{
  "data": {
    "a": {
      "slug": "granite-4-2-30b",
      "name": "Granite 4.2 30B",
      "vendor": "IBM",
      "vendorUrl": "https://www.ibm.com/granite",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Granite 4.2 30B is IBM's open-weight dense reasoning model of 29.3B parameters, post-trained from Granite 4.1 30B Base, with switchable thinking, tool calling and a 131,072-token context, published in BF16, FP8, FP4, GGUF and MLX under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/granite-4-2-30b",
      "markdownUrl": "https://www.anchorterminal.com/tools/granite-4-2-30b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/granite-4-2-30b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/granite-4-2-30b.json",
      "dns": "granite-4-2-30b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/ibm-granite/granite-4.2-30b",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "ibm-granite/granite-4.2-30b"
        },
        {
          "registry": "huggingface",
          "name": "ibm-granite/granite-4.2-30b-GGUF"
        },
        {
          "registry": "huggingface",
          "name": "ibm-granite/granite-4.2-30b-fp8"
        },
        {
          "registry": "huggingface",
          "name": "ibm-granite/granite-4.2-30b-mxfp4"
        },
        {
          "registry": "huggingface",
          "name": "ibm-granite/granite-4.2-30b-nvfp4"
        },
        {
          "registry": "huggingface",
          "name": "ibm-granite/granite-4.2-30b-q4-mlx"
        },
        {
          "registry": "ollama",
          "name": "granite4.2:30b"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The BF16 repository and IBM's FP8, MXFP4, NVFP4, GGUF and MLX repositories are not gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it yourself costs the hardware, 58.5 GB for the BF16 files, 17.7 GB for IBM's Q4_K_M GGUF or 16.5 GB for its 4-bit MLX build (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/ibm-granite/granite-4.2-30b",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "reasoning",
        "tool-calling",
        "multilingual",
        "apache-2.0",
        "safetensors",
        "fp8",
        "fp4",
        "gguf",
        "mlx",
        "signed",
        "self-hosted"
      ],
      "lastRelease": "2026-09-04",
      "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": 52,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 85,
          "reach": 90,
          "risk": 55,
          "upkeep": 87
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "IBM's largest reasoning cousin, with the same strengths as the 3B. It runs in every runtime we count from IBM's own builds on a 24 GB card, is signed, plain Apache 2.0, and comes with a disclosure of its training data. No safety results are published and structured output isn't documented.",
        "bestFor": "Agents that want a documented, signed, Apache 2.0 reasoning model with tool calling on a 24 GB card",
        "strengths": [
          "Runs in llama.cpp, Ollama, vLLM, SGLang and Transformers from IBM's own FP8, FP4, GGUF and MLX builds",
          "Plain Apache 2.0, ungated, signed with sigstore",
          "The card documents the tool-calling format, the parser to use and three thinking modes",
          "IBM publishes a machine-readable disclosure of the training datasets",
          "IBM answers most questions on the repository"
        ],
        "weaknesses": [
          "No safety results published for it",
          "Structured output isn't documented",
          "The card's vLLM command still loads a Python plugin from the repository, which released vLLM no longer needs",
          "No knowledge cut-off on the card",
          "One inference provider, so an agent without the hardware has little choice"
        ],
        "agentNotes": [
          "Start it with `ollama run granite4.2:30b`, or `llama-server -hf ibm-granite/granite-4.2-30b-GGUF:Q4_K_M --jinja`",
          "With vLLM 0.32.0 or later leave out `--reasoning-parser-plugin`, since the parser is built in, and use `--tool-call-parser qwen3_coder`",
          "Thinking is on by default. Pass `enable_thinking` false to turn it off or `low_effort` true for short reasoning",
          "The context is 131,072 tokens. The card's 512K extension isn't documented",
          "Validate JSON output yourself, since structured output isn't documented",
          "IBM recommends Granite Guardian beside it to flag risky inputs and outputs"
        ],
        "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": 90,
          "risk": 55,
          "upkeep": 87
        },
        "provenanceScore": 88,
        "checked": {
          "categories": {
            "interface": "2026-11-10",
            "reach": "2026-11-10",
            "risk": "2026-11-10",
            "upkeep": "2026-11-10"
          },
          "last": "2026-11-10",
          "oldest": "2026-11-10",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama run granite4.2:30b\n# or, with llama.cpp and IBM's own GGUF:\nllama-server -hf ibm-granite/granite-4.2-30b-GGUF:Q4_K_M --jinja\n# or, with vLLM 0.32.0 or later:\nvllm serve ibm-granite/granite-4.2-30b --max-model-len 131072 \\\n  --reasoning-parser granite_thinking_parser \\\n  --tool-call-parser qwen3_coder --enable-auto-tool-choice",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"granite4.2:30b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/granite-4-2-30b"
      },
      "sameCompany": [
        "granite-embedding-small-english-r2",
        "granite-guardian",
        "granite-speech-5-0-2b",
        "ibm-watson-stt",
        "granite-switch",
        "granite-4-2-3b",
        "granite-4-2-8b"
      ],
      "weights": {
        "trainedBy": "IBM",
        "builtOn": "ibm-granite/granite-4.1-30b-base (IBM's own base checkpoint, post-trained; named on the card and in its metadata)",
        "params": "29.3B",
        "context": 131072,
        "goodFor": "IBM's largest dense reasoning model for tool calling, coding and multilingual chat, with thinking that is on, off or short per request, on a 24 GB card at 4 bits.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "granite-4.2-30b-nvfp4",
            "url": "https://huggingface.co/ibm-granite/granite-4.2-30b-nvfp4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 17.65,
            "memoryGB": 24,
            "format": "safetensors",
            "runsOn": [
              "vLLM"
            ]
          },
          {
            "name": "granite-4.2-30b-mxfp4",
            "url": "https://huggingface.co/ibm-granite/granite-4.2-30b-mxfp4",
            "who": "publisher",
            "precision": "mxfp4",
            "sizeGB": 16.76,
            "memoryGB": 24,
            "format": "safetensors",
            "runsOn": [
              "vLLM"
            ]
          },
          {
            "name": "granite-4.2-30b-Q4_K_M",
            "url": "https://huggingface.co/ibm-granite/granite-4.2-30b-GGUF",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 17.72,
            "memoryGB": 24,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "IBM's GGUF repository also holds Q4_0 (16.58 GB), Q6_K and BF16 files among others"
          },
          {
            "name": "granite-4.2-30b-q4-mlx",
            "url": "https://huggingface.co/ibm-granite/granite-4.2-30b-q4-mlx",
            "who": "publisher",
            "precision": "q4",
            "sizeGB": 16.47,
            "memoryGB": 24,
            "format": "mlx",
            "runsOn": [
              "mlx-lm"
            ],
            "note": "The smallest build at 4-bit or better. IBM also publishes q8-mlx (31.11 GB) and bf16-mlx"
          },
          {
            "name": "ollama granite4.2:30b",
            "url": "https://ollama.com/library/granite4.2:30b",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 18,
            "memoryGB": 24,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Ollama's library build, size as its tags page shows it on 10 November 2026"
          },
          {
            "name": "granite-4.2-30b-fp8",
            "url": "https://huggingface.co/ibm-granite/granite-4.2-30b-fp8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 30.11,
            "memoryGB": 48,
            "format": "safetensors",
            "runsOn": [
              "vLLM"
            ]
          },
          {
            "name": "granite-4.2-30b-Q8_0",
            "url": "https://huggingface.co/ibm-granite/granite-4.2-30b-GGUF",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 31.11,
            "memoryGB": 48,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ]
          },
          {
            "name": "granite-4.2-30b",
            "url": "https://huggingface.co/ibm-granite/granite-4.2-30b",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 58.55,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang",
              "Transformers"
            ]
          }
        ],
        "run": "ollama run granite4.2:30b",
        "runBuild": "ollama granite4.2:30b",
        "checked": "2026-11-10",
        "minMemoryGB": 24
      },
      "family": {
        "lab": "IBM (Granite)",
        "family": "Granite 4 (4.0, 4.1, 4.2)",
        "url": "https://www.anchorterminal.com/open-weights/#ibm",
        "others": [
          {
            "name": "granite-4.2-3b",
            "url": "https://huggingface.co/ibm-granite/granite-4.2-3b",
            "params": "3B (Hub safetensors count 3.66B)",
            "listing": "https://www.anchorterminal.com/tools/granite-4-2-3b"
          },
          {
            "name": "granite-4.2-8b",
            "url": "https://huggingface.co/ibm-granite/granite-4.2-8b",
            "params": "8B (Hub safetensors count 8.79B)",
            "listing": "https://www.anchorterminal.com/tools/granite-4-2-8b"
          },
          {
            "name": "granite-4.1-8b-base",
            "url": "https://huggingface.co/ibm-granite/granite-4.1-8b-base",
            "params": "8B (Hub safetensors count 8.38B)"
          },
          {
            "name": "granite-4.1-8b",
            "url": "https://huggingface.co/ibm-granite/granite-4.1-8b",
            "params": "8B (Hub safetensors count 8.79B)"
          },
          {
            "name": "granite-4.1-3b-base",
            "url": "https://huggingface.co/ibm-granite/granite-4.1-3b-base",
            "params": "3B (Hub safetensors count 3.40B)"
          },
          {
            "name": "granite-4.1-3b",
            "url": "https://huggingface.co/ibm-granite/granite-4.1-3b",
            "params": "3B (Hub safetensors count 3.40B)"
          },
          {
            "name": "granite-4.1-30b-base",
            "url": "https://huggingface.co/ibm-granite/granite-4.1-30b-base",
            "params": "30B (Hub safetensors count 28.87B)"
          },
          {
            "name": "granite-4.1-30b",
            "url": "https://huggingface.co/ibm-granite/granite-4.1-30b",
            "params": "30B (Hub safetensors count 28.87B)"
          },
          {
            "name": "granite-4.0-h-350m-base",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-h-350m-base",
            "params": "350M (Hub safetensors count 340M)"
          },
          {
            "name": "granite-4.0-h-350m",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-h-350m",
            "params": "350M (Hub safetensors count 340M)"
          },
          {
            "name": "granite-4.0-h-1b-base",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-h-1b-base",
            "params": "1B (Hub safetensors count 1.46B)"
          },
          {
            "name": "granite-4.0-h-1b",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-h-1b",
            "params": "1B (Hub safetensors count 1.46B)"
          },
          {
            "name": "granite-4.0-350m-base",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-350m-base",
            "params": "350M (Hub safetensors count 352M)"
          },
          {
            "name": "granite-4.0-350m",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-350m",
            "params": "350M (Hub safetensors count 352M)"
          },
          {
            "name": "granite-4.0-1b-base",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-1b-base",
            "params": "1B (Hub safetensors count 1.63B)"
          },
          {
            "name": "granite-4.0-1b",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-1b",
            "params": "1B (Hub safetensors count 1.63B)"
          },
          {
            "name": "granite-4.0-micro-base",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-micro-base",
            "params": "3.40B (Hub safetensors count; the name states none)"
          },
          {
            "name": "granite-4.0-micro",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-micro",
            "params": "3.40B (Hub safetensors count; the name states none)"
          },
          {
            "name": "granite-4.0-h-tiny-base",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-h-tiny-base",
            "params": "6.94B (Hub safetensors count; the name states none)"
          },
          {
            "name": "granite-4.0-h-tiny",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-h-tiny",
            "params": "6.94B (Hub safetensors count; the name states none)"
          },
          {
            "name": "granite-4.0-h-small-base",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-h-small-base",
            "params": "32.21B (Hub safetensors count; the name states none)"
          },
          {
            "name": "granite-4.0-h-small",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-h-small",
            "params": "32.21B (Hub safetensors count; the name states none)"
          },
          {
            "name": "granite-4.0-h-micro-base",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-h-micro-base",
            "params": "3.19B (Hub safetensors count; the name states none)"
          },
          {
            "name": "granite-4.0-h-micro",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-h-micro",
            "params": "3.19B (Hub safetensors count; the name states none)"
          },
          {
            "name": "granite-4.0-tiny-preview",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-tiny-preview",
            "params": "6.67B (Hub safetensors count; the name states none)"
          },
          {
            "name": "granite-4.0-tiny-base-preview",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-tiny-base-preview",
            "params": "6.67B (Hub safetensors count; the name states none)"
          }
        ],
        "olderCount": 64
      },
      "area": "models",
      "provenance": {
        "legalEntity": "International Business Machines Corporation (per IBM's naming guidance, as read for granite-guardian)",
        "domain": "ibm.com",
        "domainRegistered": "1986-03-19",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/ibm-granite/granite-4.2-30b/commits/main",
        "securityTxt": "valid",
        "checked": "2026-11-10",
        "notes": [
          "IBM's Granite docs page for 4.2 links the ibm-granite collection on Hugging Face and the GitHub repository, and the docs site says it is no longer updated. The domain date is carried from the granite-guardian check.",
          "The ibm-granite organisation (209 members) isn't verified on the Hub, which the calibration doesn't require.",
          "Nothing is hosted for this listing, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the Hugging Face commit history, 15 commits from 7 August to 20 October 2026, most titled only \"granite-4.2\".",
          "www.ibm.com/.well-known/security.txt is present with PSIRT contacts and expires on 21 November 2026.",
          "Released on 25 August 2026 by the card and IBM's blog, which firstReleased uses. The repository and its weight files date from 7 August 2026, the date BATCH.md and the IBM family page used before 21 October."
        ],
        "score": 88
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/granite-4-2-30b.json"
    },
    "answer": "Granite 4.2 30B scores 61 (C) on agent readiness against MiniMax-M2.7's 57.6 (C), and leads in every scored category.",
    "b": {
      "slug": "minimax-m2-7",
      "name": "MiniMax-M2.7",
      "vendor": "MiniMax",
      "vendorUrl": "https://www.minimax.io",
      "kind": "weights",
      "category": "open-weights",
      "summary": "MiniMax-M2.7 is MiniMax's open-weight mixture-of-experts model for coding, agent harnesses and office work, about 229B parameters in FP8 with thinking always on, published on Hugging Face on 9 April 2026 under a non-commercial licence.",
      "url": "https://www.anchorterminal.com/tools/minimax-m2-7",
      "markdownUrl": "https://www.anchorterminal.com/tools/minimax-m2-7.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/minimax-m2-7.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/minimax-m2-7.json",
      "dns": "minimax-m2-7.grade.anchorterminal.com",
      "repo": "https://huggingface.co/MiniMaxAI/MiniMax-M2.7",
      "license": "MiniMax M2.7 non-commercial licence (MIT-style terms, commercial use needs MiniMax's written authorisation)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "MiniMaxAI/MiniMax-M2.7"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and to use for personal, research and non-profit purposes. Since 11 April 2026 the licence prohibits commercial use without MiniMax's prior written authorisation, and commercial users must display \"Built with MiniMax M2.7\". Running it yourself costs the hardware, 230.2 GB of FP8 files. MiniMax's API closed M2.7 to new users on 19 October 2026 and retires it on 18 November 2026 (checked 2026-10-21).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-21"
      },
      "docsUrl": "https://huggingface.co/MiniMaxAI/MiniMax-M2.7",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "reasoning",
        "tool-calling",
        "coding",
        "non-commercial",
        "safetensors",
        "fp8",
        "self-hosted"
      ],
      "lastRelease": "2026-04-20",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 57.6,
        "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": 80,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "capability": 71,
          "interface": 75,
          "reach": 60,
          "risk": 30,
          "upkeep": 42
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-11-04"
        },
        "negative": 0,
        "verdict": "A 230B mixture-of-experts model for coding and agent harnesses, with parallel tool calls, about 200K of context and parsers in vLLM and SGLang. It needs a 4 GPU server for FP8 or about 166 GB for a 4-bit library build, and it always thinks. The main risk is the licence, switched to non-commercial two days after release.",
        "bestFor": "Researchers and personal projects with a 4 to 8 GPU server who want an open agent and coding model with parallel tool calls",
        "strengths": [
          "Runs in released vLLM, SGLang, llama.cpp, Transformers and LM Studio, and in Ollama through its pinned llama.cpp",
          "Tool-call format documented by MiniMax with named vLLM and SGLang parsers and a parsing function",
          "Several tool calls in one block, and about 200K tokens of context",
          "Safetensors only, in the publisher's organisation with a SHA-256 for every shard",
          "No gate or account, and served by three inference providers on Hugging Face"
        ],
        "weaknesses": [
          "Commercial use needs MiniMax's written authorisation since a licence swap on 11 April 2026, made without a note",
          "230 GB in FP8, and about 166 GB for the smallest 4-bit library build, more than a 128 GB machine",
          "Thinking is always on, with no documented way to turn it off or budget it",
          "No safety evaluation, agent guidance or security contact, and every loading route runs repository code",
          "No MiniMax reply in discussions since June 2026, and the card leaves out limitations, architecture, parameters and a cut-off"
        ],
        "agentNotes": [
          "Check the use is personal, research or non-profit before starting it; anything commercial needs MiniMax's written authorisation, and commercial users must show Built with MiniMax M2.7",
          "Start it with vLLM on 4 GPUs of 96 GB, SAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.7 --trust-remote-code --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 --reasoning-parser minimax_m2_append_think",
          "In SGLang use --tool-call-parser minimax-m2 and --reasoning-parser minimax-append-think, with --trust-remote-code",
          "MiniMax's guides budget 220 GB for the weights and 240 GB per 1M tokens of KV cache, with 196K the most per sequence",
          "On one large-memory machine, LM Studio's catalogue entry minimax/minimax-m2.7 lists a 138 GB Q4_K_M GGUF and states 138 GB minimum memory",
          "Sample with temperature 1.0, top_p 0.95 and top_k 40, as the card recommends, and budget output tokens for thinking on every turn",
          "Pin Transformers 4.57.1 as MiniMax's guide does; a report that it fails on Transformers v5 has no answer",
          "Without the hardware, call it through Novita, Featherless AI or DeepInfra; MiniMax's own API retires M2.7 on 18 November 2026"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 1.8,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 57.6
          }
        ],
        "editorialScores": {
          "capability": 71,
          "interface": 75,
          "reach": 60,
          "risk": 30,
          "upkeep": 42
        },
        "provenanceScore": 60,
        "checked": {
          "categories": {
            "capability": "2026-11-04",
            "interface": "2026-11-04",
            "reach": "2026-11-04",
            "risk": "2026-11-04",
            "upkeep": "2026-11-04"
          },
          "last": "2026-11-04",
          "lastMoved": "2026-11-04",
          "oldest": "2026-11-04",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "# MiniMax's vLLM guide, 4 GPUs\nuv pip install vllm --torch-backend=auto\nSAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.7 --trust-remote-code \\\n  --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 \\\n  --reasoning-parser minimax_m2_append_think",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"model\": \"MiniMaxAI/MiniMax-M2.7\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"temperature\": 1.0, \"top_p\": 0.95, \"top_k\": 40}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/minimax-m2-7"
      },
      "sameCompany": [
        "minimax-api",
        "minimax-video",
        "minimax-stt",
        "minimax-speech",
        "minimax-voice-cloning",
        "minimax-code",
        "minimax-m2-5",
        "minimax-m3-1-flash"
      ],
      "weights": {
        "trainedBy": "MiniMax",
        "params": "228.7B",
        "context": 204800,
        "goodFor": "Mixture-of-experts model for coding, agent harnesses and office documents, with parallel tool calls and always-on thinking, text only, for non-commercial use unless MiniMax authorises otherwise.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "LM Studio",
          "Transformers",
          "Ollama"
        ],
        "builds": [
          {
            "name": "LM Studio minimax/minimax-m2.7 (Q4_K_M GGUF)",
            "url": "https://huggingface.co/lmstudio-community/MiniMax-M2.7-GGUF",
            "who": "lmstudio",
            "precision": "q4_k_m",
            "sizeGB": 138.3,
            "memoryGB": 192,
            "format": "gguf",
            "runsOn": [
              "LM Studio",
              "llama.cpp"
            ],
            "note": "MiniMax-M2.7-Q4_K_M split GGUF, 138.34 GB summed from the Hub's file list. The catalogue entry also lists Q6_K (187.8 GB) and Q8_0 (243.1 GB). LM Studio states 138 GB minimum memory"
          },
          {
            "name": "MiniMax-M2.7",
            "url": "https://huggingface.co/MiniMaxAI/MiniMax-M2.7",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 230.2,
            "memoryGB": 512,
            "format": "safetensors",
            "note": "Native FP8 E4M3 with 128 by 128 blocks; the only build MiniMax publishes. Size summed from the Hub's file list"
          }
        ],
        "run": "SAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.7 --trust-remote-code --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 --reasoning-parser minimax_m2_append_think",
        "runBuild": "MiniMax-M2.7",
        "checked": "2026-11-04",
        "minMemoryGB": 192
      },
      "family": {
        "lab": "MiniMax",
        "family": "MiniMax-M2",
        "url": "https://www.anchorterminal.com/open-weights/#minimax",
        "others": [
          {
            "name": "MiniMax-M2",
            "url": "https://huggingface.co/MiniMaxAI/MiniMax-M2",
            "params": "230B total, 10B active"
          },
          {
            "name": "MiniMax-M2.1",
            "url": "https://huggingface.co/MiniMaxAI/MiniMax-M2.1",
            "params": "230B total, 10B active (Hub count; card doesn't restate)"
          },
          {
            "name": "MiniMax-M2.5",
            "url": "https://huggingface.co/MiniMaxAI/MiniMax-M2.5",
            "params": "230B total, 10B active (Hub count; card doesn't restate)",
            "listing": "https://www.anchorterminal.com/tools/minimax-m2-5"
          }
        ],
        "olderCount": 7,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "MiniMax (named in the licence's copyright line; MiniMax's platform terms name Nanonoble Pte. Ltd., Singapore)",
        "domain": "minimax.io",
        "domainRegistered": "2021-03-27",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "The current licence reads Copyright (c) 2026 MiniMax. The platform terms' legal entity and the domain's registration date are as recorded on the minimax-api listing.",
          "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored.",
          "No changelog. Commit messages are uploads and README or LICENSE updates; the licence change on 11 April 2026 reads only \"Delete LICENSE-MODEL\" and \"Upload LICENSE with huggingface_hub\".",
          "www.minimax.io/.well-known/security.txt redirects to the home page on 21 October 2026, and MiniMax-AI/MiniMax-M2.7 has no SECURITY.md, so there is no security.txt.",
          "MiniMax's site (www.minimax.io/models/text) links to huggingface.co/MiniMaxAI, and the card links the GitHub repository, so the repository is the publisher's own."
        ],
        "score": 60
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/minimax-m2-7.json",
      "live": {
        "slug": "minimax-m2-7",
        "securityTxt": {
          "url": "https://minimax.io/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-11-10T16:08:11.844974518Z"
        },
        "domain": {
          "domain": "minimax.io",
          "checkedAt": "2026-11-08T14:54:44.54388672Z"
        },
        "updatedAt": "2026-11-10T16:08:11.844974518Z"
      }
    },
    "edges": [
      {
        "by": 45,
        "category": "upkeep",
        "leads": "granite-4-2-30b",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 17, Intended use (best for), limitations (ethical considerations and limitations), architecture, parameters (30B), context length (128K native) and 12 tested languages are on the card. Training data 20, IBM's disclosure file for the 30B lists the datasets used in pre-training and post-training as machine-readable metadata, and the card describes the three training stages. Last change 15, the last commit to the repository was on 4 September 2026, 67 days before this check. Responsiveness 20, Of the 4 discussions that aren't pull requests (28 August to 3 November 2026), 3 have a reply from an ibm-granite member. Version policy 5, Granite 4.1 30B stays in its own repository and 4.2 came out in new ones. File health 10, Config, template and card agree on the architecture, the 131,072-token context and the thinking switch. 17 + 20 + 15 + 20 + 5 + 10 = 87.",
          "b": "The model card earns 2, stating only intended use and leaving out limitations, architecture, parameters, context, natural languages and a cut-off. Training data earns nothing, since only the training process is described. The last change earns 20, a card commit on 26 October 2026 that renames a MiniMax Agent link to MiniMax Code, 9 days before this check; the files, template and code last changed on 20 April 2026. Responsiveness earns 10, with a MiniMax reply in 8 of 36 discussions, none since June. Version policy earns 5, since earlier and later models sit in their own repositories without dated notes. File health earns 5, because a report that it doesn't load in Transformers v5 waits without a reply."
        },
        "scores": {
          "a": 87,
          "b": 42
        }
      },
      {
        "by": 30,
        "category": "reach",
        "leads": "granite-4-2-30b",
        "name": "Reach",
        "reasons": {
          "a": "Memory 25, IBM's own 4-bit MLX build granite-4.2-30b-q4-mlx is 16.47 GB, and 16.47 GB × 1.2 = 19.8 GB, which fits 24 GB but not 16 GB. Its Q4_0 GGUF is 16.58 GB and Ollama's library build granite4.2:30b 18 GB, the same band. Runtimes 25, Released versions run the granite architecture in llama.cpp (granite, b12242), vLLM (GraniteForCausalLM in the v0.33.0 registry), SGLang (granite.py at v0.5.23), Transformers (v5.22.2) and Ollama (its own granite4.2 library entry, 30b tag). Builds 15, IBM publishes FP8, MXFP4 and NVFP4 safetensors builds, a GGUF repository with Q4_0 to Q8_0 and BF16 files, and 4-bit, 8-bit and BF16 MLX builds, so quantised builds at two precisions or more. Terms 15, Apache 2.0, an OSI-approved licence, with no use policy or other terms added on the card. Download 10, the Hub API reports gated false for the BF16, FP8, MXFP4, NVFP4, GGUF and MLX repositories, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps one inference provider (DeepInfra, live), short of the two the line needs. 25 + 25 + 15 + 15 + 10 + 0 = 90.",
          "b": "Memory earns 5. MiniMax only publishes its 230 GB FP8 release, and the smallest build of 4-bit or better from a runtime's own library is LM Studio's Q4_K_M GGUF at 138.3 GB, about 166 GB with the fifth added, so it needs more than our 107.4 GB machine and fits within 256 GB. Runtimes earn the full 25, with model code in released vLLM, SGLang, llama.cpp and Transformers, an LM Studio catalogue entry, and Ollama 0.40.14, whose pinned llama.cpp b11963 has the minimax-m2 architecture (capped at 25). Builds earn 10 for one publisher precision, the FP8 release. Terms earn 5, since the licence today allows only personal, research and non-profit use without MiniMax's written authorisation. Download earns 10, with no gate or account. Hosted earns 5, since Novita, Featherless AI and DeepInfra serve it through Hugging Face, while MiniMax's own API is closed to new users and retires it on 18 November 2026."
        },
        "scores": {
          "a": 90,
          "b": 60
        }
      },
      {
        "by": 25,
        "category": "risk",
        "leads": "granite-4-2-30b",
        "name": "Risk",
        "reasons": {
          "a": "File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the ibm-granite organisation, which IBM's Granite docs and the GitHub README link to (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions. Licence risk 15, Apache 2.0 has no use policy, field-of-use limit, threshold or change clause, and the card's licence link resolves. Security programme 5, www.ibm.com/.well-known/security.txt names IBM PSIRT, read for the 3B listing on 4 November 2026 (5). 15 + 15 + 0 + 5 + 15 + 5 = 55.",
          "b": "File safety earns 15, safetensors only with no pickle files, less 10 because every documented loading route passes trust_remote_code for the repository's Python. Provenance earns the full 15, files in the organisation MiniMax links to with a SHA-256 for each shard. Safety evaluation and agent guidance earn nothing, as MiniMax publishes neither. Licence risk earns nothing (read 21 October 2026). The Prohibited Uses appendix goes beyond the law, MiniMax swapped a commercial licence for a non-commercial one two days after release, and neither that swap nor the amendment three days later carries a dated note. The security programme earns nothing, with no security.txt, disclosure policy, SECURITY.md or advisories."
        },
        "scores": {
          "a": 55,
          "b": 30
        }
      },
      {
        "by": 10,
        "category": "interface",
        "leads": "granite-4-2-30b",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, chat_template.jinja in the repository, with roles, tools, tool responses, the thinking switch and low effort. Tool-calling format 25, the card has tool-calling sections for vLLM and SGLang, declares tools with OpenAI's function schema and names the parser to use, qwen3_coder, which both runtimes ship in the released versions read. Trained tool calling 20, the template loops over every entry in an assistant turn's tool_calls, so it renders several calls in one turn, and the card names tool calling and agentic workflows as what it's best for. Structured output 0, the card doesn't document JSON or JSON Schema output or how to ask for it. Reasoning control 10, the card documents thinking on by default, non-thinking and low-effort modes per request through the template (enable_thinking and low_effort), with the reasoning returned apart from the answer. Context 10, 131,072 tokens natively per the card and config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85.",
          "b": "The chat template earns 20 for chat_template.jinja in the repository. The tool-calling format earns 25, as MiniMax's own guide documents how tools go in and how calls come back, gives a parsing function, and its deployment guides name the vLLM and SGLang parsers. Trained tool calling earns 20, with several invoke blocks in one call. Structured output earns nothing, since JSON output isn't documented or claimed. Reasoning control earns nothing, because thinking is always on with no documented way to turn it off or budget it. Context earns 10 for 204,800 tokens in the config with no position scaling."
        },
        "scores": {
          "a": 85,
          "b": 75
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "IBM",
        "b": "MiniMax",
        "name": "Vendor"
      },
      {
        "a": "no (local only)",
        "b": "no (local only)",
        "name": "Hosted endpoint"
      },
      {
        "a": "",
        "b": "",
        "name": "Transports"
      },
      {
        "a": "None",
        "b": "None",
        "name": "Auth"
      },
      {
        "a": "Free",
        "b": "Free",
        "name": "Pricing"
      },
      {
        "a": "no",
        "b": "no",
        "name": "x402"
      },
      {
        "a": "Apache-2.0",
        "b": "MiniMax M2.7 non-commercial licence (MIT-style terms, commercial use needs MiniMax's written authorisation)",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-09-04",
        "b": "2026-04-20",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "none",
        "b": "1.8/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Granite 4.2 30B scores 61 (C) on agent readiness against MiniMax-M2.7's 57.6 (C), and leads in every scored category.",
        "question": "Which is better for AI agents, Granite 4.2 30B or MiniMax-M2.7?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 90 against 60",
          "Risk, 55 against 30",
          "Agent interface, 85 against 75",
          "Documentation and upkeep, 87 against 42"
        ],
        "also": null,
        "goodFor": "Agents that want a documented, signed, Apache 2.0 reasoning model with tool calling on a 24 GB card",
        "slug": "granite-4-2-30b",
        "watchFor": "No safety results published for it"
      },
      {
        "aheadOn": null,
        "also": null,
        "goodFor": "Researchers and personal projects with a 4 to 8 GPU server who want an open agent and coding model with parallel tool calls",
        "slug": "minimax-m2-7",
        "watchFor": "Commercial use needs MiniMax's written authorisation since a licence swap on 11 April 2026, made without a note"
      }
    ],
    "history": {
      "granite-4-2-30b": null,
      "minimax-m2-7": [
        {
          "causes": [
            "product"
          ],
          "date": "2026-11-04",
          "delta": 1.5,
          "from": 56.1,
          "fromGrade": "C",
          "to": 57.6,
          "toGrade": "C"
        }
      ]
    },
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-granite-4-2-30b.json",
        "title": "Agents-A1-4B vs Granite 4.2 30B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-granite-4-2-30b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minimax-m2-7.json",
        "title": "Agents-A1-4B vs MiniMax-M2.7",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minimax-m2-7"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-granite-4-2-30b.json",
        "title": "Apertus v1.5 8B vs Granite 4.2 30B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-granite-4-2-30b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minimax-m2-7.json",
        "title": "Apertus v1.5 8B vs MiniMax-M2.7",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minimax-m2-7"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-granite-4-2-30b.json",
        "title": "Apodex-1.1-mini vs Granite 4.2 30B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-granite-4-2-30b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minimax-m2-7.json",
        "title": "Apodex-1.1-mini vs MiniMax-M2.7",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minimax-m2-7"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-granite-4-2-30b.json",
        "title": "Arete-27B vs Granite 4.2 30B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-granite-4-2-30b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-minimax-m2-7.json",
        "title": "Arete-27B vs MiniMax-M2.7",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-minimax-m2-7"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-granite-4-2-30b.json",
        "title": "BigBang-v1 vs Granite 4.2 30B",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-granite-4-2-30b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-minimax-m2-7.json",
        "title": "BigBang-v1 vs MiniMax-M2.7",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-minimax-m2-7"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-granite-4-2-30b.json",
        "title": "Carbon-X vs Granite 4.2 30B",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-granite-4-2-30b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-minimax-m2-7.json",
        "title": "Carbon-X vs MiniMax-M2.7",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-minimax-m2-7"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-granite-4-2-30b.json",
        "title": "Cola2 vs Granite 4.2 30B",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-granite-4-2-30b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-minimax-m2-7.json",
        "title": "Cola2 vs MiniMax-M2.7",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-minimax-m2-7"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-granite-4-2-30b.json",
        "title": "Cosmos-Reason2-2B vs Granite 4.2 30B",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-granite-4-2-30b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minimax-m2-7.json",
        "title": "Cosmos-Reason2-2B vs MiniMax-M2.7",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minimax-m2-7"
      },
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        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-granite-4-2-30b.json",
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        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-minimax-m2-7"
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      {
        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-minimax-m2-7.json",
        "title": "Ling-3.1-flash-1019 vs MiniMax-M2.7",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-minimax-m2-7"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-minimax-m2-7.json",
        "title": "LongCat-2.5 vs MiniMax-M2.7",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-minimax-m2-7"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-minimax-m2-7.json",
        "title": "MiMo-V2.6 Distill Qwen 9B vs MiniMax-M2.7",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-minimax-m2-7"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minimax-m2-7.json",
        "title": "MiMo-V2.6-Flash-RL vs MiniMax-M2.7",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minimax-m2-7"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-minimax-m2-7.json",
        "title": "MiniCPM-o 4.5 vs MiniMax-M2.7",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-minimax-m2-7"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-minimax-m2-7.json",
        "title": "MiniCPM-V 4.7 1B vs MiniMax-M2.7",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-minimax-m2-7"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-minimax-m2-7.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs MiniMax-M2.7",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-minimax-m2-7"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-minimax-m2-7.json",
        "title": "MiniCPM5-2B vs MiniMax-M2.7",
        "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-minimax-m2-7"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-minimax-m2-7.json",
        "title": "MiniMax-M2.5 vs MiniMax-M2.7",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-minimax-m2-7"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-minimax-m3-1-flash.json",
        "title": "MiniMax-M2.7 vs MiniMax-M3.1-Flash",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-minimax-m3-1-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-mistral-large-4.json",
        "title": "MiniMax-M2.7 vs Mistral Large 4",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-mistral-large-4"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-muse-glimmer-30b.json",
        "title": "MiniMax-M2.7 vs Muse Glimmer 30B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-muse-glimmer-30b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-nanbeige-4-2-3b.json",
        "title": "MiniMax-M2.7 vs Nanbeige4.2-3B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-nanbeige-4-2-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-nanbeige-4-3-33b.json",
        "title": "MiniMax-M2.7 vs Nanbeige4.3-33B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-nanbeige-4-3-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-5-bolt-e4b-4gb.json",
        "title": "MiniMax-M2.7 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-5-bolt-e4b-4gb"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-5-lightning-30b-a3b.json",
        "title": "MiniMax-M2.7 vs Nemotron 3.5 Lightning 30B A3B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-5-lightning-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-nano-30b-a3b.json",
        "title": "MiniMax-M2.7 vs Nemotron 3 Nano 30B A3B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-nano-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-nano-4b.json",
        "title": "MiniMax-M2.7 vs Nemotron 3 Nano 4B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-nano-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "MiniMax-M2.7 vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-super-120b-a12b.json",
        "title": "MiniMax-M2.7 vs Nemotron 3 Super 120B A12B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-super-120b-a12b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-ultra-550b-a55b.json",
        "title": "MiniMax-M2.7 vs Nemotron 3 Ultra 550B A55B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-ultra-550b-a55b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-north-mini-code-1-0.json",
        "title": "MiniMax-M2.7 vs North Mini Code 1.0",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-north-mini-code-1-0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-occamy-1-0.json",
        "title": "MiniMax-M2.7 vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-occamy-1-0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-olmo-3-7b-think.json",
        "title": "MiniMax-M2.7 vs Olmo 3 7B Think",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-olmo-3-7b-think"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-ornith-1-5-122b.json",
        "title": "MiniMax-M2.7 vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-ornith-1-5-35b-a3b.json",
        "title": "MiniMax-M2.7 vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-ornith-1-5-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-ornith-1-5-397b.json",
        "title": "MiniMax-M2.7 vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-ornith-1-5-397b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-ornith-1-5-9b.json",
        "title": "MiniMax-M2.7 vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-ornith-1-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-otel-2-0-llm-31b-it.json",
        "title": "MiniMax-M2.7 vs OTel-2.0-LLM-31B-IT",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-otel-2-0-llm-31b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen-agentworld-35b-a3b.json",
        "title": "MiniMax-M2.7 vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-30b-a3b-instruct-2507.json",
        "title": "MiniMax-M2.7 vs Qwen3-30B-A3B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-30b-a3b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-4b-instruct-2507.json",
        "title": "MiniMax-M2.7 vs Qwen3-4B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-4b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-0-8b.json",
        "title": "MiniMax-M2.7 vs Qwen3.5-0.8B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-0-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-122b-a10b.json",
        "title": "MiniMax-M2.7 vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-122b-a10b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-2b.json",
        "title": "MiniMax-M2.7 vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-397b-a17b.json",
        "title": "MiniMax-M2.7 vs Qwen3.5-397B-A17B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-397b-a17b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-4b.json",
        "title": "MiniMax-M2.7 vs Qwen3.5-4B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-9b.json",
        "title": "MiniMax-M2.7 vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-6-27b.json",
        "title": "MiniMax-M2.7 vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-6-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-6-35b-a3b.json",
        "title": "MiniMax-M2.7 vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-6-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-8-27b.json",
        "title": "MiniMax-M2.7 vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-8-flash-next.json",
        "title": "MiniMax-M2.7 vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "MiniMax-M2.7 vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-coder-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-coder-next.json",
        "title": "MiniMax-M2.7 vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "MiniMax-M2.7 vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-omni-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-2b-instruct.json",
        "title": "MiniMax-M2.7 vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-2b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "MiniMax-M2.7 vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-32b-instruct.json",
        "title": "MiniMax-M2.7 vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-4b-instruct.json",
        "title": "MiniMax-M2.7 vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-8b-instruct.json",
        "title": "MiniMax-M2.7 vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwopus3-8-27b-flash-v2.json",
        "title": "MiniMax-M2.7 vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-recursive-flint.json",
        "title": "MiniMax-M2.7 vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-ripple-65b-preview.json",
        "title": "MiniMax-M2.7 vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-sensenova-loop-9b-preview.json",
        "title": "MiniMax-M2.7 vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-sensenova-loop-9b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-smollm3-3b.json",
        "title": "MiniMax-M2.7 vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-spark-x2-6-4b.json",
        "title": "MiniMax-M2.7 vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-step-3-7-flash.json",
        "title": "MiniMax-M2.7 vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-swift-1-5-qwen3-8-27b.json",
        "title": "MiniMax-M2.7 vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "MiniMax-M2.7 vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-thinkingcap-qwen3-8-27b.json",
        "title": "MiniMax-M2.7 vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-unlimited-ocr-2.json",
        "title": "MiniMax-M2.7 vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-wevisone-vl-2b.json",
        "title": "MiniMax-M2.7 vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-wevisone-vl-4b.json",
        "title": "MiniMax-M2.7 vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-zaya1-8b.json",
        "title": "MiniMax-M2.7 vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-zaya1-8b"
      }
    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 30,
        "edge": "granite-4-2-30b",
        "granite-4-2-30b": 90,
        "key": "reach",
        "minimax-m2-7": 60,
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 25,
        "edge": "granite-4-2-30b",
        "granite-4-2-30b": 55,
        "key": "risk",
        "minimax-m2-7": 30,
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 10,
        "edge": "granite-4-2-30b",
        "granite-4-2-30b": 85,
        "key": "interface",
        "minimax-m2-7": 75,
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 45,
        "edge": "granite-4-2-30b",
        "granite-4-2-30b": 87,
        "key": "upkeep",
        "minimax-m2-7": 42,
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "Granite 4.2 30B scores 61 (C) on agent readiness against MiniMax-M2.7's 57.6 (C), and leads in every scored category. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "granite-4-2-30b": "IBM's largest reasoning cousin, with the same strengths as the 3B. It runs in every runtime we count from IBM's own builds on a 24 GB card, is signed, plain Apache 2.0, and comes with a disclosure of its training data. No safety results are published and structured output isn't documented.",
      "minimax-m2-7": "A 230B mixture-of-experts model for coding and agent harnesses, with parallel tool calls, about 200K of context and parsers in vLLM and SGLang. It needs a 4 GPU server for FP8 or about 166 GB for a 4-bit library build, and it always thinks. The main risk is the licence, switched to non-commercial two days after release."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-minimax-m2-7",
    "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-minimax-m2-7.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-minimax-m2-7.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-minimax-m2-7.min.md"
  },
  "markdown": "Granite 4.2 30B scores 61 (C) on agent readiness against MiniMax-M2.7's 57.6 (C), and leads in every scored category. Both do open-weight models you run yourself.\n\n- Granite 4.2 30B: grade C, 61/100, rank #65 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/granite-4-2-30b.md · JSON https://www.anchorterminal.com/api/v1/tools/granite-4-2-30b.json\n- MiniMax-M2.7: grade C, 57.6/100, rank #106 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/minimax-m2-7.md · JSON https://www.anchorterminal.com/api/v1/tools/minimax-m2-7.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### Granite 4.2 30B (C)\n\nGood for: Agents that want a documented, signed, Apache 2.0 reasoning model with tool calling on a 24 GB card\n\nAhead on:\n- Reach, 90 against 60\n- Risk, 55 against 30\n- Agent interface, 85 against 75\n- Documentation and upkeep, 87 against 42\n\nWatch for: No safety results published for it\n\n### MiniMax-M2.7 (C)\n\nGood for: Researchers and personal projects with a 4 to 8 GPU server who want an open agent and coding model with parallel tool calls\n\nWatch for: Commercial use needs MiniMax's written authorisation since a licence swap on 11 April 2026, made without a note\n\n\n## Why the edge\n\n### Documentation and upkeep: Granite 4.2 30B +45\n\n- Granite 4.2 30B (87): Model card 17, Intended use (best for), limitations (ethical considerations and limitations), architecture, parameters (30B), context length (128K native) and 12 tested languages are on the card. Training data 20, IBM's disclosure file for the 30B lists the datasets used in pre-training and post-training as machine-readable metadata, and the card describes the three training stages. Last change 15, the last commit to the repository was on 4 September 2026, 67 days before this check. Responsiveness 20, Of the 4 discussions that aren't pull requests (28 August to 3 November 2026), 3 have a reply from an ibm-granite member. Version policy 5, Granite 4.1 30B stays in its own repository and 4.2 came out in new ones. File health 10, Config, template and card agree on the architecture, the 131,072-token context and the thinking switch. 17 + 20 + 15 + 20 + 5 + 10 = 87.\n- MiniMax-M2.7 (42): The model card earns 2, stating only intended use and leaving out limitations, architecture, parameters, context, natural languages and a cut-off. Training data earns nothing, since only the training process is described. The last change earns 20, a card commit on 26 October 2026 that renames a MiniMax Agent link to MiniMax Code, 9 days before this check; the files, template and code last changed on 20 April 2026. Responsiveness earns 10, with a MiniMax reply in 8 of 36 discussions, none since June. Version policy earns 5, since earlier and later models sit in their own repositories without dated notes. File health earns 5, because a report that it doesn't load in Transformers v5 waits without a reply.\n\n### Reach: Granite 4.2 30B +30\n\n- Granite 4.2 30B (90): Memory 25, IBM's own 4-bit MLX build granite-4.2-30b-q4-mlx is 16.47 GB, and 16.47 GB × 1.2 = 19.8 GB, which fits 24 GB but not 16 GB. Its Q4_0 GGUF is 16.58 GB and Ollama's library build granite4.2:30b 18 GB, the same band. Runtimes 25, Released versions run the granite architecture in llama.cpp (granite, b12242), vLLM (GraniteForCausalLM in the v0.33.0 registry), SGLang (granite.py at v0.5.23), Transformers (v5.22.2) and Ollama (its own granite4.2 library entry, 30b tag). Builds 15, IBM publishes FP8, MXFP4 and NVFP4 safetensors builds, a GGUF repository with Q4_0 to Q8_0 and BF16 files, and 4-bit, 8-bit and BF16 MLX builds, so quantised builds at two precisions or more. Terms 15, Apache 2.0, an OSI-approved licence, with no use policy or other terms added on the card. Download 10, the Hub API reports gated false for the BF16, FP8, MXFP4, NVFP4, GGUF and MLX repositories, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps one inference provider (DeepInfra, live), short of the two the line needs. 25 + 25 + 15 + 15 + 10 + 0 = 90.\n- MiniMax-M2.7 (60): Memory earns 5. MiniMax only publishes its 230 GB FP8 release, and the smallest build of 4-bit or better from a runtime's own library is LM Studio's Q4_K_M GGUF at 138.3 GB, about 166 GB with the fifth added, so it needs more than our 107.4 GB machine and fits within 256 GB. Runtimes earn the full 25, with model code in released vLLM, SGLang, llama.cpp and Transformers, an LM Studio catalogue entry, and Ollama 0.40.14, whose pinned llama.cpp b11963 has the minimax-m2 architecture (capped at 25). Builds earn 10 for one publisher precision, the FP8 release. Terms earn 5, since the licence today allows only personal, research and non-profit use without MiniMax's written authorisation. Download earns 10, with no gate or account. Hosted earns 5, since Novita, Featherless AI and DeepInfra serve it through Hugging Face, while MiniMax's own API is closed to new users and retires it on 18 November 2026.\n\n### Risk: Granite 4.2 30B +25\n\n- Granite 4.2 30B (55): File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the ibm-granite organisation, which IBM's Granite docs and the GitHub README link to (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions. Licence risk 15, Apache 2.0 has no use policy, field-of-use limit, threshold or change clause, and the card's licence link resolves. Security programme 5, www.ibm.com/.well-known/security.txt names IBM PSIRT, read for the 3B listing on 4 November 2026 (5). 15 + 15 + 0 + 5 + 15 + 5 = 55.\n- MiniMax-M2.7 (30): File safety earns 15, safetensors only with no pickle files, less 10 because every documented loading route passes trust_remote_code for the repository's Python. Provenance earns the full 15, files in the organisation MiniMax links to with a SHA-256 for each shard. Safety evaluation and agent guidance earn nothing, as MiniMax publishes neither. Licence risk earns nothing (read 21 October 2026). The Prohibited Uses appendix goes beyond the law, MiniMax swapped a commercial licence for a non-commercial one two days after release, and neither that swap nor the amendment three days later carries a dated note. The security programme earns nothing, with no security.txt, disclosure policy, SECURITY.md or advisories.\n\n### Agent interface: Granite 4.2 30B +10\n\n- Granite 4.2 30B (85): Chat template 20, chat_template.jinja in the repository, with roles, tools, tool responses, the thinking switch and low effort. Tool-calling format 25, the card has tool-calling sections for vLLM and SGLang, declares tools with OpenAI's function schema and names the parser to use, qwen3_coder, which both runtimes ship in the released versions read. Trained tool calling 20, the template loops over every entry in an assistant turn's tool_calls, so it renders several calls in one turn, and the card names tool calling and agentic workflows as what it's best for. Structured output 0, the card doesn't document JSON or JSON Schema output or how to ask for it. Reasoning control 10, the card documents thinking on by default, non-thinking and low-effort modes per request through the template (enable_thinking and low_effort), with the reasoning returned apart from the answer. Context 10, 131,072 tokens natively per the card and config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85.\n- MiniMax-M2.7 (75): The chat template earns 20 for chat_template.jinja in the repository. The tool-calling format earns 25, as MiniMax's own guide documents how tools go in and how calls come back, gives a parsing function, and its deployment guides name the vLLM and SGLang parsers. Trained tool calling earns 20, with several invoke blocks in one call. Structured output earns nothing, since JSON output isn't documented or claimed. Reasoning control earns nothing, because thinking is always on with no documented way to turn it off or budget it. Context earns 10 for 204,800 tokens in the config with no position scaling.\n\n## Score by category\n\n| Category | Weight | Granite 4.2 30B | MiniMax-M2.7 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 90 | 60 | Granite 4.2 30B +30 |\n| Risk | 20% (20 this run) | 55 | 30 | Granite 4.2 30B +25 |\n| Agent interface | 10% (10 this run) | 85 | 75 | Granite 4.2 30B +10 |\n| Documentation and upkeep | 10% (10 this run) | 87 | 42 | Granite 4.2 30B +45 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **57.6 · C** | |\n\n## Facts side by side\n\n| Fact | Granite 4.2 30B | MiniMax-M2.7 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | IBM | MiniMax |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports |  |  |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 | MiniMax M2.7 non-commercial licence (MIT-style terms, commercial use needs MiniMax's written authorisation) |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-09-04 | 2026-04-20 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | none | 1.8/5 (4) |\n\n## How the scores have moved\n\n### Granite 4.2 30B\n\nNo score change since it was listed.\n\n### MiniMax-M2.7\n\n- +1.5 on 4 November 2026: product\n\n## Verdicts\n\n**Granite 4.2 30B.** IBM's largest reasoning cousin, with the same strengths as the 3B. It runs in every runtime we count from IBM's own builds on a 24 GB card, is signed, plain Apache 2.0, and comes with a disclosure of its training data. No safety results are published and structured output isn't documented.\n\n**MiniMax-M2.7.** A 230B mixture-of-experts model for coding and agent harnesses, with parallel tool calls, about 200K of context and parsers in vLLM and SGLang. It needs a 4 GPU server for FP8 or about 166 GB for a 4-bit library build, and it always thinks. The main risk is the licence, switched to non-commercial two days after release.\n\n## Before you call either\n\n### Granite 4.2 30B\n\n1. Start it with `ollama run granite4.2:30b`, or `llama-server -hf ibm-granite/granite-4.2-30b-GGUF:Q4_K_M --jinja`\n2. With vLLM 0.32.0 or later leave out `--reasoning-parser-plugin`, since the parser is built in, and use `--tool-call-parser qwen3_coder`\n3. Thinking is on by default. Pass `enable_thinking` false to turn it off or `low_effort` true for short reasoning\n4. The context is 131,072 tokens. The card's 512K extension isn't documented\n5. Validate JSON output yourself, since structured output isn't documented\n6. IBM recommends Granite Guardian beside it to flag risky inputs and outputs\n\n### MiniMax-M2.7\n\n1. Check the use is personal, research or non-profit before starting it; anything commercial needs MiniMax's written authorisation, and commercial users must show Built with MiniMax M2.7\n2. Start it with vLLM on 4 GPUs of 96 GB, SAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.7 --trust-remote-code --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 --reasoning-parser minimax_m2_append_think\n3. In SGLang use --tool-call-parser minimax-m2 and --reasoning-parser minimax-append-think, with --trust-remote-code\n4. MiniMax's guides budget 220 GB for the weights and 240 GB per 1M tokens of KV cache, with 196K the most per sequence\n5. On one large-memory machine, LM Studio's catalogue entry minimax/minimax-m2.7 lists a 138 GB Q4_K_M GGUF and states 138 GB minimum memory\n6. Sample with temperature 1.0, top_p 0.95 and top_k 40, as the card recommends, and budget output tokens for thinking on every turn\n7. Pin Transformers 4.57.1 as MiniMax's guide does; a report that it fails on Transformers v5 has no answer\n8. Without the hardware, call it through Novita, Featherless AI or DeepInfra; MiniMax's own API retires M2.7 on 18 November 2026\n\n## Questions\n\n### Which is better for AI agents, Granite 4.2 30B or MiniMax-M2.7?\n\nGranite 4.2 30B scores 61 (C) on agent readiness against MiniMax-M2.7's 57.6 (C), and leads in every scored category.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/granite-4-2-30b-vs-minimax-m2-7.json, and with the fewest tokens: https://www.anchorterminal.com/compare/granite-4-2-30b-vs-minimax-m2-7.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"granite-4-2-30b\", \"b\": \"minimax-m2-7\"}`. From a terminal: `anchor compare granite-4-2-30b minimax-m2-7`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/granite-4-2-30b.json and https://www.anchorterminal.com/api/v1/tools/minimax-m2-7.json\n\n## Other comparisons with Granite 4.2 30B or MiniMax-M2.7\n\n- [Agents-A1-4B vs Granite 4.2 30B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-granite-4-2-30b.md)\n- [Agents-A1-4B vs MiniMax-M2.7](https://www.anchorterminal.com/compare/agents-a1-4b-vs-minimax-m2-7.md)\n- [Apertus v1.5 8B vs Granite 4.2 30B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-granite-4-2-30b.md)\n- [Apertus v1.5 8B vs MiniMax-M2.7](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minimax-m2-7.md)\n- [Apodex-1.1-mini vs Granite 4.2 30B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-granite-4-2-30b.md)\n- [Apodex-1.1-mini vs MiniMax-M2.7](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minimax-m2-7.md)\n- [Arete-27B vs Granite 4.2 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    "title": "Granite 4.2 30B vs MiniMax-M2.7 for AI agents (2026) | Anchor Terminal",
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