{
  "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 Kimi K3.1 Pro's 48.6 (D), and leads in every scored category.",
    "b": {
      "slug": "kimi-k3-1-pro",
      "name": "Kimi K3.1 Pro",
      "vendor": "Moonshot AI",
      "vendorUrl": "https://www.moonshot.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Kimi K3.1 Pro is Moonshot AI's open-weight multimodal mixture-of-experts model, post-trained from Kimi K3, with 2.8T parameters, 104B active and a 1M-token context, published in MXFP4 under the modified-MIT Kimi K3.1 Pro Licence.",
      "url": "https://www.anchorterminal.com/tools/kimi-k3-1-pro",
      "markdownUrl": "https://www.anchorterminal.com/tools/kimi-k3-1-pro.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/kimi-k3-1-pro.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/kimi-k3-1-pro.json",
      "dns": "kimi-k3-1-pro.grade.anchorterminal.com",
      "repo": "https://huggingface.co/moonshotai/Kimi-K3.1-Pro",
      "license": "Kimi K3.1 Pro License (modified MIT)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "moonshotai/Kimi-K3.1-Pro"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under the Kimi K3.1 Pro Licence. A company running a model-as-a-service business with more than $20 million in revenue over 12 months needs a separate agreement with Moonshot AI, at a price not published. Running it yourself costs the hardware, 1,561 GB of files (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/moonshotai/Kimi-K3.1-Pro",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "reasoning",
        "tool-calling",
        "modified-mit",
        "safetensors",
        "mxfp4",
        "self-hosted"
      ],
      "lastRelease": "2026-11-05",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 48.6,
        "grade": "D",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 109,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 65,
          "reach": 40,
          "risk": 50,
          "upkeep": 51
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "Moonshot's newest flagship, eight days old when read, for operators with a rack. Only llama.cpp has its model code in a release, with vLLM and SGLang support still on a release candidate and a branch. The licence is permissive below a revenue threshold, and Moonshot hasn't answered a discussion on the repository yet.",
        "bestFor": "Operators with a multi-accelerator node who want Moonshot's newest open-weight model for coding and long agent sessions, and everyone else through an API",
        "strengths": [
          "Published weights for a model Moonshot calls its most capable, with a 1M-token context and images in",
          "Free commercial use under a modified MIT licence, ungated, with a hash on every file",
          "Moonshot's API and Together serve the same model for agents without the hardware",
          "reasoning_effort per request and several tool calls a turn",
          "vLLM and SGLang both publish serving recipes for it"
        ],
        "weaknesses": [
          "1,561 GB of files, so it needs a node of eight large accelerators at least",
          "Only llama.cpp has the model code in a release, and no GGUF is published",
          "No chat template, and prompts are built by code from the repository",
          "No limitations, safety guidance or security contact on the card, and no independent safety evaluation yet",
          "None of the newest discussions has a reply from Moonshot"
        ],
        "agentNotes": [
          "Use Moonshot's API or Together unless you have a node of eight B300-class accelerators",
          "With vLLM use 0.34.0 or later and the recipe's flags, `--tool-call-parser kimi_k3 --reasoning-parser kimi_k3`",
          "Pass the whole assistant message back in later turns, reasoning_content and tool_calls included, as the card requires",
          "Set `reasoning_effort` to low, high or max per request. Max is the default and thinking can't be turned off",
          "Kimi K3's model code doesn't load it, because the attention layers changed",
          "Validate JSON output yourself, since the card doesn't document structured output for the weights"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "D",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 48.6
          }
        ],
        "editorialScores": {
          "interface": 65,
          "reach": 40,
          "risk": 50,
          "upkeep": 51
        },
        "provenanceScore": 70,
        "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": "# vLLM, per its Kimi-K3.1-Pro recipe linked from the card (vLLM 0.34.0 or later, two GB300 trays)\nvllm serve moonshotai/Kimi-K3.1-Pro --trust-remote-code \\\n  --tensor-parallel-size 8 --nnodes 2 --node-rank 0 --master-addr $HEAD_IP \\\n  --load-format fastsafetensors --enable-auto-tool-choice \\\n  --tool-call-parser kimi_k3 --reasoning-parser kimi_k3",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\n    \"model\": \"moonshotai/Kimi-K3.1-Pro\",\n    \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}],\n    \"reasoning_effort\": \"high\"\n  }'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/kimi-k3-1-pro"
      },
      "sameCompany": [
        "kimi-k3"
      ],
      "weights": {
        "trainedBy": "Moonshot AI",
        "builtOn": "Kimi K3, post-trained, per the card. Not declared in the metadata",
        "params": "2.8T",
        "active": "104B",
        "context": 1048576,
        "goodFor": "Large multimodal agent model for coding, research and long tool-calling sessions, text and images in, with always-on reasoning you can budget.",
        "runtimes": [
          "llama.cpp"
        ],
        "builds": [
          {
            "name": "Kimi-K3.1-Pro",
            "url": "https://huggingface.co/moonshotai/Kimi-K3.1-Pro",
            "who": "publisher",
            "precision": "mxfp4",
            "sizeGB": 1561.3,
            "memoryGB": 2048,
            "format": "safetensors",
            "note": "Trained in MXFP4, the only build Moonshot publishes. vLLM's recipe needs 0.34.0 or later"
          }
        ],
        "run": "vllm serve moonshotai/Kimi-K3.1-Pro --trust-remote-code --tensor-parallel-size 8 --nnodes 2 --node-rank 0 --master-addr $HEAD_IP --load-format fastsafetensors --enable-auto-tool-choice --tool-call-parser kimi_k3 --reasoning-parser kimi_k3",
        "runBuild": "Kimi-K3.1-Pro",
        "checked": "2026-11-10",
        "minMemoryGB": 2048
      },
      "family": {
        "lab": "Moonshot AI (Kimi)",
        "family": "Kimi K3.1",
        "url": "https://www.anchorterminal.com/open-weights/#moonshot",
        "olderCount": 18
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Moonshot AI (the licence names Moonshot AI; the Kimi Platform terms name Moonshot AI PTE. LTD.)",
        "domain": "moonshot.ai",
        "domainRegistered": "2017-12-15",
        "domainNote": "The .ai registry's RDAP server gave moonshot.ai a registration date of 15 December 2017, read 24 October 2026 for the Kimi K3 listing.",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/moonshotai/Kimi-K3.1-Pro/commits/main",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "The licence names Moonshot AI. The Kimi Platform's terms name Moonshot AI PTE. LTD. as the party for its API, as the Kimi K3 listing records.",
          "Nothing is hosted, so the Kimi K3.1 Pro Licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the repository's commit history, two commits, the first on 2 November 2026 and an update to encoding_k3.py on 5 November.",
          "No security.txt was found on moonshot.ai or kimi.com for the Kimi K3 listing on 24 October 2026. unchecked on 10 November."
        ],
        "score": 70
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/kimi-k3-1-pro.json"
    },
    "edges": [
      {
        "by": 50,
        "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 0, the only build is Moonshot's 1,561 GB MXFP4 release, far above 256 GB. Runtimes 5, llama.cpp b12242 alone has the model code in a release, since vLLM's recipe needs 0.34.0 and SGLang's cookbook a branch. Builds 10, one precision. Terms 10, free commercial use below a model-as-a-service revenue threshold. Download 10, not gated. Hosted 5, Moonshot's API and Together serve it. 0 + 5 + 10 + 10 + 10 + 5 = 40."
        },
        "scores": {
          "a": 90,
          "b": 40
        }
      },
      {
        "by": 36,
        "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": "Model card 11, no limitations, languages or cut-off. Training data 5, one general sentence. Last change 20, a commit on 5 November 2026. Responsiveness 0, none of the 8 newest discussions has a reply from Moonshot. Version policy 5, Kimi K3 keeps its repository and no dated note was found. File health 10, the files agree. 11 + 5 + 20 + 0 + 5 + 10 = 51."
        },
        "scores": {
          "a": 87,
          "b": 51
        }
      },
      {
        "by": 20,
        "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": "Chat template 10, the format is only code to run. Tool-calling format 10, the parser is named in the runtimes' recipes and the card doesn't say how tools are declared. Trained tool calling 20, several calls a turn on Kimi K3's tools channel. Structured output 5, named among linked guides without the how. Reasoning control 10, reasoning_effort per request. Context 10, 1M tokens. 10 + 10 + 20 + 5 + 10 + 10 = 65."
        },
        "scores": {
          "a": 85,
          "b": 65
        }
      },
      {
        "by": 5,
        "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 15, safetensors only, less 10 for remote code and prompts built by the repository's script. Provenance 15, Moonshot's own organisation with a hash on every file. Safety evaluation 10, Moonshot's own cyber benchmark results and nobody else's yet. Agent guidance 0, none on the card. Licence risk 10, the revenue threshold. Security programme 0, no way to report a flaw found. 15 + 15 + 10 + 0 + 10 + 0 = 50."
        },
        "scores": {
          "a": 55,
          "b": 50
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "IBM",
        "b": "Moonshot AI",
        "name": "Vendor"
      },
      {
        "a": "no (local only)",
        "b": "no (local only)",
        "name": "Hosted endpoint"
      },
      {
        "a": "",
        "b": "",
        "name": "Transports"
      },
      {
        "a": "None",
        "b": "None",
        "name": "Auth"
      },
      {
        "a": "Free",
        "b": "Free",
        "name": "Pricing"
      },
      {
        "a": "no",
        "b": "no",
        "name": "x402"
      },
      {
        "a": "Apache-2.0",
        "b": "Kimi K3.1 Pro License (modified MIT)",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-09-04",
        "b": "2026-11-05",
        "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": "Granite 4.2 30B scores 61 (C) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in every scored category.",
        "question": "Which is better for AI agents, Granite 4.2 30B or Kimi K3.1 Pro?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 90 against 40",
          "Risk, 55 against 50",
          "Agent interface, 85 against 65",
          "Documentation and upkeep, 87 against 51"
        ],
        "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": "Operators with a multi-accelerator node who want Moonshot's newest open-weight model for coding and long agent sessions, and everyone else through an API",
        "slug": "kimi-k3-1-pro",
        "watchFor": "1,561 GB of files, so it needs a node of eight large accelerators at least"
      }
    ],
    "history": null,
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
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        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nanbeige-4-2-3b.json",
        "title": "Kimi K3.1 Pro vs Nanbeige4.2-3B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nanbeige-4-2-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nanbeige-4-3-33b.json",
        "title": "Kimi K3.1 Pro vs Nanbeige4.3-33B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nanbeige-4-3-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-5-bolt-e4b-4gb.json",
        "title": "Kimi K3.1 Pro vs NVIDIA Nemotron 3.5 Bolt E4B 4GB",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-5-bolt-e4b-4gb"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-5-lightning-30b-a3b.json",
        "title": "Kimi K3.1 Pro vs Nemotron 3.5 Lightning 30B A3B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-5-lightning-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-nano-30b-a3b.json",
        "title": "Kimi K3.1 Pro vs Nemotron 3 Nano 30B A3B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-nano-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-nano-4b.json",
        "title": "Kimi K3.1 Pro vs Nemotron 3 Nano 4B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-nano-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "Kimi K3.1 Pro vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-super-120b-a12b.json",
        "title": "Kimi K3.1 Pro vs Nemotron 3 Super 120B A12B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-super-120b-a12b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-ultra-550b-a55b.json",
        "title": "Kimi K3.1 Pro vs Nemotron 3 Ultra 550B A55B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-ultra-550b-a55b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-north-mini-code-1-0.json",
        "title": "Kimi K3.1 Pro vs North Mini Code 1.0",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-north-mini-code-1-0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-occamy-1-0.json",
        "title": "Kimi K3.1 Pro vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-occamy-1-0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-olmo-3-7b-think.json",
        "title": "Kimi K3.1 Pro vs Olmo 3 7B Think",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-olmo-3-7b-think"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-ornith-1-5-122b.json",
        "title": "Kimi K3.1 Pro vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-ornith-1-5-35b-a3b.json",
        "title": "Kimi K3.1 Pro vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-ornith-1-5-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-ornith-1-5-397b.json",
        "title": "Kimi K3.1 Pro vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-ornith-1-5-397b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-ornith-1-5-9b.json",
        "title": "Kimi K3.1 Pro vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-ornith-1-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-otel-2-0-llm-31b-it.json",
        "title": "Kimi K3.1 Pro vs OTel-2.0-LLM-31B-IT",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-otel-2-0-llm-31b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen-agentworld-35b-a3b.json",
        "title": "Kimi K3.1 Pro vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-30b-a3b-instruct-2507.json",
        "title": "Kimi K3.1 Pro vs Qwen3-30B-A3B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-30b-a3b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-4b-instruct-2507.json",
        "title": "Kimi K3.1 Pro vs Qwen3-4B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-4b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-5-0-8b.json",
        "title": "Kimi K3.1 Pro vs Qwen3.5-0.8B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-5-0-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-5-122b-a10b.json",
        "title": "Kimi K3.1 Pro vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-5-122b-a10b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-5-2b.json",
        "title": "Kimi K3.1 Pro vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-5-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-5-397b-a17b.json",
        "title": "Kimi K3.1 Pro vs Qwen3.5-397B-A17B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-5-397b-a17b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-5-4b.json",
        "title": "Kimi K3.1 Pro vs Qwen3.5-4B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-5-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-5-9b.json",
        "title": "Kimi K3.1 Pro vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-6-27b.json",
        "title": "Kimi K3.1 Pro vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-6-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-6-35b-a3b.json",
        "title": "Kimi K3.1 Pro vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-6-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-8-27b.json",
        "title": "Kimi K3.1 Pro vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-8-flash-next.json",
        "title": "Kimi K3.1 Pro vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "Kimi K3.1 Pro vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-coder-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-coder-next.json",
        "title": "Kimi K3.1 Pro vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Kimi K3.1 Pro vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-omni-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-2b-instruct.json",
        "title": "Kimi K3.1 Pro vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-2b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Kimi K3.1 Pro vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-32b-instruct.json",
        "title": "Kimi K3.1 Pro vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-4b-instruct.json",
        "title": "Kimi K3.1 Pro vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-8b-instruct.json",
        "title": "Kimi K3.1 Pro vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Kimi K3.1 Pro vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-recursive-flint.json",
        "title": "Kimi K3.1 Pro vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-ripple-65b-preview.json",
        "title": "Kimi K3.1 Pro vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-sensenova-loop-9b-preview.json",
        "title": "Kimi K3.1 Pro vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-sensenova-loop-9b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-smollm3-3b.json",
        "title": "Kimi K3.1 Pro vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-spark-x2-6-4b.json",
        "title": "Kimi K3.1 Pro vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-spark-x2-6-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-step-3-7-flash.json",
        "title": "Kimi K3.1 Pro vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Kimi K3.1 Pro vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Kimi K3.1 Pro vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Kimi K3.1 Pro vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-unlimited-ocr-2.json",
        "title": "Kimi K3.1 Pro vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-wevisone-vl-2b.json",
        "title": "Kimi K3.1 Pro vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-wevisone-vl-4b.json",
        "title": "Kimi K3.1 Pro vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-zaya1-8b.json",
        "title": "Kimi K3.1 Pro vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-zaya1-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-kimi-k3-1-pro.json",
        "title": "Kimi K3 vs Kimi K3.1 Pro",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-kimi-k3-1-pro"
      }
    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 50,
        "edge": "granite-4-2-30b",
        "granite-4-2-30b": 90,
        "key": "reach",
        "kimi-k3-1-pro": 40,
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 5,
        "edge": "granite-4-2-30b",
        "granite-4-2-30b": 55,
        "key": "risk",
        "kimi-k3-1-pro": 50,
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 20,
        "edge": "granite-4-2-30b",
        "granite-4-2-30b": 85,
        "key": "interface",
        "kimi-k3-1-pro": 65,
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 36,
        "edge": "granite-4-2-30b",
        "granite-4-2-30b": 87,
        "key": "upkeep",
        "kimi-k3-1-pro": 51,
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "Granite 4.2 30B scores 61 (C) on agent readiness against Kimi K3.1 Pro's 48.6 (D), 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.",
      "kimi-k3-1-pro": "Moonshot's newest flagship, eight days old when read, for operators with a rack. Only llama.cpp has its model code in a release, with vLLM and SGLang support still on a release candidate and a branch. The licence is permissive below a revenue threshold, and Moonshot hasn't answered a discussion on the repository yet."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-kimi-k3-1-pro",
    "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-kimi-k3-1-pro.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-kimi-k3-1-pro.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-kimi-k3-1-pro.min.md"
  },
  "markdown": "Granite 4.2 30B scores 61 (C) on agent readiness against Kimi K3.1 Pro's 48.6 (D), 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- Kimi K3.1 Pro: grade D, 48.6/100, rank #151 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/kimi-k3-1-pro.md · JSON https://www.anchorterminal.com/api/v1/tools/kimi-k3-1-pro.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 40\n- Risk, 55 against 50\n- Agent interface, 85 against 65\n- Documentation and upkeep, 87 against 51\n\nWatch for: No safety results published for it\n\n### Kimi K3.1 Pro (D)\n\nGood for: Operators with a multi-accelerator node who want Moonshot's newest open-weight model for coding and long agent sessions, and everyone else through an API\n\nWatch for: 1,561 GB of files, so it needs a node of eight large accelerators at least\n\n\n## Why the edge\n\n### Reach: Granite 4.2 30B +50\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- Kimi K3.1 Pro (40): Memory 0, the only build is Moonshot's 1,561 GB MXFP4 release, far above 256 GB. Runtimes 5, llama.cpp b12242 alone has the model code in a release, since vLLM's recipe needs 0.34.0 and SGLang's cookbook a branch. Builds 10, one precision. Terms 10, free commercial use below a model-as-a-service revenue threshold. Download 10, not gated. Hosted 5, Moonshot's API and Together serve it. 0 + 5 + 10 + 10 + 10 + 5 = 40.\n\n### Documentation and upkeep: Granite 4.2 30B +36\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- Kimi K3.1 Pro (51): Model card 11, no limitations, languages or cut-off. Training data 5, one general sentence. Last change 20, a commit on 5 November 2026. Responsiveness 0, none of the 8 newest discussions has a reply from Moonshot. Version policy 5, Kimi K3 keeps its repository and no dated note was found. File health 10, the files agree. 11 + 5 + 20 + 0 + 5 + 10 = 51.\n\n### Agent interface: Granite 4.2 30B +20\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- Kimi K3.1 Pro (65): Chat template 10, the format is only code to run. Tool-calling format 10, the parser is named in the runtimes' recipes and the card doesn't say how tools are declared. Trained tool calling 20, several calls a turn on Kimi K3's tools channel. Structured output 5, named among linked guides without the how. Reasoning control 10, reasoning_effort per request. Context 10, 1M tokens. 10 + 10 + 20 + 5 + 10 + 10 = 65.\n\n### Risk: Granite 4.2 30B +5\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- Kimi K3.1 Pro (50): File safety 15, safetensors only, less 10 for remote code and prompts built by the repository's script. Provenance 15, Moonshot's own organisation with a hash on every file. Safety evaluation 10, Moonshot's own cyber benchmark results and nobody else's yet. Agent guidance 0, none on the card. Licence risk 10, the revenue threshold. Security programme 0, no way to report a flaw found. 15 + 15 + 10 + 0 + 10 + 0 = 50.\n\n## Score by category\n\n| Category | Weight | Granite 4.2 30B | Kimi K3.1 Pro | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 90 | 40 | Granite 4.2 30B +50 |\n| Risk | 20% (20 this run) | 55 | 50 | Granite 4.2 30B +5 |\n| Agent interface | 10% (10 this run) | 85 | 65 | Granite 4.2 30B +20 |\n| Documentation and upkeep | 10% (10 this run) | 87 | 51 | Granite 4.2 30B +36 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **48.6 · D** | |\n\n## Facts side by side\n\n| Fact | Granite 4.2 30B | Kimi K3.1 Pro |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | IBM | Moonshot AI |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports |  |  |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 | Kimi K3.1 Pro License (modified MIT) |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-09-04 | 2026-11-05 |\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**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**Kimi K3.1 Pro.** Moonshot's newest flagship, eight days old when read, for operators with a rack. Only llama.cpp has its model code in a release, with vLLM and SGLang support still on a release candidate and a branch. The licence is permissive below a revenue threshold, and Moonshot hasn't answered a discussion on the repository yet.\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### Kimi K3.1 Pro\n\n1. Use Moonshot's API or Together unless you have a node of eight B300-class accelerators\n2. With vLLM use 0.34.0 or later and the recipe's flags, `--tool-call-parser kimi_k3 --reasoning-parser kimi_k3`\n3. Pass the whole assistant message back in later turns, reasoning_content and tool_calls included, as the card requires\n4. Set `reasoning_effort` to low, high or max per request. Max is the default and thinking can't be turned off\n5. Kimi K3's model code doesn't load it, because the attention layers changed\n6. Validate JSON output yourself, since the card doesn't document structured output for the weights\n\n## Questions\n\n### Which is better for AI agents, Granite 4.2 30B or Kimi K3.1 Pro?\n\nGranite 4.2 30B scores 61 (C) on agent readiness against Kimi K3.1 Pro's 48.6 (D), 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-kimi-k3-1-pro.json, and with the fewest tokens: https://www.anchorterminal.com/compare/granite-4-2-30b-vs-kimi-k3-1-pro.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"granite-4-2-30b\", \"b\": \"kimi-k3-1-pro\"}`. 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    "title": "Granite 4.2 30B vs Kimi K3.1 Pro for AI agents (2026)",
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