{
  "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 Ornith-1.5-122B score within a point of each other on agent readiness, 61 (C) and 61 (C).",
    "b": {
      "slug": "ornith-1-5-122b",
      "name": "Ornith-1.5-122B",
      "vendor": "Ornith",
      "vendorUrl": "https://ornith.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Ornith-1.5-122B is Ornith's open-weight mixture-of-experts reasoning model for coding and agent work, about 125B parameters on Qwen3.5's architecture with a 262,144-token context, published in BF16, FP8, NVFP4, GGUF and MLX and marked MIT.",
      "url": "https://www.anchorterminal.com/tools/ornith-1-5-122b",
      "markdownUrl": "https://www.anchorterminal.com/tools/ornith-1-5-122b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ornith-1-5-122b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ornith-1-5-122b.json",
      "dns": "ornith-1-5-122b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/ornith-ai/Ornith-1.5-122B",
      "license": "MIT",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-122B"
        },
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-122B-GGUF"
        },
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-122B-FP8"
        },
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-122B-NVFP4"
        },
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-122B-MLX-4bit"
        },
        {
          "registry": "ollama",
          "name": "ornith-1.5:122b-q4_K_M"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and Ornith's six build repositories for this size are not gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under the MIT licence named in the card's metadata. The repository has no LICENSE file, and the card's licence link returns 404. Running it yourself costs the hardware, about 250 GB for the BF16 files, 69.7 GB for Ornith's MLX 4-bit build or 75.8 GB for its Q4_K_M GGUF (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/ornith-ai/Ornith-1.5-122B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "reasoning",
        "tool-calling",
        "coding",
        "mit",
        "safetensors",
        "gguf",
        "mlx",
        "fp8",
        "nvfp4",
        "self-hosted"
      ],
      "lastRelease": "2026-10-24",
      "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": 64,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 85,
          "reach": 75,
          "risk": 50,
          "upkeep": 36
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "The largest Ornith that fits a 128 GB machine, a coding and terminal-agent cousin on Qwen3.5-122B-A10B's architecture with XML tool calls and 262,144 tokens of context. Its 4-bit builds need 84 to 93 GB. The main risk is an absent publisher, with no licence file, safety results or replies, and a card that misstates the hardware.",
        "bestFor": "Local coding and terminal agents on a 128 GB unified-memory machine or a 96 GB GPU setup, where a clear licence file and an answering publisher aren't required",
        "strengths": [
          "4-bit builds from Ornith itself in GGUF, MLX and NVFP4, at 70 to 78 GB, plus Ollama's Q4_K_M",
          "Runs in vLLM, SGLang, Transformers, llama.cpp, Ollama and LM Studio from their released versions",
          "The card names the tool and reasoning parsers for vLLM and SGLang, with a tool-call example",
          "262,144 tokens of native context",
          "Safetensors and GGUF only, no gate and no code in the repository"
        ],
        "weaknesses": [
          "No LICENSE file though the card links one, and no notice for the Apache 2.0 Qwen3.5 and Gemma 4 work it builds on",
          "No safety evaluation, agent-use guidance or security contact",
          "The card says a single 80 GB GPU serves it, which its 250 GB of BF16 files don't fit, and names a Mobile variant that isn't published",
          "Ornith answered none of the 5 discussions, and the MLX builds were uploaded again with no note",
          "The two chat templates disagree on keeping earlier reasoning, and structured output isn't documented"
        ],
        "agentNotes": [
          "Start it with `ollama run ornith-1.5:122b-q4_K_M`, or `llama-server -hf ornith-ai/Ornith-1.5-122B-GGUF:Q4_K_M --jinja -c 262144`",
          "Budget about 92 GB for the Q4_K_M GGUF with a working context, and less context if the machine has under 100 GB for the model",
          "In vLLM pass `--enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3`, and in SGLang `--tool-call-parser qwen3_coder --reasoning-parser qwen3`, so tool calls and reasoning come back parsed",
          "Thinking is on by default. Send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention",
          "Pin which template your runtime loads, since chat_template.jinja keeps earlier reasoning and the tokenizer_config.json template drops it",
          "Use temperature 0.6, top_p 0.95, top_k 20 for coding and temperature 1.0 for general tasks, as the card recommends",
          "Add mmproj-Ornith-1.5-122B-BF16.gguf (0.9 GB) for image input in llama.cpp, and validate JSON output yourself, since structured output isn't documented"
        ],
        "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": 75,
          "risk": 50,
          "upkeep": 36
        },
        "provenanceScore": 19,
        "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 ornith-1.5:122b-q4_K_M\n# or, with llama.cpp and Ornith's own GGUF:\nllama-server -hf ornith-ai/Ornith-1.5-122B-GGUF:Q4_K_M --jinja -c 262144\n# or, with the card's vLLM command (abridged):\nvllm serve ornith-ai/Ornith-1.5-122B --served-model-name Ornith-1.5-122B --max-model-len 262144 \\\n  --enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"ornith-1.5:122b-q4_K_M\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/ornith-1-5-122b"
      },
      "sameCompany": [
        "ornith-1-5-35b-a3b",
        "ornith-1-5-397b",
        "ornith-1-5-9b"
      ],
      "weights": {
        "trainedBy": "Ornith",
        "builtOn": "Qwen3.5-122B-A10B, inferred. Not declared in the metadata. Same qwen3_5_moe architecture and an identical parameter count to Qwen3.5-122B-A10B (Apache 2.0); the card says Ornith-1.0 was built on Qwen3.5 and Gemma 4 and that 1.5 extends it",
        "params": "125B",
        "context": 262144,
        "goodFor": "Coding and terminal-agent mixture-of-experts model with tool calling and thinking on by default, text and images in, the largest Ornith that fits a 128 GB machine at 4 bits.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Ornith-1.5-122B-NVFP4",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-NVFP4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 77.7,
            "memoryGB": 96,
            "format": "safetensors",
            "note": "NVFP4 needs NVIDIA Blackwell"
          },
          {
            "name": "Ornith-1.5-122B-Q4_K_M",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-GGUF/blob/main/Ornith-1.5-122B-Q4_K_M.gguf",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 75.8,
            "memoryGB": 96,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "Add mmproj-Ornith-1.5-122B-BF16.gguf (0.9 GB) for image input"
          },
          {
            "name": "Ornith-1.5-122B-MLX-4bit",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-MLX-4bit",
            "who": "publisher",
            "precision": "4bit",
            "sizeGB": 69.7,
            "memoryGB": 96,
            "format": "mlx",
            "note": "MLX runs on Apple silicon only"
          },
          {
            "name": "ollama ornith-1.5:122b-q4_K_M",
            "url": "https://ollama.com/library/ornith-1.5:122b-q4_K_M",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 77,
            "memoryGB": 96,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Q4_K_M (76 GB) with a BF16 vision projector (912 MB), as Ollama's tag page shows it, rounded to whole GB. The 73 GB ornith-1.5:122b tag is marked for MLX and llama.cpp and its quantisation wasn't established, so it isn't listed as a build"
          },
          {
            "name": "Ornith-1.5-122B-Q6_K",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-GGUF/blob/main/Ornith-1.5-122B-Q6_K.gguf",
            "who": "publisher",
            "precision": "q6_k",
            "sizeGB": 102.4,
            "memoryGB": 128,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          },
          {
            "name": "Ornith-1.5-122B-Q5_K_M",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-GGUF/blob/main/Ornith-1.5-122B-Q5_K_M.gguf",
            "who": "publisher",
            "precision": "q5_k_m",
            "sizeGB": 88.7,
            "memoryGB": 128,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          },
          {
            "name": "Ornith-1.5-122B-FP8",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 133,
            "memoryGB": 192,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang"
            ],
            "note": "Too big for the 128 GB reference machine"
          },
          {
            "name": "Ornith-1.5-122B-Q8_0",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-GGUF/blob/main/Ornith-1.5-122B-Q8_0.gguf",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 132.6,
            "memoryGB": 192,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "Too big for the 128 GB reference machine"
          },
          {
            "name": "Ornith-1.5-122B-MLX-8bit",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-MLX-8bit",
            "who": "publisher",
            "precision": "8bit",
            "sizeGB": 130.7,
            "memoryGB": 192,
            "format": "mlx",
            "note": "MLX runs on Apple silicon only"
          },
          {
            "name": "Ornith-1.5-122B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 250.2,
            "memoryGB": 512,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ],
            "note": "Too big for the 128 GB reference machine"
          },
          {
            "name": "Ornith-1.5-122B-BF16",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-GGUF/blob/main/Ornith-1.5-122B-BF16.gguf",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 249.4,
            "memoryGB": 512,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "Too big for the 128 GB reference machine"
          },
          {
            "name": "Ornith-1.5-122B-MLX",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-MLX",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 245.2,
            "memoryGB": 512,
            "format": "mlx",
            "note": "MLX runs on Apple silicon only"
          }
        ],
        "run": "ollama run ornith-1.5:122b-q4_K_M",
        "runBuild": "ollama ornith-1.5:122b-q4_K_M",
        "checked": "2026-11-10",
        "minMemoryGB": 96
      },
      "family": {
        "lab": "Ornith",
        "family": "Ornith-1",
        "url": "https://www.anchorterminal.com/open-weights/#ornith",
        "others": [
          {
            "name": "Ornith-1.0-9B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.0-9B",
            "params": "9B (dense)"
          },
          {
            "name": "Ornith-1.0-35B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.0-35B",
            "params": "35B (MoE)"
          },
          {
            "name": "Ornith-1.0-397B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.0-397B",
            "params": "397B (MoE)"
          },
          {
            "name": "Ornith-1.5-9B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B",
            "params": "9B (dense)",
            "listing": "https://www.anchorterminal.com/tools/ornith-1-5-9b"
          },
          {
            "name": "Ornith-1.5-9B-DFlash",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-DFlash",
            "params": "1.29B (Hub safetensors count)"
          },
          {
            "name": "Ornith-1.5-35B-A3B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B",
            "params": "35B total, 3B active (MoE)",
            "listing": "https://www.anchorterminal.com/tools/ornith-1-5-35b-a3b"
          },
          {
            "name": "Ornith-1.5-35B-A3B-DFlash",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-DFlash",
            "params": "0.39B (Hub safetensors count)"
          },
          {
            "name": "Ornith-1.5-397B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B",
            "params": "397B (MoE)",
            "listing": "https://www.anchorterminal.com/tools/ornith-1-5-397b"
          },
          {
            "name": "Ornith-1.5-397B-DFlash",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-DFlash",
            "params": "1.29B (Hub safetensors count)"
          }
        ]
      },
      "area": "models",
      "provenance": {
        "legalEntity": "",
        "domain": "ornith.ai",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "No legal entity named on the card, the blog or the repository. The citation reads Ornith Team. Ollama's copy of the licence names deepreinforce.ai as the copyright holder.",
          "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored. The licence is MIT in the card's metadata only; the LICENSE file the card links returns 404.",
          "No changelog for the files. The commit messages are uploads, deletions and README updates.",
          "ornith.ai/.well-known/security.txt redirects to the home page, so there is no security.txt.",
          "unchecked: the domain's registration date."
        ],
        "score": 19
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/ornith-1-5-122b.json"
    },
    "edges": [
      {
        "by": 56,
        "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": "Model card 11, it covers intended use, architecture, parameters and context but not limitations, languages or a knowledge cut-off. Training data 5, a description of the method with no data or cut-off. Last change 20, a folder upload to the MLX builds on 24 October 2026, 17 days ago. Responsiveness 0, Ornith answered none of the 5 questions and reports. Version policy 0, the MLX builds were uploaded a second time four days after the first with no note. File health 0, the card links a missing LICENSE, states hardware its own files don't fit and names a variant that doesn't exist, and the two chat templates differ. 11 + 5 + 20 + 0 + 0 + 0 = 36."
        },
        "scores": {
          "a": 92,
          "b": 36
        }
      },
      {
        "by": 20,
        "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 10, since Ornith's own MLX 4-bit build is 69.7 GB and needs about 83.6 GB with the fifth added, over 64 GB and inside the reference machine's 107.4 GB, as do its Q4_K_M GGUF, its NVFP4 build and Ollama's build. Runtimes 25, the most the line gives, from vLLM 0.33.0, SGLang 0.5.23, Transformers 5.22.2, llama.cpp's qwen35moe architecture, Ollama's library and LM Studio's catalogue for the qwen35moe architecture. Builds 15, Ornith publishes FP8, NVFP4, GGUF and MLX at several precisions. Terms 15, MIT as named in the card's metadata, with nothing added, read by its terms though the repository has no licence file. Download 10, nothing is gated. Hosted 0, Ornith runs no API and no inference provider serves it. 10 + 25 + 15 + 15 + 10 + 0 = 75."
        },
        "scores": {
          "a": 95,
          "b": 75
        }
      },
      {
        "by": 5,
        "category": "risk",
        "leads": "granite-4-2-8b",
        "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 25, safetensors and GGUF only and no Python in any of Ornith's repositories for this size, so the card's --trust-remote-code flag loads nothing from the repository. Provenance 15, the files come from the ornith-ai organisation Ornith's blog links to, with a SHA-256 for every weight file. Safety evaluation 0 and agent guidance 0, nothing published on either. Licence risk 10, MIT carries no policy, threshold or changeable terms, but the card's licence link is broken. Security programme 0, no security.txt, disclosure policy or security contact."
        },
        "scores": {
          "a": 55,
          "b": 50
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "IBM",
        "b": "Ornith",
        "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": "MIT",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-10-20",
        "b": "2026-10-24",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      }
    ],
    "faq": [
      {
        "answer": "Granite 4.2 8B and Ornith-1.5-122B score within a point of each other on agent readiness, 61 (C) and 61 (C).",
        "question": "Which is better for AI agents, Granite 4.2 8B or Ornith-1.5-122B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 95 against 75",
          "Risk, 55 against 50",
          "Documentation and upkeep, 92 against 36"
        ],
        "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": null,
        "also": null,
        "goodFor": "Local coding and terminal agents on a 128 GB unified-memory machine or a 96 GB GPU setup, where a clear licence file and an answering publisher aren't required",
        "slug": "ornith-1-5-122b",
        "watchFor": "No LICENSE file though the card links one, and no notice for the Apache 2.0 Qwen3.5 and Gemma 4 work it builds on"
      }
    ],
    "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-4-2-8b.json",
        "title": "Agents-A1-4B vs Granite 4.2 8B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-granite-4-2-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-122b.json",
        "title": "Agents-A1-4B vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-122b"
      },
      {
        "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",
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      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-recursive-flint.json",
        "title": "Ornith-1.5-122B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-ripple-65b-preview.json",
        "title": "Ornith-1.5-122B vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-sensenova-loop-9b-preview.json",
        "title": "Ornith-1.5-122B vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-sensenova-loop-9b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-smollm3-3b.json",
        "title": "Ornith-1.5-122B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-spark-x2-6-4b.json",
        "title": "Ornith-1.5-122B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-step-3-7-flash.json",
        "title": "Ornith-1.5-122B vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Ornith-1.5-122B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Ornith-1.5-122B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Ornith-1.5-122B vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-unlimited-ocr-2.json",
        "title": "Ornith-1.5-122B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-wevisone-vl-2b.json",
        "title": "Ornith-1.5-122B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-wevisone-vl-4b.json",
        "title": "Ornith-1.5-122B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-zaya1-8b.json",
        "title": "Ornith-1.5-122B vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-zaya1-8b"
      }
    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 20,
        "edge": "granite-4-2-8b",
        "granite-4-2-8b": 95,
        "key": "reach",
        "name": "Reach",
        "ornith-1-5-122b": 75,
        "weight": 25
      },
      {
        "by": 5,
        "edge": "granite-4-2-8b",
        "granite-4-2-8b": 55,
        "key": "risk",
        "name": "Risk",
        "ornith-1-5-122b": 50,
        "weight": 20
      },
      {
        "by": 0,
        "edge": "",
        "granite-4-2-8b": 85,
        "key": "interface",
        "name": "Agent interface",
        "ornith-1-5-122b": 85,
        "weight": 10
      },
      {
        "by": 56,
        "edge": "granite-4-2-8b",
        "granite-4-2-8b": 92,
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "ornith-1-5-122b": 36,
        "weight": 10
      }
    ],
    "summary": "Granite 4.2 8B and Ornith-1.5-122B score within a point of each other on agent readiness, 61 (C) and 61 (C). 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.",
      "ornith-1-5-122b": "The largest Ornith that fits a 128 GB machine, a coding and terminal-agent cousin on Qwen3.5-122B-A10B's architecture with XML tool calls and 262,144 tokens of context. Its 4-bit builds need 84 to 93 GB. The main risk is an absent publisher, with no licence file, safety results or replies, and a card that misstates the hardware."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-ornith-1-5-122b",
    "json": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-ornith-1-5-122b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-ornith-1-5-122b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-ornith-1-5-122b.min.md"
  },
  "markdown": "Granite 4.2 8B and Ornith-1.5-122B score within a point of each other on agent readiness, 61 (C) and 61 (C). 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- Ornith-1.5-122B: grade C, 61/100, rank #81 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/ornith-1-5-122b.md · JSON https://www.anchorterminal.com/api/v1/tools/ornith-1-5-122b.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 75\n- Risk, 55 against 50\n- Documentation and upkeep, 92 against 36\n\nWatch for: No safety results published for it\n\n### Ornith-1.5-122B (C)\n\nGood for: Local coding and terminal agents on a 128 GB unified-memory machine or a 96 GB GPU setup, where a clear licence file and an answering publisher aren't required\n\nWatch for: No LICENSE file though the card links one, and no notice for the Apache 2.0 Qwen3.5 and Gemma 4 work it builds on\n\n\n## Why the edge\n\n### Documentation and upkeep: Granite 4.2 8B +56\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- Ornith-1.5-122B (36): Model card 11, it covers intended use, architecture, parameters and context but not limitations, languages or a knowledge cut-off. Training data 5, a description of the method with no data or cut-off. Last change 20, a folder upload to the MLX builds on 24 October 2026, 17 days ago. Responsiveness 0, Ornith answered none of the 5 questions and reports. Version policy 0, the MLX builds were uploaded a second time four days after the first with no note. File health 0, the card links a missing LICENSE, states hardware its own files don't fit and names a variant that doesn't exist, and the two chat templates differ. 11 + 5 + 20 + 0 + 0 + 0 = 36.\n\n### Reach: Granite 4.2 8B +20\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- Ornith-1.5-122B (75): Memory 10, since Ornith's own MLX 4-bit build is 69.7 GB and needs about 83.6 GB with the fifth added, over 64 GB and inside the reference machine's 107.4 GB, as do its Q4_K_M GGUF, its NVFP4 build and Ollama's build. Runtimes 25, the most the line gives, from vLLM 0.33.0, SGLang 0.5.23, Transformers 5.22.2, llama.cpp's qwen35moe architecture, Ollama's library and LM Studio's catalogue for the qwen35moe architecture. Builds 15, Ornith publishes FP8, NVFP4, GGUF and MLX at several precisions. Terms 15, MIT as named in the card's metadata, with nothing added, read by its terms though the repository has no licence file. Download 10, nothing is gated. Hosted 0, Ornith runs no API and no inference provider serves it. 10 + 25 + 15 + 15 + 10 + 0 = 75.\n\n### Risk: Granite 4.2 8B +5\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- Ornith-1.5-122B (50): File safety 25, safetensors and GGUF only and no Python in any of Ornith's repositories for this size, so the card's --trust-remote-code flag loads nothing from the repository. Provenance 15, the files come from the ornith-ai organisation Ornith's blog links to, with a SHA-256 for every weight file. Safety evaluation 0 and agent guidance 0, nothing published on either. Licence risk 10, MIT carries no policy, threshold or changeable terms, but the card's licence link is broken. Security programme 0, no security.txt, disclosure policy or security contact.\n\n## Score by category\n\n| Category | Weight | Granite 4.2 8B | Ornith-1.5-122B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 95 | 75 | Granite 4.2 8B +20 |\n| Risk | 20% (20 this run) | 55 | 50 | Granite 4.2 8B +5 |\n| Agent interface | 10% (10 this run) | 85 | 85 | even |\n| Documentation and upkeep | 10% (10 this run) | 92 | 36 | Granite 4.2 8B +56 |\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 | Ornith-1.5-122B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | IBM | Ornith |\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 | MIT |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-20 | 2026-10-24 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n\n## Verdicts\n\n**Granite 4.2 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**Ornith-1.5-122B.** The largest Ornith that fits a 128 GB machine, a coding and terminal-agent cousin on Qwen3.5-122B-A10B's architecture with XML tool calls and 262,144 tokens of context. Its 4-bit builds need 84 to 93 GB. The main risk is an absent publisher, with no licence file, safety results or replies, and a card that misstates the hardware.\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### Ornith-1.5-122B\n\n1. Start it with `ollama run ornith-1.5:122b-q4_K_M`, or `llama-server -hf ornith-ai/Ornith-1.5-122B-GGUF:Q4_K_M --jinja -c 262144`\n2. Budget about 92 GB for the Q4_K_M GGUF with a working context, and less context if the machine has under 100 GB for the model\n3. In vLLM pass `--enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3`, and in SGLang `--tool-call-parser qwen3_coder --reasoning-parser qwen3`, so tool calls and reasoning come back parsed\n4. Thinking is on by default. Send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention\n5. Pin which template your runtime loads, since chat_template.jinja keeps earlier reasoning and the tokenizer_config.json template drops it\n6. Use temperature 0.6, top_p 0.95, top_k 20 for coding and temperature 1.0 for general tasks, as the card recommends\n7. Add mmproj-Ornith-1.5-122B-BF16.gguf (0.9 GB) for image input in llama.cpp, and validate JSON output yourself, since structured output isn't documented\n\n## Questions\n\n### Which is better for AI agents, Granite 4.2 8B or Ornith-1.5-122B?\n\nGranite 4.2 8B and Ornith-1.5-122B score within a point of each other on agent readiness, 61 (C) and 61 (C).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/granite-4-2-8b-vs-ornith-1-5-122b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/granite-4-2-8b-vs-ornith-1-5-122b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"granite-4-2-8b\", \"b\": \"ornith-1-5-122b\"}`. From a terminal: `anchor compare granite-4-2-8b ornith-1-5-122b`\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/ornith-1-5-122b.json\n\n## Other comparisons with Granite 4.2 8B or Ornith-1.5-122B\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 Ornith-1.5-122B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-122b.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 Ornith-1.5-122B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-122b.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 Ornith-1.5-122B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ornith-1-5-122b.md)\n- [Arete-27B vs Granite 4.2 8B](https://www.anchorterminal.com/compare/arete-27b-vs-granite-4-2-8b.md)\n- [Arete-27B vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/arete-27b-vs-ornith-1-5-122b.md)\n- [BigBang-v1 vs Granite 4.2 8B](https://www.anchorterminal.com/compare/bigbang-v1-vs-granite-4-2-8b.md)\n- [BigBang-v1 vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/bigbang-v1-vs-ornith-1-5-122b.md)\n- [Carbon-X vs Granite 4.2 8B](https://www.anchorterminal.com/compare/carbon-x-vs-granite-4-2-8b.md)\n- [Carbon-X vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/carbon-x-vs-ornith-1-5-122b.md)\n- [Cola2 vs Granite 4.2 8B](https://www.anchorterminal.com/compare/cola2-vs-granite-4-2-8b.md)\n- [Cola2 vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/cola2-vs-ornith-1-5-122b.md)\n- [Cosmos-Reason2-2B vs Granite 4.2 8B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-granite-4-2-8b.md)\n- [Cosmos-Reason2-2B vs 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    "description": "Granite 4.2 8B and Ornith-1.5-122B score within a point of each other for open-weight models you run yourself, 61 and 61 out of 100. Prices, MCP, x402, uptime and agent notes side by side.",
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    "title": "Granite 4.2 8B vs Ornith-1.5-122B for AI agents (2026)",
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