 1.0](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-north-mini-code-1-0.md)\n- [Gemma 4 E4B IT vs Mistral Large 4](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-mistral-large-4.md)\n- [Gemma 4 E4B IT vs North Mini Code 1.0](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-north-mini-code-1-0.md)\n- [GLM-4.5-Air vs Mistral Large 4](https://www.anchorterminal.com/compare/glm-4-5-air-vs-mistral-large-4.md)\n- [GLM-4.5-Air vs North Mini Code 1.0](https://www.anchorterminal.com/compare/glm-4-5-air-vs-north-mini-code-1-0.md)\n- [GLM-4.7-Flash vs Mistral Large 4](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-mistral-large-4.md)\n- [GLM-4.7-Flash vs North Mini Code 1.0](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-north-mini-code-1-0.md)\n- [GLM-5.3-Flash vs Mistral Large 4](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-mistral-large-4.md)\n- [GLM-5.3-Flash vs North Mini Code 1.0](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-north-mini-code-1-0.md)\n- [GLM-5.3 vs Mistral Large 4](https://www.anchorterminal.com/compare/glm-5-3-vs-mistral-large-4.md)\n- [GLM-5.3 vs North Mini Code 1.0](https://www.anchorterminal.com/compare/glm-5-3-vs-north-mini-code-1-0.md)\n- [gpt-oss-120b vs Mistral Large 4](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-mistral-large-4.md)\n- [gpt-oss-120b vs North Mini Code 1.0](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-north-mini-code-1-0.md)\n- [gpt-oss-20b vs Mistral Large 4](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-mistral-large-4.md)\n- [gpt-oss-20b vs North Mini Code 1.0](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-north-mini-code-1-0.md)\n- [Granite 4.2 30B vs Mistral Large 4](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-mistral-large-4.md)\n- [Granite 4.2 30B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-north-mini-code-1-0.md)\n- [Granite 4.2 3B vs Mistral Large 4](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-mistral-large-4.md)\n- [Granite 4.2 3B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-north-mini-code-1-0.md)\n- [Granite 4.2 8B vs Mistral Large 4](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-mistral-large-4.md)\n- [Granite 4.2 8B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-north-mini-code-1-0.md)\n- [Hy-Memory-30B-A3B vs Mistral Large 4](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-mistral-large-4.md)\n- [Hy-Memory-30B-A3B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-north-mini-code-1-0.md)\n- [Hy3 vs Mistral Large 4](https://www.anchorterminal.com/compare/hy3-vs-mistral-large-4.md)\n- [Hy3 vs North Mini Code 1.0](https://www.anchorterminal.com/compare/hy3-vs-north-mini-code-1-0.md)\n- [Inkling-Small vs Mistral Large 4](https://www.anchorterminal.com/compare/inkling-small-vs-mistral-large-4.md)\n- [Inkling-Small vs North Mini Code 1.0](https://www.anchorterminal.com/compare/inkling-small-vs-north-mini-code-1-0.md)\n- [Inkling vs Mistral Large 4](https://www.anchorterminal.com/compare/inkling-vs-mistral-large-4.md)\n- [Inkling vs North Mini Code 1.0](https://www.anchorterminal.com/compare/inkling-vs-north-mini-code-1-0.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Mistral Large 4](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-mistral-large-4.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs North Mini Code 1.0](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-north-mini-code-1-0.md)\n- [Kimi K3.1 Pro vs Mistral Large 4](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-mistral-large-4.md)\n- [Kimi K3.1 Pro vs North Mini Code 1.0](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-north-mini-code-1-0.md)\n- [Kimi K3 vs Mistral Large 4](https://www.anchorterminal.com/compare/kimi-k3-vs-mistral-large-4.md)\n- [Kimi K3 vs North Mini Code 1.0](https://www.anchorterminal.com/compare/kimi-k3-vs-north-mini-code-1-0.md)\n- [Kolibri-1 vs Mistral Large 4](https://www.anchorterminal.com/compare/kolibri-1-vs-mistral-large-4.md)\n- [Kolibri-1 vs North Mini Code 1.0](https://www.anchorterminal.com/compare/kolibri-1-vs-north-mini-code-1-0.md)\n- [Kronos vs Mistral Large 4](https://www.anchorterminal.com/compare/kronos-vs-mistral-large-4.md)\n- [Kronos vs North Mini Code 1.0](https://www.anchorterminal.com/compare/kronos-vs-north-mini-code-1-0.md)\n- [Laguna M.1 vs Mistral Large 4](https://www.anchorterminal.com/compare/laguna-m-1-vs-mistral-large-4.md)\n- [Laguna M.1 vs North Mini Code 1.0](https://www.anchorterminal.com/compare/laguna-m-1-vs-north-mini-code-1-0.md)\n- [Laguna S 2.1 vs Mistral Large 4](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-mistral-large-4.md)\n- [Laguna S 2.1 vs North Mini Code 1.0](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-north-mini-code-1-0.md)\n- [Laguna XS 2.1 vs Mistral Large 4](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-mistral-large-4.md)\n- [Laguna XS 2.1 vs North Mini Code 1.0](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-north-mini-code-1-0.md)\n- [LBS-27B vs Mistral Large 4](https://www.anchorterminal.com/compare/lbs-27b-vs-mistral-large-4.md)\n- [LBS-27B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/lbs-27b-vs-north-mini-code-1-0.md)\n- [LFM2.5-2.6B vs Mistral Large 4](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-mistral-large-4.md)\n- [LFM2.5-2.6B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-north-mini-code-1-0.md)\n- [LFM3-1.7B vs Mistral Large 4](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-mistral-large-4.md)\n- [LFM3-1.7B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-north-mini-code-1-0.md)\n- [LFM3-Audio-2B vs Mistral Large 4](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-mistral-large-4.md)\n- [LFM3-Audio-2B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-north-mini-code-1-0.md)\n- [LFM3-Omni-2B vs Mistral Large 4](https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-mistral-large-4.md)\n- [LFM3-Omni-2B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-north-mini-code-1-0.md)\n- [LFM3-VL-2B vs Mistral Large 4](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-mistral-large-4.md)\n- [LFM3-VL-2B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-north-mini-code-1-0.md)\n- [Ling-3.0-tiny vs Mistral Large 4](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-mistral-large-4.md)\n- [Ling-3.0-tiny vs North Mini Code 1.0](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-north-mini-code-1-0.md)\n- [Ling-3.1-flash-1019 vs Mistral Large 4](https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-mistral-large-4.md)\n- [Ling-3.1-flash-1019 vs North Mini Code 1.0](https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-north-mini-code-1-0.md)\n- [LongCat-2.5 vs Mistral Large 4](https://www.anchorterminal.com/compare/longcat-2-5-vs-mistral-large-4.md)\n- [LongCat-2.5 vs North Mini Code 1.0](https://www.anchorterminal.com/compare/longcat-2-5-vs-north-mini-code-1-0.md)\n- [MiMo-V2.6 Distill Qwen 9B vs Mistral Large 4](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-mistral-large-4.md)\n- [MiMo-V2.6 Distill Qwen 9B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-north-mini-code-1-0.md)\n- [MiMo-V2.6-Flash-RL vs Mistral Large 4](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-mistral-large-4.md)\n- [MiMo-V2.6-Flash-RL vs North Mini Code 1.0](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-north-mini-code-1-0.md)\n- [MiniCPM-o 4.5 vs Mistral Large 4](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-mistral-large-4.md)\n- [MiniCPM-o 4.5 vs North Mini Code 1.0](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-north-mini-code-1-0.md)\n- [MiniCPM-V 4.7 1B vs Mistral Large 4](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-mistral-large-4.md)\n- [MiniCPM-V 4.7 1B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-north-mini-code-1-0.md)\n- [MiniCPM-V 4.7 35B-A3B vs Mistral Large 4](https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-mistral-large-4.md)\n- [MiniCPM-V 4.7 35B-A3B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-north-mini-code-1-0.md)\n- [MiniCPM5-2B vs Mistral Large 4](https://www.anchorterminal.com/compare/minicpm5-2b-vs-mistral-large-4.md)\n- [MiniCPM5-2B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/minicpm5-2b-vs-north-mini-code-1-0.md)\n- [MiniMax-M2.5 vs Mistral Large 4](https://www.anchorterminal.com/compare/minimax-m2-5-vs-mistral-large-4.md)\n- [MiniMax-M2.5 vs North Mini Code 1.0](https://www.anchorterminal.com/compare/minimax-m2-5-vs-north-mini-code-1-0.md)\n- [MiniMax-M2.7 vs Mistral Large 4](https://www.anchorterminal.com/compare/minimax-m2-7-vs-mistral-large-4.md)\n- [MiniMax-M2.7 vs North Mini Code 1.0](https://www.anchorterminal.com/compare/minimax-m2-7-vs-north-mini-code-1-0.md)\n- [MiniMax-M3.1-Flash vs Mistral Large 4](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-mistral-large-4.md)\n- [MiniMax-M3.1-Flash vs North Mini Code 1.0](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-north-mini-code-1-0.md)\n- [Mistral Large 4 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/mistral-large-4-vs-muse-glimmer-30b.md)\n- [Mistral Large 4 vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/mistral-large-4-vs-nanbeige-4-2-3b.md)\n- [Mistral Large 4 vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/mistral-large-4-vs-nanbeige-4-3-33b.md)\n- [Mistral Large 4 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Mistral Large 4 vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Mistral Large 4 vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-nano-30b-a3b.md)\n- [Mistral Large 4 vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-nano-4b.md)\n- [Mistral Large 4 vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Mistral Large 4 vs Nemotron 3 Super 120B 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