{
  "data": {
    "a": {
      "slug": "granite-4-2-3b",
      "name": "Granite 4.2 3B",
      "vendor": "IBM",
      "vendorUrl": "https://www.ibm.com/granite",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Granite 4.2 3B is IBM's open-weight dense reasoning model of 3.7B parameters, post-trained from Granite 4.1 3B 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-4-2-3b",
      "markdownUrl": "https://www.anchorterminal.com/tools/granite-4-2-3b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/granite-4-2-3b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/granite-4-2-3b.json",
      "dns": "granite-4-2-3b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/ibm-granite/granite-4.2-3b",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "ibm-granite/granite-4.2-3b"
        },
        {
          "registry": "huggingface",
          "name": "ibm-granite/granite-4.2-3b-GGUF"
        },
        {
          "registry": "huggingface",
          "name": "ibm-granite/granite-4.2-3b-fp8"
        },
        {
          "registry": "huggingface",
          "name": "ibm-granite/granite-4.2-3b-mxfp4"
        },
        {
          "registry": "huggingface",
          "name": "ibm-granite/granite-4.2-3b-nvfp4"
        },
        {
          "registry": "huggingface",
          "name": "ibm-granite/granite-4.2-3b-q4-mlx"
        },
        {
          "registry": "ollama",
          "name": "granite4.2:3b"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of IBM's 4.2 3B repositories is gated (checked 2026-10-21). The files carry a sigstore signature (model.sig) that IBM documents how to verify.",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. watsonx.ai lists Granite 4.1 models but not 4.2, and DeepInfra is the only inference provider Hugging Face maps for it. Running it yourself costs the hardware, 7.3 GB for the BF16 files and 2.2 GB for Ollama's Q4_K_M build (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/ibm-granite/granite-4.2-3b",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "small",
        "reasoning",
        "tool-calling",
        "multilingual",
        "apache-2.0",
        "safetensors",
        "fp8",
        "fp4",
        "gguf",
        "mlx",
        "signed",
        "self-hosted"
      ],
      "lastRelease": "2026-10-20",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 59.8,
        "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": 72,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "capability": 21,
          "interface": 85,
          "reach": 95,
          "risk": 55,
          "upkeep": 92
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-11-04"
        },
        "negative": 0,
        "verdict": "A small reasoning model for tool calls, coding and multilingual chat, published by IBM under plain Apache 2.0 with signed files and a dataset disclosure, and run by llama.cpp, Ollama, vLLM, SGLang and Transformers from a 2.2 GB Ollama build. Thinking turns off or shortens per request. It has no published safety results and no documented JSON mode.",
        "bestFor": "Agents on small hardware that want a permissive reasoning model for tool calls and coding, with thinking they can switch off",
        "strengths": [
          "Apache 2.0 with nothing added, ungated, signed with sigstore in IBM's own organisation",
          "2.2 GB as Ollama's Q4_K_M build, with IBM's own FP8, FP4, GGUF and MLX builds",
          "Runs in llama.cpp, Ollama, vLLM, SGLang and Transformers",
          "Tool format and parser documented for vLLM and SGLang, with several calls a turn",
          "Thinking that turns off or runs at low effort per request",
          "A machine-readable disclosure of the training datasets with sources and sizes"
        ],
        "weaknesses": [
          "No safety results published for 4.2, and only general safety cautions",
          "No documented JSON or schema output",
          "The card's vLLM route loads a parser plugin from the repository",
          "Only one inference provider, and watsonx.ai doesn't list 4.2",
          "No knowledge cut-off, and Granite 4.1 3B carries no note that 4.2 replaced it"
        ],
        "agentNotes": [
          "Start it with `ollama run granite4.2:3b`; set options.num_ctx in the request, since Ollama's default context is short and the model takes up to 131,072",
          "With vLLM 0.32.0 or later, serve `ibm-granite/granite-4.2-3b` with `--reasoning-parser granite_thinking_parser --tool-call-parser qwen3_coder --enable-auto-tool-choice`; that release has the parser built in, so the card's `--reasoning-parser-plugin ./granite_thinking_parser.py` isn't needed",
          "With SGLang 0.5.18 or later, `--reasoning-parser auto --tool-call-parser auto` resolve to IBM's recommended parsers, and the reasoning comes back in reasoning_content",
          "Use temperature 1.0 and top_p 0.95 for every mode, as IBM requires",
          "Pass `enable_thinking` false for direct answers or `low_effort` true for short reasoning, through chat_template_kwargs on an OpenAI-compatible server",
          "Earlier turns' thinking is stripped by default (`truncate_history_thinking`), so pass it false to keep reasoning in multi-turn history",
          "For JSON, use the runtime's guided decoding, since IBM documents no JSON mode for 4.2",
          "Verify the files with `python -m model_signing verify sigstore` and IBM's identity, per IBM's signature guide"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 3.8,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 59.8
          }
        ],
        "editorialScores": {
          "capability": 21,
          "interface": 85,
          "reach": 95,
          "risk": 55,
          "upkeep": 92
        },
        "provenanceScore": 88,
        "checked": {
          "categories": {
            "capability": "2026-11-04",
            "interface": "2026-11-04",
            "reach": "2026-11-04",
            "risk": "2026-11-04",
            "upkeep": "2026-11-04"
          },
          "last": "2026-11-04",
          "oldest": "2026-11-04",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama run granite4.2:3b\n# or, with vLLM 0.32.0 or later:\nvllm serve ibm-granite/granite-4.2-3b --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\": \"granite4.2:3b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/granite-4-2-3b"
      },
      "sameCompany": [
        "granite-embedding-small-english-r2",
        "granite-guardian",
        "granite-speech-5-0-2b",
        "ibm-watson-stt",
        "granite-switch",
        "granite-4-2-30b",
        "granite-4-2-8b"
      ],
      "weights": {
        "trainedBy": "IBM",
        "builtOn": "ibm-granite/granite-4.1-3b-base (IBM's own base checkpoint, post-trained; named on the card and in its metadata)",
        "params": "3.66B",
        "context": 131072,
        "goodFor": "Small dense reasoning model for tool calling, coding and multilingual chat, with thinking that is on, off or short per request.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "granite-4.2-3b-fp8",
            "url": "https://huggingface.co/ibm-granite/granite-4.2-3b-fp8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 4.19,
            "memoryGB": 8,
            "format": "safetensors",
            "runsOn": [
              "vLLM"
            ]
          },
          {
            "name": "granite-4.2-3b-nvfp4",
            "url": "https://huggingface.co/ibm-granite/granite-4.2-3b-nvfp4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 2.81,
            "memoryGB": 8,
            "format": "safetensors",
            "runsOn": [
              "vLLM"
            ],
            "note": "GPTQ calibrated with LLM Compressor, per IBM's blog"
          },
          {
            "name": "granite-4.2-3b-mxfp4",
            "url": "https://huggingface.co/ibm-granite/granite-4.2-3b-mxfp4",
            "who": "publisher",
            "precision": "mxfp4",
            "sizeGB": 2.71,
            "memoryGB": 8,
            "format": "safetensors",
            "runsOn": [
              "vLLM"
            ],
            "note": "GPTQ calibrated with LLM Compressor, per IBM's blog"
          },
          {
            "name": "granite-4.2-3b-Q8_0",
            "url": "https://huggingface.co/ibm-granite/granite-4.2-3b-GGUF/blob/main/granite-4.2-3b-Q8_0.gguf",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 3.89,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ]
          },
          {
            "name": "granite-4.2-3b-Q4_K_M",
            "url": "https://huggingface.co/ibm-granite/granite-4.2-3b-GGUF/blob/main/granite-4.2-3b-Q4_K_M.gguf",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 2.24,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "IBM's GGUF repository also holds Q2_K to Q6_K, Q4_0, Q4_1, Q5_0, Q5_1 and BF16 files; Q4_0 is the smallest 4-bit GGUF at 2.13 GB"
          },
          {
            "name": "granite-4.2-3b-q4-mlx",
            "url": "https://huggingface.co/ibm-granite/granite-4.2-3b-q4-mlx",
            "who": "publisher",
            "precision": "q4",
            "sizeGB": 2.07,
            "memoryGB": 8,
            "format": "mlx",
            "runsOn": [
              "mlx-lm"
            ],
            "note": "The smallest build at 4-bit or better. IBM also publishes q8-mlx (3.90 GB) and bf16-mlx (7.33 GB)"
          },
          {
            "name": "ollama granite4.2:3b",
            "url": "https://ollama.com/library/granite4.2:3b",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 2.2,
            "memoryGB": 8,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Q4_K_M, 3.66B parameters, granite architecture, per Ollama's model page (digest 40577dc168a3). Size as Ollama shows it"
          },
          {
            "name": "granite-4.2-3b",
            "url": "https://huggingface.co/ibm-granite/granite-4.2-3b",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 7.33,
            "memoryGB": 12,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang",
              "Transformers"
            ]
          }
        ],
        "run": "ollama run granite4.2:3b",
        "runBuild": "ollama granite4.2:3b",
        "checked": "2026-10-21",
        "minMemoryGB": 8
      },
      "family": {
        "lab": "IBM (Granite)",
        "family": "Granite 4 (4.0, 4.1, 4.2)",
        "url": "https://www.anchorterminal.com/open-weights/#ibm",
        "others": [
          {
            "name": "granite-4.2-8b",
            "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-4.2-30b",
            "url": "https://huggingface.co/ibm-granite/granite-4.2-30b",
            "params": "30B (Hub safetensors count 29.28B)",
            "listing": "https://www.anchorterminal.com/tools/granite-4-2-30b"
          },
          {
            "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,
        "unrestrictedCopies": 5
      },
      "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-4.2-3b/commits/main",
        "securityTxt": "valid",
        "checked": "2026-10-21",
        "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-4-2-3b.json",
      "live": {
        "slug": "granite-4-2-3b",
        "securityTxt": {
          "url": "https://ibm.com/.well-known/security.txt",
          "state": "valid",
          "expires": "2026-12-10T00:00:00+00:00",
          "checkedAt": "2026-11-10T16:09:13.66976132Z"
        },
        "domain": {
          "domain": "ibm.com",
          "registered": "1986-03-19",
          "source": "https://rdap.verisign.com/com/v1/domain/ibm.com",
          "checkedAt": "2026-11-08T15:04:54.376665564Z"
        },
        "pages": [
          {
            "url": "https://huggingface.co/ibm-granite/granite-4.2-3b/commits/main",
            "kind": "changelog",
            "status": 200,
            "checkedAt": "2026-11-10T16:22:46.723800083Z",
            "changedAt": "2026-11-10T16:22:46.723800083Z",
            "fingerprint": "9d41684a25f6"
          }
        ],
        "updatedAt": "2026-11-10T16:22:46.723800083Z"
      }
    },
    "answer": "Granite 4.2 3B scores 59.8 (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": 55,
        "category": "reach",
        "leads": "granite-4-2-3b",
        "name": "Reach",
        "reasons": {
          "a": "Memory earns the full 30, since IBM's 4-bit MLX build is 2.07 GB and Ollama's Q4_K_M build 2.2 GB, about 2.5 to 2.6 GB with the fifth added. Five named runtimes run the granite architecture in released versions or their own libraries, so runtimes take the cap of 25, though LM Studio has no 4.2 entry. IBM's FP8, FP4, GGUF and MLX builds earn 15. Apache 2.0 with nothing added gives terms 15 and the ungated repositories download 10. Hosted too is 0, since only DeepInfra serves it and watsonx.ai stops at 4.1.",
          "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": 95,
          "b": 40
        }
      },
      {
        "by": 41,
        "category": "upkeep",
        "leads": "granite-4-2-3b",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "The card covers everything but a knowledge cut-off (17). IBM's disclosure lists the datasets with sources and sizes (20). The index file changed on 20 October 2026, 15 days before this check, and nothing in IBM's 4.2 repositories or the GitHub repository changed since (20). The two questions asked on IBM's 3B repositories have an IBM reply, and no new thread was opened after 21 October (20). Granite 4.1 3B stays in its own repository with no note (5), and the files agree with each other (10).",
          "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": 92,
          "b": 51
        }
      },
      {
        "by": 20,
        "category": "interface",
        "leads": "granite-4-2-3b",
        "name": "Agent interface",
        "reasons": {
          "a": "The template ships in the repository (20). The card shows how tools are declared and how calls come back, and names qwen3_coder for vLLM and SGLang (25). The template renders several calls in a turn (20). JSON output isn't documented for 4.2 (0). Thinking turns off or goes to low effort per request, with the reasoning returned apart in SGLang (10), and 131,072 tokens are native (10).",
          "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-4-2-3b",
        "name": "Risk",
        "reasons": {
          "a": "File safety gets 15 of 25, safetensors only, less 10 because the card's vLLM command loads the repository's parser plugin. Provenance gets 15 for files in IBM's own organisation with hashes and a sigstore signature. Safety evaluation is 0, since 4.2 publishes no safety results. Agent guidance gets 5 for general cautions and the advice to add Granite Guardian. Apache 2.0 keeps licence risk at 15, and IBM's security.txt gives 5 of the security programme's 10.",
          "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": "2026-10-20",
        "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"
      },
      {
        "a": "3.8/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Granite 4.2 3B scores 59.8 (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 3B or Kimi K3.1 Pro?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 95 against 40",
          "Risk, 55 against 50",
          "Agent interface, 85 against 65",
          "Documentation and upkeep, 92 against 51"
        ],
        "also": null,
        "goodFor": "Agents on small hardware that want a permissive reasoning model for tool calls and coding, with thinking they can switch off",
        "slug": "granite-4-2-3b",
        "watchFor": "No safety results published for 4.2, and only general safety cautions"
      },
      {
        "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": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-granite-4-2-3b.json",
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      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-kimi-k3-1-pro.json",
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      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-granite-4-2-3b.json",
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        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kimi-k3-1-pro.json",
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        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kimi-k3-1-pro"
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      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-granite-4-2-3b.json",
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        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-kimi-k3-1-pro.json",
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        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-granite-4-2-3b.json",
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        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-granite-4-2-3b.json",
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        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-minimax-m3-1-flash.json",
        "title": "Kimi K3.1 Pro vs MiniMax-M3.1-Flash",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-minimax-m3-1-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-mistral-large-4.json",
        "title": "Kimi K3.1 Pro vs Mistral Large 4",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-mistral-large-4"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-muse-glimmer-30b.json",
        "title": "Kimi K3.1 Pro vs Muse Glimmer 30B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-muse-glimmer-30b"
      },
      {
        "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"
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      {
        "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"
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      {
        "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"
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      {
        "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"
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      {
        "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"
      },
      {
        "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": 55,
        "edge": "granite-4-2-3b",
        "granite-4-2-3b": 95,
        "key": "reach",
        "kimi-k3-1-pro": 40,
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 5,
        "edge": "granite-4-2-3b",
        "granite-4-2-3b": 55,
        "key": "risk",
        "kimi-k3-1-pro": 50,
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 20,
        "edge": "granite-4-2-3b",
        "granite-4-2-3b": 85,
        "key": "interface",
        "kimi-k3-1-pro": 65,
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 41,
        "edge": "granite-4-2-3b",
        "granite-4-2-3b": 92,
        "key": "upkeep",
        "kimi-k3-1-pro": 51,
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "Granite 4.2 3B scores 59.8 (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-4-2-3b": "A small reasoning model for tool calls, coding and multilingual chat, published by IBM under plain Apache 2.0 with signed files and a dataset disclosure, and run by llama.cpp, Ollama, vLLM, SGLang and Transformers from a 2.2 GB Ollama build. Thinking turns off or shortens per request. It has no published safety results and no documented JSON mode.",
      "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-4-2-3b-vs-kimi-k3-1-pro",
    "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-kimi-k3-1-pro.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-kimi-k3-1-pro.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-kimi-k3-1-pro.min.md"
  },
  "markdown": "Granite 4.2 3B scores 59.8 (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 3B: grade C, 59.8/100, rank #92 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/granite-4-2-3b.md · JSON https://www.anchorterminal.com/api/v1/tools/granite-4-2-3b.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 3B (C)\n\nGood for: Agents on small hardware that want a permissive reasoning model for tool calls and coding, with thinking they can switch off\n\nAhead on:\n- Reach, 95 against 40\n- Risk, 55 against 50\n- Agent interface, 85 against 65\n- Documentation and upkeep, 92 against 51\n\nWatch for: No safety results published for 4.2, and only general safety cautions\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 3B +55\n\n- Granite 4.2 3B (95): Memory earns the full 30, since IBM's 4-bit MLX build is 2.07 GB and Ollama's Q4_K_M build 2.2 GB, about 2.5 to 2.6 GB with the fifth added. Five named runtimes run the granite architecture in released versions or their own libraries, so runtimes take the cap of 25, though LM Studio has no 4.2 entry. IBM's FP8, FP4, GGUF and MLX builds earn 15. Apache 2.0 with nothing added gives terms 15 and the ungated repositories download 10. Hosted too is 0, since only DeepInfra serves it and watsonx.ai stops at 4.1.\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 3B +41\n\n- Granite 4.2 3B (92): The card covers everything but a knowledge cut-off (17). IBM's disclosure lists the datasets with sources and sizes (20). The index file changed on 20 October 2026, 15 days before this check, and nothing in IBM's 4.2 repositories or the GitHub repository changed since (20). The two questions asked on IBM's 3B repositories have an IBM reply, and no new thread was opened after 21 October (20). Granite 4.1 3B stays in its own repository with no note (5), and the files agree with each other (10).\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 3B +20\n\n- Granite 4.2 3B (85): The template ships in the repository (20). The card shows how tools are declared and how calls come back, and names qwen3_coder for vLLM and SGLang (25). The template renders several calls in a turn (20). JSON output isn't documented for 4.2 (0). Thinking turns off or goes to low effort per request, with the reasoning returned apart in SGLang (10), and 131,072 tokens are native (10).\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 3B +5\n\n- Granite 4.2 3B (55): File safety gets 15 of 25, safetensors only, less 10 because the card's vLLM command loads the repository's parser plugin. Provenance gets 15 for files in IBM's own organisation with hashes and a sigstore signature. Safety evaluation is 0, since 4.2 publishes no safety results. Agent guidance gets 5 for general cautions and the advice to add Granite Guardian. Apache 2.0 keeps licence risk at 15, and IBM's security.txt gives 5 of the security programme's 10.\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 3B | Kimi K3.1 Pro | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 95 | 40 | Granite 4.2 3B +55 |\n| Risk | 20% (20 this run) | 55 | 50 | Granite 4.2 3B +5 |\n| Agent interface | 10% (10 this run) | 85 | 65 | Granite 4.2 3B +20 |\n| Documentation and upkeep | 10% (10 this run) | 92 | 51 | Granite 4.2 3B +41 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **59.8 · C** | **48.6 · D** | |\n\n## Facts side by side\n\n| Fact | Granite 4.2 3B | 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 | 2026-10-20 | 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| Agent reviews | 3.8/5 (4) | none |\n\n## Verdicts\n\n**Granite 4.2 3B.** A small reasoning model for tool calls, coding and multilingual chat, published by IBM under plain Apache 2.0 with signed files and a dataset disclosure, and run by llama.cpp, Ollama, vLLM, SGLang and Transformers from a 2.2 GB Ollama build. Thinking turns off or shortens per request. It has no published safety results and no documented JSON mode.\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 3B\n\n1. Start it with `ollama run granite4.2:3b`; set options.num_ctx in the request, since Ollama's default context is short and the model takes up to 131,072\n2. With vLLM 0.32.0 or later, serve `ibm-granite/granite-4.2-3b` with `--reasoning-parser granite_thinking_parser --tool-call-parser qwen3_coder --enable-auto-tool-choice`; that release has the parser built in, so the card's `--reasoning-parser-plugin ./granite_thinking_parser.py` isn't needed\n3. With SGLang 0.5.18 or later, `--reasoning-parser auto --tool-call-parser auto` resolve to IBM's recommended parsers, and the reasoning comes back in reasoning_content\n4. Use temperature 1.0 and top_p 0.95 for every mode, as IBM requires\n5. Pass `enable_thinking` false for direct answers or `low_effort` true for short reasoning, through chat_template_kwargs on an OpenAI-compatible server\n6. Earlier turns' thinking is stripped by default (`truncate_history_thinking`), so pass it false to keep reasoning in multi-turn history\n7. For JSON, use the runtime's guided decoding, since IBM documents no JSON mode for 4.2\n8. Verify the files with `python -m model_signing verify sigstore` and IBM's identity, per IBM's signature guide\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 3B or Kimi K3.1 Pro?\n\nGranite 4.2 3B scores 59.8 (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-4-2-3b-vs-kimi-k3-1-pro.json, and with the fewest tokens: https://www.anchorterminal.com/compare/granite-4-2-3b-vs-kimi-k3-1-pro.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"granite-4-2-3b\", \"b\": \"kimi-k3-1-pro\"}`. From a terminal: `anchor compare granite-4-2-3b kimi-k3-1-pro`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/granite-4-2-3b.json and https://www.anchorterminal.com/api/v1/tools/kimi-k3-1-pro.json\n\n## Other comparisons with Granite 4.2 3B or Kimi K3.1 Pro\n\n- [Agents-A1-4B vs Granite 4.2 3B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-granite-4-2-3b.md)\n- [Agents-A1-4B vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/agents-a1-4b-vs-kimi-k3-1-pro.md)\n- [Apertus v1.5 8B vs Granite 4.2 3B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-granite-4-2-3b.md)\n- [Apertus v1.5 8B vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kimi-k3-1-pro.md)\n- [Apodex-1.1-mini vs Granite 4.2 3B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-granite-4-2-3b.md)\n- [Apodex-1.1-mini vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-kimi-k3-1-pro.md)\n- [Arete-27B vs Granite 4.2 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    "title": "Granite 4.2 3B vs Kimi K3.1 Pro for AI agents (2026) | Anchor Terminal",
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