{
  "data": {
    "a": {
      "slug": "granite-4-2-8b",
      "name": "Granite 4.2 8B",
      "vendor": "IBM",
      "vendorUrl": "https://www.ibm.com/granite",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Granite 4.2 8B is IBM's open-weight dense reasoning model of 8.8B parameters, post-trained from Granite 4.1 8B 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-4-2-8b",
      "markdownUrl": "https://www.anchorterminal.com/tools/granite-4-2-8b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/granite-4-2-8b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/granite-4-2-8b.json",
      "dns": "granite-4-2-8b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/ibm-granite/granite-4.2-8b",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "ibm-granite/granite-4.2-8b"
        },
        {
          "registry": "huggingface",
          "name": "ibm-granite/granite-4.2-8b-GGUF"
        },
        {
          "registry": "huggingface",
          "name": "ibm-granite/granite-4.2-8b-fp8"
        },
        {
          "registry": "huggingface",
          "name": "ibm-granite/granite-4.2-8b-mxfp4"
        },
        {
          "registry": "huggingface",
          "name": "ibm-granite/granite-4.2-8b-nvfp4"
        },
        {
          "registry": "huggingface",
          "name": "ibm-granite/granite-4.2-8b-q4-mlx"
        },
        {
          "registry": "ollama",
          "name": "granite4.2:8b"
        }
      ],
      "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, 17.6 GB for the BF16 files, 5.3 GB for IBM's Q4_K_M GGUF or 5.0 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-4.2-8b",
      "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": "2026-10-20",
      "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": 53,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 85,
          "reach": 95,
          "risk": 55,
          "upkeep": 92
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "IBM's mid-size reasoning cousin, with the same strengths as the 3B. It runs in every runtime we count from IBM's own builds on a 16 GB machine, 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 16 GB machine",
        "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 granite4.2:8b`, or `llama-server -hf ibm-granite/granite-4.2-8b-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": 95,
          "risk": 55,
          "upkeep": 92
        },
        "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 granite4.2:8b\n# or, with llama.cpp and IBM's own GGUF:\nllama-server -hf ibm-granite/granite-4.2-8b-GGUF:Q4_K_M --jinja\n# or, with vLLM 0.32.0 or later:\nvllm serve ibm-granite/granite-4.2-8b --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\": \"granite4.2:8b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/granite-4-2-8b"
      },
      "sameCompany": [
        "granite-embedding-small-english-r2",
        "granite-guardian",
        "granite-speech-5-0-2b",
        "ibm-watson-stt",
        "granite-switch",
        "granite-4-2-30b",
        "granite-4-2-3b"
      ],
      "weights": {
        "trainedBy": "IBM",
        "builtOn": "ibm-granite/granite-4.1-8b-base (IBM's own base checkpoint, post-trained; named on the card and in its metadata)",
        "params": "8.79B",
        "context": 131072,
        "goodFor": "Mid-size dense reasoning model for tool calling, coding and multilingual chat on a laptop GPU, with thinking that is on, off or short per request.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "granite-4.2-8b-nvfp4",
            "url": "https://huggingface.co/ibm-granite/granite-4.2-8b-nvfp4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 6.13,
            "memoryGB": 8,
            "format": "safetensors",
            "runsOn": [
              "vLLM"
            ]
          },
          {
            "name": "granite-4.2-8b-mxfp4",
            "url": "https://huggingface.co/ibm-granite/granite-4.2-8b-mxfp4",
            "who": "publisher",
            "precision": "mxfp4",
            "sizeGB": 5.88,
            "memoryGB": 8,
            "format": "safetensors",
            "runsOn": [
              "vLLM"
            ]
          },
          {
            "name": "granite-4.2-8b-Q4_K_M",
            "url": "https://huggingface.co/ibm-granite/granite-4.2-8b-GGUF",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 5.35,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "IBM's GGUF repository also holds Q4_0 (5.06 GB), Q6_K and BF16 files among others"
          },
          {
            "name": "granite-4.2-8b-q4-mlx",
            "url": "https://huggingface.co/ibm-granite/granite-4.2-8b-q4-mlx",
            "who": "publisher",
            "precision": "q4",
            "sizeGB": 4.95,
            "memoryGB": 8,
            "format": "mlx",
            "runsOn": [
              "mlx-lm"
            ],
            "note": "The smallest build at 4-bit or better. IBM also publishes q8-mlx (9.34 GB) and bf16-mlx"
          },
          {
            "name": "ollama granite4.2:8b",
            "url": "https://ollama.com/library/granite4.2:8b",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 5.3,
            "memoryGB": 8,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Ollama's library build, size as its tags page shows it on 10 November 2026"
          },
          {
            "name": "granite-4.2-8b-fp8",
            "url": "https://huggingface.co/ibm-granite/granite-4.2-8b-fp8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 9.62,
            "memoryGB": 12,
            "format": "safetensors",
            "runsOn": [
              "vLLM"
            ]
          },
          {
            "name": "granite-4.2-8b-Q8_0",
            "url": "https://huggingface.co/ibm-granite/granite-4.2-8b-GGUF",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 9.35,
            "memoryGB": 12,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ]
          },
          {
            "name": "granite-4.2-8b",
            "url": "https://huggingface.co/ibm-granite/granite-4.2-8b",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 17.58,
            "memoryGB": 24,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang",
              "Transformers"
            ]
          }
        ],
        "run": "ollama run granite4.2:8b",
        "runBuild": "ollama granite4.2:8b",
        "checked": "2026-11-10",
        "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-4.2-3b",
            "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-4.2-30b",
            "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
      },
      "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-4.2-8b/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-4-2-8b.json"
    },
    "answer": "Granite 4.2 8B and Kolibri-1 score within a point of each other on agent readiness, 61 (C) and 61 (C). Kolibri-1 leads on risk and agent interface.",
    "b": {
      "slug": "kolibri-1",
      "name": "Kolibri-1",
      "vendor": "Aleph Alpha",
      "vendorUrl": "https://aleph-alpha.com",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Kolibri-1 is Aleph Alpha's open-weight mixture-of-experts reasoning model for German and English, 78B parameters with 3.46B active per token, published on Hugging Face in FP8 and BF16 under Apache 2.0 and served with vLLM.",
      "url": "https://www.anchorterminal.com/tools/kolibri-1",
      "markdownUrl": "https://www.anchorterminal.com/tools/kolibri-1.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/kolibri-1.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/kolibri-1.json",
      "dns": "kolibri-1.grade.anchorterminal.com",
      "repo": "https://huggingface.co/Aleph-Alpha/Kolibri-1",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "Aleph-Alpha/Kolibri-1"
        },
        {
          "registry": "huggingface",
          "name": "Aleph-Alpha/Kolibri-1-BF16"
        },
        {
          "registry": "pypi",
          "name": "aleph-alpha-inference"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither repository is gated (checked 2026-10-20).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Aleph Alpha sells no per-call access to these weights. Running them costs the hardware, about 78 GB of accelerator memory for the FP8 weights per the model card (checked 2026-10-20).",
      "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 2026-10-20).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-20"
      },
      "docsUrl": "https://huggingface.co/Aleph-Alpha/Kolibri-1",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "reasoning",
        "tool-calling",
        "german",
        "apache-2.0",
        "safetensors",
        "fp8",
        "self-hosted",
        "new"
      ],
      "lastRelease": "2026-10-19",
      "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": 34,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 90,
          "reach": 65,
          "risk": 65,
          "upkeep": 70
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-03"
        },
        "negative": 0,
        "verdict": "Kolibri-1 is a German and English reasoning model with a documented tool-call format, switchable thinking and a 262,144-token context, under Apache 2.0. Aleph Alpha's FP8 build needs about 95 GB and runs on vLLM and Transformers 5.20; llama.cpp and Ollama 0.40.14 need a GGUF. The main risk is no published safety results or security contact, and few repository answers.",
        "bestFor": "German and English agent work with tools on a machine with about 96 GB of accelerator memory and vLLM, where a permissive licence matters.",
        "strengths": [
          "Apache 2.0 with no use policy, downloadable with no account",
          "Safetensors only, in Aleph Alpha's own organisation with a SHA-256 for every shard",
          "Tool calling documented end to end with the kolibri1 parser for vLLM and a working example",
          "Thinking can be set from none to high per request, with the reasoning returned separately",
          "262,144 tokens trained, with a full model card and a data card that gives the mix, filtering and cut-off"
        ],
        "weaknesses": [
          "The smallest publisher build is FP8 at 78.8 GB, so it needs about 95 GB and won't fit a 64 GB machine",
          "No LM Studio or SGLang support in a released version, and vLLM's kolibri1 parsers come only with Aleph Alpha's plugin pinned to vLLM 0.29",
          "No safety evaluation results and no security contact or disclosure policy found",
          "Aleph Alpha answered 2 of 14 discussions, and a report of a broken tool-call parser in its container image has no reply",
          "No hosted API or inference provider, so an agent without the hardware can't call it",
          "Structured output is claimed without saying how to request it"
        ],
        "agentNotes": [
          "Start it with pip install 'aleph-alpha-inference\u003e=1' then vllm serve Aleph-Alpha/Kolibri-1 --kv-cache-dtype fp8 --reasoning-parser kolibri1 --tool-call-parser kolibri1 --enable-auto-tool-choice",
          "Without the kolibri1 tool-call parser and enable-auto-tool-choice, tool calls come back as text in tool_call tags",
          "The plugin supports one vLLM minor version, 0.29. vLLM 0.33.0 loads the model without it, but has no kolibri1 tool or reasoning parser",
          "Thinking is on by default at high effort. Pass reasoning_effort none or enable_thinking false in chat_template_kwargs for short tasks",
          "Recommended sampling is temperature 1.0, top_p 0.97 and top_k 128",
          "Past 262,144 tokens, add --max-model-len 1048576 and the max_position_embeddings override in --hf-overrides, though Aleph Alpha recommends staying at or below 262,144",
          "With Transformers 5.20 or later, tokenizer.parse_response returns the answer in content and the reasoning in thinking",
          "On the reference machine the FP8 build fits by size, but no document confirms vLLM runs FP8 on gfx1151, and Aleph Alpha ships no GGUF for llama.cpp or Ollama",
          "Validate tool results in your own code, as the card asks"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 3.3,
        "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": 90,
          "reach": 65,
          "risk": 65,
          "upkeep": 70
        },
        "provenanceScore": 70,
        "checked": {
          "categories": {
            "interface": "2026-11-03",
            "reach": "2026-11-03",
            "risk": "2026-11-03",
            "upkeep": "2026-11-03"
          },
          "last": "2026-11-03",
          "lastMoved": "2026-11-03",
          "oldest": "2026-11-03",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "pip install 'aleph-alpha-inference\u003e=1'\nvllm serve Aleph-Alpha/Kolibri-1 --kv-cache-dtype fp8 \\\n  --reasoning-parser kolibri1 --tool-call-parser kolibri1 --enable-auto-tool-choice\n# or, with llama.cpp and bartowski's GGUF, a third party's conversion:\nllama-server -hf bartowski/Kolibri-1-GGUF:Q4_K_M --jinja",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\n    \"model\": \"Aleph-Alpha/Kolibri-1\",\n    \"messages\": [{\"role\": \"user\", \"content\": \"What is the capital of France?\"}],\n    \"chat_template_kwargs\": {\"enable_thinking\": false}\n  }'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/kolibri-1"
      },
      "weights": {
        "trainedBy": "Aleph Alpha",
        "params": "78.1B",
        "active": "3.46B",
        "context": 262144,
        "goodFor": "German and English reasoning model with tool calling and adjustable reasoning effort, text only.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "Transformers"
        ],
        "builds": [
          {
            "name": "bartowski Kolibri-1-GGUF (Q4_K_M)",
            "url": "https://huggingface.co/bartowski/Kolibri-1-GGUF",
            "who": "bartowski",
            "precision": "q4_k_m",
            "sizeGB": 49.76,
            "memoryGB": 64,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From bartowski, a third party's conversion and not Aleph Alpha's. 49.76 GB of Q4_K_M weights, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it"
          },
          {
            "name": "Kolibri-1",
            "url": "https://huggingface.co/Aleph-Alpha/Kolibri-1",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 78.8,
            "memoryGB": 96,
            "format": "safetensors"
          },
          {
            "name": "bartowski Kolibri-1-GGUF (Q8_0)",
            "url": "https://huggingface.co/bartowski/Kolibri-1-GGUF",
            "who": "bartowski",
            "precision": "q8_0",
            "sizeGB": 83.14,
            "memoryGB": 128,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From bartowski, a third party's conversion and not Aleph Alpha's. 83.14 GB of Q8_0 weights, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it"
          },
          {
            "name": "Kolibri-1-BF16",
            "url": "https://huggingface.co/Aleph-Alpha/Kolibri-1-BF16",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 156.2,
            "memoryGB": 192,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)"
            ]
          }
        ],
        "run": "pip install 'aleph-alpha-inference\u003e=1' \u0026\u0026 vllm serve Aleph-Alpha/Kolibri-1 --kv-cache-dtype fp8 --reasoning-parser kolibri1 --tool-call-parser kolibri1 --enable-auto-tool-choice",
        "runBuild": "Kolibri-1",
        "checked": "2026-11-03",
        "minMemoryGB": 64
      },
      "family": {
        "lab": "Aleph Alpha",
        "family": "Kolibri",
        "url": "https://www.anchorterminal.com/open-weights/#aleph-alpha",
        "olderCount": 23,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Aleph Alpha GmbH",
        "domain": "aleph-alpha.com",
        "domainRegistered": "2018-08-11",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/Aleph-Alpha/Kolibri-1/commits/main",
        "securityTxt": "unknown",
        "checked": "2026-10-20",
        "notes": [
          "The model card names Aleph Alpha GmbH as the provider and Aleph Alpha Research GmbH as the developer. The imprint names Aleph Alpha GmbH, Heidelberg, commercial register Mannheim HRB 732804.",
          "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the repository's commit history, whose messages say what changed (Release 2026-10-02, a quantisation config fix on 13 October, transformers_version added on 19 October).",
          "aleph-alpha.com/.well-known/security.txt answered 403 to our reader on 20 October 2026, so it is counted as unchecked rather than absent."
        ],
        "score": 70
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/kolibri-1.json",
      "live": {
        "slug": "kolibri-1",
        "versions": [
          {
            "registry": "pypi",
            "name": "aleph-alpha-inference",
            "version": "1.0.0",
            "released": "2026-10-03",
            "seenAt": "2026-11-10T03:51:07.580354462Z"
          }
        ],
        "pypiWeekly": 115,
        "securityTxt": {
          "url": "https://aleph-alpha.com/.well-known/security.txt",
          "state": "unknown",
          "checkedAt": "2026-11-10T16:08:53.984135769Z"
        },
        "domain": {
          "domain": "aleph-alpha.com",
          "registered": "2018-08-11",
          "source": "https://rdap.verisign.com/com/v1/domain/aleph-alpha.com",
          "checkedAt": "2026-11-08T14:59:31.895475432Z"
        },
        "pages": [
          {
            "url": "https://huggingface.co/Aleph-Alpha/Kolibri-1/commits/main",
            "kind": "changelog",
            "status": 200,
            "checkedAt": "2026-11-10T16:22:08.257556045Z",
            "changedAt": "2026-11-10T16:22:08.257556045Z",
            "fingerprint": "847be72adc2c"
          }
        ],
        "updatedAt": "2026-11-10T16:22:08.257556045Z"
      }
    },
    "edges": [
      {
        "by": 30,
        "category": "reach",
        "leads": "granite-4-2-8b",
        "name": "Reach",
        "reasons": {
          "a": "Memory 30, IBM's own 4-bit MLX build granite-4.2-8b-q4-mlx is 4.95 GB, and 4.95 GB × 1.2 = 5.9 GB, within 8 GB. Its Q4_0 GGUF is 5.06 GB and Ollama's library build granite4.2:8b (5.3 GB) fits 8 GB too. 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, 8b 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. 30 + 25 + 15 + 15 + 10 + 0 = 95.",
          "b": "Memory earns 10 because the smallest qualifying build is Aleph Alpha's own FP8 one at 78.8 GB, which comes to 94.6 GB with the fifth added and so fits only the reference machine's band. There is no publisher 4-bit or GGUF build and no Ollama library entry to bring it lower. Runtimes earn 20, for released Transformers 5.20.0, released llama.cpp builds from b11709, Ollama 0.40.14, which builds llama.cpp b11963 with kolibri1, and vLLM, through Aleph Alpha's plugin for 0.29 and natively in 0.33.0 through its Transformers backend. LM Studio's latest runtime predates kolibri1 and SGLang 0.5.23 has no model file. Builds earn 10 for the one FP8 quantisation beside BF16. Terms earn 15 for Apache 2.0 with no use policy. Download earns 10 because neither repository is gated. Hosted earns 0, since no inference provider is listed on the Hub and Aleph Alpha names no API serving it."
        },
        "scores": {
          "a": 95,
          "b": 65
        }
      },
      {
        "by": 22,
        "category": "upkeep",
        "leads": "granite-4-2-8b",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 17, Intended use (best for), limitations (ethical considerations and limitations), architecture, parameters (8B), context length (128K native) and 12 tested languages are on the card. Training data 20, IBM's disclosure file for the 8B lists the datasets used in pre-training and post-training as machine-readable metadata, and the card describes the three training stages. Last change 20, the last commit, on 20 October 2026, replaced model.safetensors.index.json (titled \"granite-4.2 index file fix\"), 21 days before this check. Responsiveness 20, Of the 5 discussions that aren't pull requests (13 September to 5 November 2026), 4 have a reply from an ibm-granite member. Version policy 5, Granite 4.1 8B 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 + 20 + 20 + 5 + 10 = 92.",
          "b": "The model card earns 20 with all seven items. Training data earns 15 for a data card with the mix, the filtering and the cut-off, short of a dataset list with sizes. The last change earns 20, for template and card commits on 20 October 2026. Responsiveness earns 5, since Aleph Alpha replied in 2 of 14 discussions across both repositories and none since 13 October. Version policy earns 5 for a first release. File health earns 5, because the files agree but a report of a broken tool-call parser in the publisher's container image has waited since 7 October without a reply."
        },
        "scores": {
          "a": 92,
          "b": 70
        }
      },
      {
        "by": 10,
        "category": "risk",
        "leads": "kolibri-1",
        "name": "Risk",
        "reasons": {
          "a": "File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the ibm-granite organisation, which IBM's Granite docs and the GitHub README link to (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions. Licence risk 15, Apache 2.0 has no use policy, field-of-use limit, threshold or change clause, and the card's licence link resolves. Security programme 5, www.ibm.com/.well-known/security.txt names IBM PSIRT, read for the 3B listing on 4 November 2026 (5). 15 + 15 + 0 + 5 + 15 + 5 = 55.",
          "b": "File safety earns 25 for safetensors only, with no repository code run on load. Provenance earns 15, for the Aleph-Alpha organisation the launch post links and a SHA-256 for every weight shard. Safety evaluation earns 0, because the card says refusals were measured and neither the card nor the technical report publishes results. Agent guidance earns 10, since the card says what the trained refusals cover and which safeguards to add around the model, without prompt-injection guidance. Licence risk earns the full 15 under unchanged Apache 2.0. The security programme earns 0, as no contact, policy or advisories were found and security.txt answered 403 on 3 November, so it is scored as absent and left open."
        },
        "scores": {
          "a": 55,
          "b": 65
        }
      },
      {
        "by": 5,
        "category": "interface",
        "leads": "kolibri-1",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, chat_template.jinja in the repository, with roles, tools, tool responses, the thinking switch and low effort. Tool-calling format 25, the card has tool-calling sections for vLLM and SGLang, declares tools with OpenAI's function schema and names the parser to use, qwen3_coder, which both runtimes ship in the released versions read. Trained tool calling 20, the template loops over every entry in an assistant turn's tool_calls, so it renders several calls in one turn, and the card names tool calling and agentic workflows as what it's best for. Structured output 0, the card doesn't document JSON or JSON Schema output or how to ask for it. Reasoning control 10, the card documents thinking on by default, non-thinking and low-effort modes per request through the template (enable_thinking and low_effort), with the reasoning returned apart from the answer. Context 10, 131,072 tokens natively per the card and config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85.",
          "b": "The chat template earns 20 as Jinja in tokenizer_config.json. The tool-calling format earns 25, because the card documents how tools are declared and how calls come back and names the kolibri1 parser for vLLM with a full example. Trained tool calling earns 20, from training on tool environments and a template built for several calls in one turn. Structured output earns 5, since the card claims it without saying how to ask. Reasoning control earns 10 for effort levels and an off switch per request, with the reasoning returned in a separate field. Context earns 10 for 262,144 tokens trained."
        },
        "scores": {
          "a": 85,
          "b": 90
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "IBM",
        "b": "Aleph Alpha",
        "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": "Apache-2.0",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-10-20",
        "b": "2026-10-19",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "none",
        "b": "3.3/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Granite 4.2 8B and Kolibri-1 score within a point of each other on agent readiness, 61 (C) and 61 (C). Kolibri-1 leads on risk and agent interface.",
        "question": "Which is better for AI agents, Granite 4.2 8B or Kolibri-1?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 95 against 65",
          "Documentation and upkeep, 92 against 70"
        ],
        "also": null,
        "goodFor": "Agents that want a documented, signed, Apache 2.0 reasoning model with tool calling on a 16 GB machine",
        "slug": "granite-4-2-8b",
        "watchFor": "No safety results published for it"
      },
      {
        "aheadOn": [
          "Risk, 65 against 55",
          "Agent interface, 90 against 85"
        ],
        "also": null,
        "goodFor": "German and English agent work with tools on a machine with about 96 GB of accelerator memory and vLLM, where a permissive licence matters.",
        "slug": "kolibri-1",
        "watchFor": "The smallest publisher build is FP8 at 78.8 GB, so it needs about 95 GB and won't fit a 64 GB machine"
      }
    ],
    "history": {
      "granite-4-2-8b": null,
      "kolibri-1": [
        {
          "causes": [
            "product"
          ],
          "date": "2026-11-03",
          "delta": 0,
          "from": 61,
          "fromGrade": "C",
          "to": 61,
          "toGrade": "C"
        },
        {
          "causes": [
            "method"
          ],
          "date": "2026-10-21",
          "delta": -14.8,
          "from": 75.8,
          "fromGrade": "BB",
          "to": 61,
          "toGrade": "C"
        }
      ]
    },
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
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        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-granite-4-2-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-kolibri-1.json",
        "title": "Agents-A1-4B vs Kolibri-1",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-kolibri-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-granite-4-2-8b.json",
        "title": "Apertus v1.5 8B vs Granite 4.2 8B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-granite-4-2-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kolibri-1.json",
        "title": "Apertus v1.5 8B vs Kolibri-1",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kolibri-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-granite-4-2-8b.json",
        "title": "Apodex-1.1-mini vs Granite 4.2 8B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-granite-4-2-8b"
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      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-kolibri-1.json",
        "title": "Apodex-1.1-mini vs Kolibri-1",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-kolibri-1"
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      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-granite-4-2-8b.json",
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        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-granite-4-2-8b"
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        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-kolibri-1.json",
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        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-kolibri-1"
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        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-granite-4-2-8b.json",
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        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-granite-4-2-8b"
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        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-kolibri-1.json",
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        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-kolibri-1"
      },
      {
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-granite-4-2-8b"
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      },
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      },
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        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-longcat-2-5.json",
        "title": "Kolibri-1 vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-longcat-2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-mimo-v2-6-distill-qwen-9b.json",
        "title": "Kolibri-1 vs MiMo-V2.6 Distill Qwen 9B",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-mimo-v2-6-distill-qwen-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-mimo-v2-6-flash-rl.json",
        "title": "Kolibri-1 vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-mimo-v2-6-flash-rl"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-minicpm-o-4-5.json",
        "title": "Kolibri-1 vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-minicpm-o-4-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-minicpm-v-4-7-1b.json",
        "title": "Kolibri-1 vs MiniCPM-V 4.7 1B",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-minicpm-v-4-7-1b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-minicpm-v-4-7-35b-a3b.json",
        "title": "Kolibri-1 vs MiniCPM-V 4.7 35B-A3B",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-minicpm-v-4-7-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-minicpm5-2b.json",
        "title": "Kolibri-1 vs MiniCPM5-2B",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-minicpm5-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-minimax-m2-5.json",
        "title": "Kolibri-1 vs MiniMax-M2.5",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-minimax-m2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-minimax-m2-7.json",
        "title": "Kolibri-1 vs MiniMax-M2.7",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-minimax-m2-7"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-minimax-m3-1-flash.json",
        "title": "Kolibri-1 vs MiniMax-M3.1-Flash",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-minimax-m3-1-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-mistral-large-4.json",
        "title": "Kolibri-1 vs Mistral Large 4",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-mistral-large-4"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-muse-glimmer-30b.json",
        "title": "Kolibri-1 vs Muse Glimmer 30B",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-muse-glimmer-30b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-nanbeige-4-2-3b.json",
        "title": "Kolibri-1 vs Nanbeige4.2-3B",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-nanbeige-4-2-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-nanbeige-4-3-33b.json",
        "title": "Kolibri-1 vs Nanbeige4.3-33B",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-nanbeige-4-3-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-nemotron-3-5-bolt-e4b-4gb.json",
        "title": "Kolibri-1 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-nemotron-3-5-bolt-e4b-4gb"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-nemotron-3-5-lightning-30b-a3b.json",
        "title": "Kolibri-1 vs Nemotron 3.5 Lightning 30B A3B",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-nemotron-3-5-lightning-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-nemotron-3-nano-30b-a3b.json",
        "title": "Kolibri-1 vs Nemotron 3 Nano 30B A3B",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-nemotron-3-nano-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-nemotron-3-nano-4b.json",
        "title": "Kolibri-1 vs Nemotron 3 Nano 4B",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-nemotron-3-nano-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "Kolibri-1 vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-nemotron-3-super-120b-a12b.json",
        "title": "Kolibri-1 vs Nemotron 3 Super 120B A12B",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-nemotron-3-super-120b-a12b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-nemotron-3-ultra-550b-a55b.json",
        "title": "Kolibri-1 vs Nemotron 3 Ultra 550B A55B",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-nemotron-3-ultra-550b-a55b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-north-mini-code-1-0.json",
        "title": "Kolibri-1 vs North Mini Code 1.0",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-north-mini-code-1-0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-occamy-1-0.json",
        "title": "Kolibri-1 vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-occamy-1-0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-olmo-3-7b-think.json",
        "title": "Kolibri-1 vs Olmo 3 7B Think",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-olmo-3-7b-think"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-ornith-1-5-122b.json",
        "title": "Kolibri-1 vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-ornith-1-5-35b-a3b.json",
        "title": "Kolibri-1 vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-ornith-1-5-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-ornith-1-5-397b.json",
        "title": "Kolibri-1 vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-ornith-1-5-397b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-ornith-1-5-9b.json",
        "title": "Kolibri-1 vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-ornith-1-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-otel-2-0-llm-31b-it.json",
        "title": "Kolibri-1 vs OTel-2.0-LLM-31B-IT",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-otel-2-0-llm-31b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen-agentworld-35b-a3b.json",
        "title": "Kolibri-1 vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-30b-a3b-instruct-2507.json",
        "title": "Kolibri-1 vs Qwen3-30B-A3B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-30b-a3b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-4b-instruct-2507.json",
        "title": "Kolibri-1 vs Qwen3-4B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-4b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-5-0-8b.json",
        "title": "Kolibri-1 vs Qwen3.5-0.8B",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-5-0-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-5-122b-a10b.json",
        "title": "Kolibri-1 vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-5-122b-a10b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-5-2b.json",
        "title": "Kolibri-1 vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-5-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-5-397b-a17b.json",
        "title": "Kolibri-1 vs Qwen3.5-397B-A17B",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-5-397b-a17b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-5-4b.json",
        "title": "Kolibri-1 vs Qwen3.5-4B",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-5-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-5-9b.json",
        "title": "Kolibri-1 vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-6-27b.json",
        "title": "Kolibri-1 vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-6-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-6-35b-a3b.json",
        "title": "Kolibri-1 vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-6-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-8-27b.json",
        "title": "Kolibri-1 vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-8-flash-next.json",
        "title": "Kolibri-1 vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "Kolibri-1 vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-coder-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-coder-next.json",
        "title": "Kolibri-1 vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Kolibri-1 vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-omni-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-vl-2b-instruct.json",
        "title": "Kolibri-1 vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-vl-2b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Kolibri-1 vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-vl-32b-instruct.json",
        "title": "Kolibri-1 vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-vl-4b-instruct.json",
        "title": "Kolibri-1 vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-vl-8b-instruct.json",
        "title": "Kolibri-1 vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Kolibri-1 vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-recursive-flint.json",
        "title": "Kolibri-1 vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-ripple-65b-preview.json",
        "title": "Kolibri-1 vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-sensenova-loop-9b-preview.json",
        "title": "Kolibri-1 vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-sensenova-loop-9b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-smollm3-3b.json",
        "title": "Kolibri-1 vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-spark-x2-6-4b.json",
        "title": "Kolibri-1 vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-step-3-7-flash.json",
        "title": "Kolibri-1 vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Kolibri-1 vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Kolibri-1 vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Kolibri-1 vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-unlimited-ocr-2.json",
        "title": "Kolibri-1 vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-wevisone-vl-2b.json",
        "title": "Kolibri-1 vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-wevisone-vl-4b.json",
        "title": "Kolibri-1 vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-zaya1-8b.json",
        "title": "Kolibri-1 vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-zaya1-8b"
      }
    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 30,
        "edge": "granite-4-2-8b",
        "granite-4-2-8b": 95,
        "key": "reach",
        "kolibri-1": 65,
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 10,
        "edge": "kolibri-1",
        "granite-4-2-8b": 55,
        "key": "risk",
        "kolibri-1": 65,
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 5,
        "edge": "kolibri-1",
        "granite-4-2-8b": 85,
        "key": "interface",
        "kolibri-1": 90,
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 22,
        "edge": "granite-4-2-8b",
        "granite-4-2-8b": 92,
        "key": "upkeep",
        "kolibri-1": 70,
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "Granite 4.2 8B and Kolibri-1 score within a point of each other on agent readiness, 61 (C) and 61 (C). Kolibri-1 leads on risk and agent interface. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "granite-4-2-8b": "IBM's mid-size reasoning cousin, with the same strengths as the 3B. It runs in every runtime we count from IBM's own builds on a 16 GB machine, 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.",
      "kolibri-1": "Kolibri-1 is a German and English reasoning model with a documented tool-call format, switchable thinking and a 262,144-token context, under Apache 2.0. Aleph Alpha's FP8 build needs about 95 GB and runs on vLLM and Transformers 5.20; llama.cpp and Ollama 0.40.14 need a GGUF. The main risk is no published safety results or security contact, and few repository answers."
    }
  },
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  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
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    "json": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-kolibri-1.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-kolibri-1.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-kolibri-1.min.md"
  },
  "markdown": "Granite 4.2 8B and Kolibri-1 score within a point of each other on agent readiness, 61 (C) and 61 (C). Kolibri-1 leads on risk and agent interface. Both do open-weight models you run yourself.\n\n- Granite 4.2 8B: grade C, 61/100, rank #66 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/granite-4-2-8b.md · JSON https://www.anchorterminal.com/api/v1/tools/granite-4-2-8b.json\n- Kolibri-1: grade C, 61/100, rank #44 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/kolibri-1.md · JSON https://www.anchorterminal.com/api/v1/tools/kolibri-1.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 8B (C)\n\nGood for: Agents that want a documented, signed, Apache 2.0 reasoning model with tool calling on a 16 GB machine\n\nAhead on:\n- Reach, 95 against 65\n- Documentation and upkeep, 92 against 70\n\nWatch for: No safety results published for it\n\n### Kolibri-1 (C)\n\nGood for: German and English agent work with tools on a machine with about 96 GB of accelerator memory and vLLM, where a permissive licence matters.\n\nAhead on:\n- Risk, 65 against 55\n- Agent interface, 90 against 85\n\nWatch for: The smallest publisher build is FP8 at 78.8 GB, so it needs about 95 GB and won't fit a 64 GB machine\n\n\n## Why the edge\n\n### Reach: Granite 4.2 8B +30\n\n- Granite 4.2 8B (95): Memory 30, IBM's own 4-bit MLX build granite-4.2-8b-q4-mlx is 4.95 GB, and 4.95 GB × 1.2 = 5.9 GB, within 8 GB. Its Q4_0 GGUF is 5.06 GB and Ollama's library build granite4.2:8b (5.3 GB) fits 8 GB too. 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, 8b 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. 30 + 25 + 15 + 15 + 10 + 0 = 95.\n- Kolibri-1 (65): Memory earns 10 because the smallest qualifying build is Aleph Alpha's own FP8 one at 78.8 GB, which comes to 94.6 GB with the fifth added and so fits only the reference machine's band. There is no publisher 4-bit or GGUF build and no Ollama library entry to bring it lower. Runtimes earn 20, for released Transformers 5.20.0, released llama.cpp builds from b11709, Ollama 0.40.14, which builds llama.cpp b11963 with kolibri1, and vLLM, through Aleph Alpha's plugin for 0.29 and natively in 0.33.0 through its Transformers backend. LM Studio's latest runtime predates kolibri1 and SGLang 0.5.23 has no model file. Builds earn 10 for the one FP8 quantisation beside BF16. Terms earn 15 for Apache 2.0 with no use policy. Download earns 10 because neither repository is gated. Hosted earns 0, since no inference provider is listed on the Hub and Aleph Alpha names no API serving it.\n\n### Documentation and upkeep: Granite 4.2 8B +22\n\n- Granite 4.2 8B (92): Model card 17, Intended use (best for), limitations (ethical considerations and limitations), architecture, parameters (8B), context length (128K native) and 12 tested languages are on the card. Training data 20, IBM's disclosure file for the 8B lists the datasets used in pre-training and post-training as machine-readable metadata, and the card describes the three training stages. Last change 20, the last commit, on 20 October 2026, replaced model.safetensors.index.json (titled \"granite-4.2 index file fix\"), 21 days before this check. Responsiveness 20, Of the 5 discussions that aren't pull requests (13 September to 5 November 2026), 4 have a reply from an ibm-granite member. Version policy 5, Granite 4.1 8B 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 + 20 + 20 + 5 + 10 = 92.\n- Kolibri-1 (70): The model card earns 20 with all seven items. Training data earns 15 for a data card with the mix, the filtering and the cut-off, short of a dataset list with sizes. The last change earns 20, for template and card commits on 20 October 2026. Responsiveness earns 5, since Aleph Alpha replied in 2 of 14 discussions across both repositories and none since 13 October. Version policy earns 5 for a first release. File health earns 5, because the files agree but a report of a broken tool-call parser in the publisher's container image has waited since 7 October without a reply.\n\n### Risk: Kolibri-1 +10\n\n- Granite 4.2 8B (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- Kolibri-1 (65): File safety earns 25 for safetensors only, with no repository code run on load. Provenance earns 15, for the Aleph-Alpha organisation the launch post links and a SHA-256 for every weight shard. Safety evaluation earns 0, because the card says refusals were measured and neither the card nor the technical report publishes results. Agent guidance earns 10, since the card says what the trained refusals cover and which safeguards to add around the model, without prompt-injection guidance. Licence risk earns the full 15 under unchanged Apache 2.0. The security programme earns 0, as no contact, policy or advisories were found and security.txt answered 403 on 3 November, so it is scored as absent and left open.\n\n### Agent interface: Kolibri-1 +5\n\n- Granite 4.2 8B (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- Kolibri-1 (90): The chat template earns 20 as Jinja in tokenizer_config.json. The tool-calling format earns 25, because the card documents how tools are declared and how calls come back and names the kolibri1 parser for vLLM with a full example. Trained tool calling earns 20, from training on tool environments and a template built for several calls in one turn. Structured output earns 5, since the card claims it without saying how to ask. Reasoning control earns 10 for effort levels and an off switch per request, with the reasoning returned in a separate field. Context earns 10 for 262,144 tokens trained.\n\n## Score by category\n\n| Category | Weight | Granite 4.2 8B | Kolibri-1 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 95 | 65 | Granite 4.2 8B +30 |\n| Risk | 20% (20 this run) | 55 | 65 | Kolibri-1 +10 |\n| Agent interface | 10% (10 this run) | 85 | 90 | Kolibri-1 +5 |\n| Documentation and upkeep | 10% (10 this run) | 92 | 70 | Granite 4.2 8B +22 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Granite 4.2 8B | Kolibri-1 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | IBM | Aleph Alpha |\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 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-20 | 2026-10-19 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | none | 3.3/5 (4) |\n\n## How the scores have moved\n\n### Granite 4.2 8B\n\nNo score change since it was listed.\n\n### Kolibri-1\n\n- no change on 3 November 2026: product\n- -14.8 on 21 October 2026 (BB to C): checklist\n\n## Verdicts\n\n**Granite 4.2 8B.** IBM's mid-size reasoning cousin, with the same strengths as the 3B. It runs in every runtime we count from IBM's own builds on a 16 GB machine, 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**Kolibri-1.** Kolibri-1 is a German and English reasoning model with a documented tool-call format, switchable thinking and a 262,144-token context, under Apache 2.0. Aleph Alpha's FP8 build needs about 95 GB and runs on vLLM and Transformers 5.20; llama.cpp and Ollama 0.40.14 need a GGUF. The main risk is no published safety results or security contact, and few repository answers.\n\n## Before you call either\n\n### Granite 4.2 8B\n\n1. Start it with `ollama run granite4.2:8b`, or `llama-server -hf ibm-granite/granite-4.2-8b-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### Kolibri-1\n\n1. Start it with pip install 'aleph-alpha-inference\u003e=1' then vllm serve Aleph-Alpha/Kolibri-1 --kv-cache-dtype fp8 --reasoning-parser kolibri1 --tool-call-parser kolibri1 --enable-auto-tool-choice\n2. Without the kolibri1 tool-call parser and enable-auto-tool-choice, tool calls come back as text in tool_call tags\n3. The plugin supports one vLLM minor version, 0.29. vLLM 0.33.0 loads the model without it, but has no kolibri1 tool or reasoning parser\n4. Thinking is on by default at high effort. Pass reasoning_effort none or enable_thinking false in chat_template_kwargs for short tasks\n5. Recommended sampling is temperature 1.0, top_p 0.97 and top_k 128\n6. Past 262,144 tokens, add --max-model-len 1048576 and the max_position_embeddings override in --hf-overrides, though Aleph Alpha recommends staying at or below 262,144\n7. With Transformers 5.20 or later, tokenizer.parse_response returns the answer in content and the reasoning in thinking\n8. On the reference machine the FP8 build fits by size, but no document confirms vLLM runs FP8 on gfx1151, and Aleph Alpha ships no GGUF for llama.cpp or Ollama\n9. Validate tool results in your own code, as the card asks\n\n## Questions\n\n### Which is better for AI agents, Granite 4.2 8B or Kolibri-1?\n\nGranite 4.2 8B and Kolibri-1 score within a point of each other on agent readiness, 61 (C) and 61 (C). Kolibri-1 leads on risk and agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/granite-4-2-8b-vs-kolibri-1.json, and with the fewest tokens: https://www.anchorterminal.com/compare/granite-4-2-8b-vs-kolibri-1.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"granite-4-2-8b\", \"b\": \"kolibri-1\"}`. From a terminal: `anchor compare granite-4-2-8b kolibri-1`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/granite-4-2-8b.json and https://www.anchorterminal.com/api/v1/tools/kolibri-1.json\n\n## Other comparisons with Granite 4.2 8B or Kolibri-1\n\n- [Agents-A1-4B vs Granite 4.2 8B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-granite-4-2-8b.md)\n- [Agents-A1-4B vs Kolibri-1](https://www.anchorterminal.com/compare/agents-a1-4b-vs-kolibri-1.md)\n- [Apertus v1.5 8B vs Granite 4.2 8B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-granite-4-2-8b.md)\n- [Apertus v1.5 8B vs Kolibri-1](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kolibri-1.md)\n- [Apodex-1.1-mini vs Granite 4.2 8B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-granite-4-2-8b.md)\n- [Apodex-1.1-mini vs Kolibri-1](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-kolibri-1.md)\n- [Arete-27B vs Granite 4.2 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