{
  "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": "Muse Glimmer 30B scores 70 (BB) on agent readiness against Granite 4.2 8B's 61 (C), and leads in 1 of 4 scored categories. Granite 4.2 8B leads on reach, agent interface and documentation and upkeep.",
    "b": {
      "slug": "muse-glimmer-30b",
      "name": "Muse Glimmer 30B",
      "vendor": "Meta",
      "vendorUrl": "https://dev.meta.ai/docs/muse-glimmer",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Muse Glimmer 30B is Meta's open-weight dense multimodal model for local agent work, 29.6B parameters with text and image input and a 128K-token context, published on Hugging Face under Apache 2.0 with GGUF and ExecuTorch builds.",
      "url": "https://www.anchorterminal.com/tools/muse-glimmer-30b",
      "markdownUrl": "https://www.anchorterminal.com/tools/muse-glimmer-30b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/muse-glimmer-30b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/muse-glimmer-30b.json",
      "dns": "muse-glimmer-30b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/meta-models/Muse-Glimmer-30B",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "meta-models/Muse-Glimmer-30B"
        },
        {
          "registry": "huggingface",
          "name": "meta-models/Muse-Glimmer-30B-GGUF"
        },
        {
          "registry": "huggingface",
          "name": "meta-models/Muse-Glimmer-30B-assistant"
        },
        {
          "registry": "huggingface",
          "name": "meta-models/Muse-Glimmer-30B-ExecuTorch-PTE"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of the four repositories is gated (checked 2026-10-21), and Meta's docs say there is no API key and no gated access.",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0, with a separate Muse Glimmer Usage Policy listing prohibited uses. Meta Model API doesn't serve it. Running it yourself costs the hardware, 17 GB for Meta's Q4_K_M GGUF or about 60 GB in BF16 (checked 2026-10-21).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-21"
      },
      "docsUrl": "https://dev.meta.ai/docs/muse-glimmer",
      "llmsTxt": "https://dev.meta.ai/llms.txt",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "multimodal",
        "reasoning",
        "tool-calling",
        "apache-2.0",
        "safetensors",
        "gguf",
        "self-hosted",
        "new"
      ],
      "lastRelease": "2026-10-20",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 70,
        "grade": "BB",
        "agentReady": true,
        "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": 5,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "capability": 50,
          "interface": 75,
          "reach": 90,
          "risk": 80,
          "upkeep": 65
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-11-01"
        },
        "negative": 0,
        "verdict": "A dense 30B multimodal cousin for local agent and coding work with images, documented tool calls and adjustable reasoning. Meta's 17 GB GGUF fits a 24 GB card and runs in llama.cpp, Ollama, vLLM, SGLang and Transformers. The main risks are one tool call per turn, no documented JSON output and a usage policy that adds limits to Apache 2.0.",
        "bestFor": "Local agents and coding with image input on one 24 to 32 GB GPU or an Apple silicon laptop, where one tool call per turn is enough",
        "strengths": [
          "Meta's own 16.8 GB Q4_K_M GGUF fits a 24 GB card, with a DFlash drafter for speculative decoding",
          "Released model code in vLLM, SGLang, Transformers, llama.cpp and Ollama, no trust_remote_code",
          "Tool format and parser names per runtime documented by Meta, with a Jinja template in the repository",
          "Trained against indirect prompt injection with a principal hierarchy, and injection results published",
          "Ungated download, safetensors and GGUF from Meta's verified organisation, and three live inference providers"
        ],
        "weaknesses": [
          "One tool call per turn and no parallel calls",
          "No structured output documented, and reasoning can't be turned off",
          "A usage policy adds prohibited uses beyond the law to Apache 2.0",
          "Meta replied to 1 of the 40 most recent discussions, and a chat template bug report has waited since 11 August",
          "Safety results are Meta's own, with nothing published to rerun them"
        ],
        "agentNotes": [
          "Start it with `llama serve -hf meta-models/Muse-Glimmer-30B-GGUF --hf-file Muse-Glimmer-30B-KQuant-17GB-Q4_K_M.gguf -ngl 99 -c 131072 --host 127.0.0.1 --port 8080 --chat-template-kwargs '{\"reasoning_strength\":\"high\"}'` on llama.cpp b10353 or later, or `ollama run muse-glimmer` on Ollama 0.32.7 or later",
          "In vLLM pass `--tool-call-parser muse_glimmer --reasoning-parser muse_glimmer`, in SGLang `muse` for both, in ExecuTorch `--tool-parser atem`. Without them tool calls come back as plain text",
          "Set reasoning per request with `chat_template_kwargs.reasoning_strength` (low, medium, high or xhigh). llama.cpp and ExecuTorch don't read `reasoning_effort`, and ExecuTorch rejects it with a 400",
          "Expect one tool call per turn and return each result before the next. ExecuTorch's server rejects `tool_choice=\"required\"`",
          "Keep temperature 1.0, top_p 0.95 and top_k 64, don't run greedy, and never stop on `\u003c|eom|\u003e`",
          "Leave max_tokens room for reasoning, since in llama.cpp a request cut off mid-thought returns empty content with finish_reason length",
          "For images add Meta's 1.4 GB mmproj GGUF, which takes the build to about 22 GB with the runtime's fifth"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 3.8,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "BB",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 70
          }
        ],
        "editorialScores": {
          "capability": 50,
          "interface": 75,
          "reach": 90,
          "risk": 80,
          "upkeep": 65
        },
        "provenanceScore": 73,
        "checked": {
          "categories": {
            "capability": "2026-11-01",
            "interface": "2026-11-01",
            "reach": "2026-11-01",
            "risk": "2026-11-01",
            "upkeep": "2026-11-01"
          },
          "last": "2026-11-01",
          "oldest": "2026-11-01",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama run muse-glimmer\n# or Meta's 17 GB GGUF with llama.cpp b10353 or later:\nllama serve -hf meta-models/Muse-Glimmer-30B-GGUF \\\n  --hf-file Muse-Glimmer-30B-KQuant-17GB-Q4_K_M.gguf \\\n  -ngl 99 -c 131072 --host 127.0.0.1 --port 8080 \\\n  --chat-template-kwargs '{\"reasoning_strength\":\"high\"}'",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"muse-glimmer\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/muse-glimmer-30b"
      },
      "sameCompany": [
        "meta-model-api",
        "muse-ember",
        "muse-spark-open",
        "muse-code"
      ],
      "weights": {
        "trainedBy": "Meta",
        "builtOn": "Distilled from Muse Spark",
        "params": "29.6B",
        "context": 131072,
        "goodFor": "Dense multimodal model for local agent work, text and images in, with tool calls and always-on adjustable reasoning.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Muse-Glimmer-30B-KQuant-Dynamic-Q4_K_XL",
            "url": "https://huggingface.co/meta-models/Muse-Glimmer-30B-GGUF/blob/main/Muse-Glimmer-30B-KQuant-Dynamic-Q4_K_XL.gguf",
            "who": "publisher",
            "precision": "q4_k_xl",
            "sizeGB": 19.7,
            "memoryGB": 24,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)",
              "LM Studio"
            ],
            "note": "Meta's dynamic K-quant; the card targets 32 GB. Add mmproj-Muse-Glimmer-30B-Q4_K_M.gguf (1.4 GB) for images"
          },
          {
            "name": "Muse-Glimmer-30B-KQuant-17GB-Q4_K_M",
            "url": "https://huggingface.co/meta-models/Muse-Glimmer-30B-GGUF/blob/main/Muse-Glimmer-30B-KQuant-17GB-Q4_K_M.gguf",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 16.8,
            "memoryGB": 24,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)",
              "LM Studio"
            ],
            "note": "The card targets 24 GB. Add the 1.4 GB mmproj for images and the 1.6 GB DFlash drafter for speculative decoding"
          },
          {
            "name": "Muse-Glimmer-30B-ExecuTorch-PTE (smallest: k-quant-17G text-solo, Metal)",
            "url": "https://huggingface.co/meta-models/Muse-Glimmer-30B-ExecuTorch-PTE",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 17.9,
            "memoryGB": 24,
            "format": "executorch",
            "note": "16 ExecuTorch exports from 17.9 to 31.4 GB, for Apple Metal or NVIDIA (sm80) only"
          },
          {
            "name": "ollama muse-glimmer:latest",
            "url": "https://ollama.com/library/muse-glimmer:latest",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 18,
            "memoryGB": 24,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Q4_K_M with a 1.4 GB vision projector. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant"
          },
          {
            "name": "Muse-Glimmer-30B",
            "url": "https://huggingface.co/meta-models/Muse-Glimmer-30B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 59.6,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          }
        ],
        "run": "llama serve -hf meta-models/Muse-Glimmer-30B-GGUF --hf-file Muse-Glimmer-30B-KQuant-17GB-Q4_K_M.gguf -ngl 99 -c 131072 --host 127.0.0.1 --port 8080 --chat-template-kwargs '{\"reasoning_strength\":\"high\"}'",
        "runBuild": "Muse-Glimmer-30B-KQuant-17GB-Q4_K_M",
        "checked": "2026-10-21",
        "minMemoryGB": 24
      },
      "family": {
        "lab": "Meta",
        "family": "Muse",
        "url": "https://www.anchorterminal.com/open-weights/#meta",
        "others": [
          {
            "name": "Muse Glimmer 30B assistant (DFlash drafter)",
            "url": "https://huggingface.co/meta-models/Muse-Glimmer-30B-assistant",
            "params": "2.56B (Hub safetensors count; the card states no figure)"
          },
          {
            "name": "Muse Spark Open 816B-A42B",
            "url": "https://huggingface.co/meta-models/Muse-Spark-Open-816B-A42B",
            "params": "816B total, 42B active (MoE)",
            "listing": "https://www.anchorterminal.com/tools/muse-spark-open"
          }
        ],
        "olderCount": 50,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Meta (the verified Hugging Face organisation is \"Meta Inc.\", the card names Meta Superintelligence Lab, and the licence carries no copyright line)",
        "domain": "meta.ai",
        "domainRegistered": "2017-12-16",
        "domainNote": "Registration date as recorded by RDAP for the muse-spark-open listing.",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/meta-models/Muse-Glimmer-30B/commits/main",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "meta-models is a verified Hugging Face organisation named Meta Inc. Meta's launch post on research.meta.ai and its developer docs on dev.meta.ai link to huggingface.co/meta-models/Muse-Glimmer-30B, and the same organisation holds Muse Spark Open.",
          "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. Some messages say what changed (the duplicate reasoning-strength fix on 11 August); 7 of the 15 read Update README.md.",
          "dev.meta.ai/.well-known/security.txt and research.meta.ai/.well-known/security.txt answered 404 on 21 October 2026, and ai.meta.com's redirected and wasn't followed. The usage policy sends bugs and security concerns to facebook.com/whitehat/info."
        ],
        "score": 73
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/muse-glimmer-30b.json",
      "live": {
        "slug": "muse-glimmer-30b",
        "securityTxt": {
          "url": "https://meta.ai/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-11-10T16:08:58.767810687Z"
        },
        "llmsTxt": {
          "url": "https://dev.meta.ai/llms.txt",
          "ok": true,
          "status": 200,
          "checkedAt": "2026-11-10T15:44:02.548249718Z"
        },
        "domain": {
          "domain": "meta.ai",
          "registered": "2017-12-16",
          "source": "https://rdap.identitydigital.services/rdap/domain/meta.ai",
          "checkedAt": "2026-11-08T14:51:01.706547979Z"
        },
        "pages": [
          {
            "url": "https://huggingface.co/meta-models/Muse-Glimmer-30B/commits/main",
            "kind": "changelog",
            "status": 200,
            "checkedAt": "2026-11-10T16:23:04.598495005Z",
            "changedAt": "2026-11-10T16:23:04.598495005Z",
            "fingerprint": "fe9fc096c77c"
          }
        ],
        "updatedAt": "2026-11-10T16:23:04.598495005Z"
      }
    },
    "edges": [
      {
        "by": 27,
        "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 20, all seven parts present, languages as a count of more than 100. Training data 10, a general description with a 4 January 2026 cut-off and no sources or sizes. Last change 20, a card update on 20 October citing the technical report, read as a change to the files as card edits were for other listings; Meta added its own MMI evaluation results file on 22 October. Responsiveness 5, a Meta member replied to 1 of the 40 most recent non-PR discussions (re-counted on 1 November). Version policy 5, a first release. File health 5, the files agree but a chat template bug report from 11 August still has no reply."
        },
        "scores": {
          "a": 92,
          "b": 65
        }
      },
      {
        "by": 25,
        "category": "risk",
        "leads": "muse-glimmer-30b",
        "name": "Risk",
        "reasons": {
          "a": "File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the ibm-granite organisation, which IBM's Granite docs and the GitHub README link to (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions. Licence risk 15, Apache 2.0 has no use policy, field-of-use limit, threshold or change clause, and the card's licence link resolves. Security programme 5, www.ibm.com/.well-known/security.txt names IBM PSIRT, read for the 3B listing on 4 November 2026 (5). 15 + 15 + 0 + 5 + 15 + 5 = 55.",
          "b": "File safety 25, safetensors and GGUF, with the ExecuTorch pos_embed.bin files read as raw embedding tables by their exact size and the Hub's scans, and no repository code runs. Provenance 15, the verified meta-models organisation Meta links, with a checksum on every file. Safety evaluation 10, Meta's own published results with stated set-ups but nothing to rerun and no outside assessor. Agent guidance 15, trained against indirect prompt injection with a principal hierarchy, and results published. Licence risk 10, 5 off for the use policy, and the policy's pointer to a latest copy leads back to the same repository file with no change clause. Security programme 5, a whitehat route in the usage policy, with no security.txt and public advisories unchecked, so scored as absent."
        },
        "scores": {
          "a": 55,
          "b": 80
        }
      },
      {
        "by": 10,
        "category": "interface",
        "leads": "granite-4-2-8b",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, chat_template.jinja in the repository, with roles, tools, tool responses, the thinking switch and low effort. Tool-calling format 25, the card has tool-calling sections for vLLM and SGLang, declares tools with OpenAI's function schema and names the parser to use, qwen3_coder, which both runtimes ship in the released versions read. Trained tool calling 20, the template loops over every entry in an assistant turn's tool_calls, so it renders several calls in one turn, and the card names tool calling and agentic workflows as what it's best for. Structured output 0, the card doesn't document JSON or JSON Schema output or how to ask for it. Reasoning control 10, the card documents thinking on by default, non-thinking and low-effort modes per request through the template (enable_thinking and low_effort), with the reasoning returned apart from the answer. Context 10, 131,072 tokens natively per the card and config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85.",
          "b": "Chat template 20, a Jinja file in the repository. Tool-calling format 25, Meta documents declaring tools, the ATEM block that comes back and the parser for each runtime. Trained tool calling 10, one call per turn and no parallel calls. Structured output 0, nothing documented for JSON or JSON Schema. Reasoning control 10, reasoning can't be turned off but is budgeted per request with four strength levels and returned as reasoning_content. Context 10, 131,072 tokens trained in."
        },
        "scores": {
          "a": 85,
          "b": 75
        }
      },
      {
        "by": 5,
        "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 25, Meta's 16.8 GB Q4_K_M GGUF needs about 20 GB with the runtime's fifth, so it fits a 24 GB card but not 16 GB. Runtimes 25, released model code in vLLM, SGLang, Transformers, llama.cpp and Ollama's library fills the cap. Builds 15, Meta publishes GGUF and ExecuTorch 4-bit builds, read as two kinds as gemma-4-31b-it was, though all are 4-bit k-quants. Terms 10, Apache 2.0 with a usage policy that adds limits beyond the law, so a free commercial licence under a use policy. Download 10, no repository is gated. Hosted 5, Meta's API doesn't serve it but Together, Featherless AI and DeepInfra are live on its Hub page."
        },
        "scores": {
          "a": 95,
          "b": 90
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "IBM",
        "b": "Meta",
        "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": "yes",
        "name": "llms.txt"
      },
      {
        "a": "2026-10-20",
        "b": "2026-10-20",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "none",
        "b": "3.8/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Muse Glimmer 30B scores 70 (BB) on agent readiness against Granite 4.2 8B's 61 (C), and leads in 1 of 4 scored categories. Granite 4.2 8B leads on reach, agent interface and documentation and upkeep.",
        "question": "Which is better for AI agents, Granite 4.2 8B or Muse Glimmer 30B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 95 against 90",
          "Agent interface, 85 against 75",
          "Documentation and upkeep, 92 against 65"
        ],
        "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, 80 against 55"
        ],
        "also": [
          "Agent-ready, a grade of BB or better"
        ],
        "goodFor": "Local agents and coding with image input on one 24 to 32 GB GPU or an Apple silicon laptop, where one tool call per turn is enough",
        "slug": "muse-glimmer-30b",
        "watchFor": "One tool call per turn and no parallel calls"
      }
    ],
    "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-muse-glimmer-30b.json",
        "title": "Agents-A1-4B vs Muse Glimmer 30B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-muse-glimmer-30b"
      },
      {
        "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-muse-glimmer-30b.json",
        "title": "Apertus v1.5 8B vs Muse Glimmer 30B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-muse-glimmer-30b"
      },
      {
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-muse-glimmer-30b.json",
        "title": "Apodex-1.1-mini vs Muse Glimmer 30B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-muse-glimmer-30b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-granite-4-2-8b.json",
        "title": "Arete-27B vs Granite 4.2 8B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-granite-4-2-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-muse-glimmer-30b.json",
        "title": "Arete-27B vs Muse Glimmer 30B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-muse-glimmer-30b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-granite-4-2-8b.json",
        "title": "BigBang-v1 vs Granite 4.2 8B",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-granite-4-2-8b"
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        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-wevisone-vl-2b.json",
        "title": "Muse Glimmer 30B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-wevisone-vl-4b.json",
        "title": "Muse Glimmer 30B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-zaya1-8b.json",
        "title": "Muse Glimmer 30B vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-zaya1-8b"
      }
    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 5,
        "edge": "granite-4-2-8b",
        "granite-4-2-8b": 95,
        "key": "reach",
        "muse-glimmer-30b": 90,
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 25,
        "edge": "muse-glimmer-30b",
        "granite-4-2-8b": 55,
        "key": "risk",
        "muse-glimmer-30b": 80,
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 10,
        "edge": "granite-4-2-8b",
        "granite-4-2-8b": 85,
        "key": "interface",
        "muse-glimmer-30b": 75,
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 27,
        "edge": "granite-4-2-8b",
        "granite-4-2-8b": 92,
        "key": "upkeep",
        "muse-glimmer-30b": 65,
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "Muse Glimmer 30B scores 70 (BB) on agent readiness against Granite 4.2 8B's 61 (C), and leads in 1 of 4 scored categories. Granite 4.2 8B leads on reach, agent interface and documentation and upkeep. 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.",
      "muse-glimmer-30b": "A dense 30B multimodal cousin for local agent and coding work with images, documented tool calls and adjustable reasoning. Meta's 17 GB GGUF fits a 24 GB card and runs in llama.cpp, Ollama, vLLM, SGLang and Transformers. The main risks are one tool call per turn, no documented JSON output and a usage policy that adds limits to Apache 2.0."
    }
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  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-muse-glimmer-30b",
    "json": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-muse-glimmer-30b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-muse-glimmer-30b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-muse-glimmer-30b.min.md"
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  "markdown": "Muse Glimmer 30B scores 70 (BB) on agent readiness against Granite 4.2 8B's 61 (C), and leads in 1 of 4 scored categories. Granite 4.2 8B leads on reach, agent interface and documentation and upkeep. 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- Muse Glimmer 30B: grade BB, 70/100, rank #7 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/muse-glimmer-30b.md · JSON https://www.anchorterminal.com/api/v1/tools/muse-glimmer-30b.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 90\n- Agent interface, 85 against 75\n- Documentation and upkeep, 92 against 65\n\nWatch for: No safety results published for it\n\n### Muse Glimmer 30B (BB)\n\nGood for: Local agents and coding with image input on one 24 to 32 GB GPU or an Apple silicon laptop, where one tool call per turn is enough\n\nAhead on:\n- Risk, 80 against 55\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n\nWatch for: One tool call per turn and no parallel calls\n\n\n## Why the edge\n\n### Documentation and upkeep: Granite 4.2 8B +27\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- Muse Glimmer 30B (65): Model card 20, all seven parts present, languages as a count of more than 100. Training data 10, a general description with a 4 January 2026 cut-off and no sources or sizes. Last change 20, a card update on 20 October citing the technical report, read as a change to the files as card edits were for other listings; Meta added its own MMI evaluation results file on 22 October. Responsiveness 5, a Meta member replied to 1 of the 40 most recent non-PR discussions (re-counted on 1 November). Version policy 5, a first release. File health 5, the files agree but a chat template bug report from 11 August still has no reply.\n\n### Risk: Muse Glimmer 30B +25\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- Muse Glimmer 30B (80): File safety 25, safetensors and GGUF, with the ExecuTorch pos_embed.bin files read as raw embedding tables by their exact size and the Hub's scans, and no repository code runs. Provenance 15, the verified meta-models organisation Meta links, with a checksum on every file. Safety evaluation 10, Meta's own published results with stated set-ups but nothing to rerun and no outside assessor. Agent guidance 15, trained against indirect prompt injection with a principal hierarchy, and results published. Licence risk 10, 5 off for the use policy, and the policy's pointer to a latest copy leads back to the same repository file with no change clause. Security programme 5, a whitehat route in the usage policy, with no security.txt and public advisories unchecked, so scored as absent.\n\n### Agent interface: Granite 4.2 8B +10\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- Muse Glimmer 30B (75): Chat template 20, a Jinja file in the repository. Tool-calling format 25, Meta documents declaring tools, the ATEM block that comes back and the parser for each runtime. Trained tool calling 10, one call per turn and no parallel calls. Structured output 0, nothing documented for JSON or JSON Schema. Reasoning control 10, reasoning can't be turned off but is budgeted per request with four strength levels and returned as reasoning_content. Context 10, 131,072 tokens trained in.\n\n### Reach: Granite 4.2 8B +5\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- Muse Glimmer 30B (90): Memory 25, Meta's 16.8 GB Q4_K_M GGUF needs about 20 GB with the runtime's fifth, so it fits a 24 GB card but not 16 GB. Runtimes 25, released model code in vLLM, SGLang, Transformers, llama.cpp and Ollama's library fills the cap. Builds 15, Meta publishes GGUF and ExecuTorch 4-bit builds, read as two kinds as gemma-4-31b-it was, though all are 4-bit k-quants. Terms 10, Apache 2.0 with a usage policy that adds limits beyond the law, so a free commercial licence under a use policy. Download 10, no repository is gated. Hosted 5, Meta's API doesn't serve it but Together, Featherless AI and DeepInfra are live on its Hub page.\n\n## Score by category\n\n| Category | Weight | Granite 4.2 8B | Muse Glimmer 30B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 95 | 90 | Granite 4.2 8B +5 |\n| Risk | 20% (20 this run) | 55 | 80 | Muse Glimmer 30B +25 |\n| Agent interface | 10% (10 this run) | 85 | 75 | Granite 4.2 8B +10 |\n| Documentation and upkeep | 10% (10 this run) | 92 | 65 | Granite 4.2 8B +27 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **70 · BB** | |\n\n## Facts side by side\n\n| Fact | Granite 4.2 8B | Muse Glimmer 30B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | IBM | Meta |\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 | yes |\n| Last release | 2026-10-20 | 2026-10-20 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | none | 3.8/5 (4) |\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**Muse Glimmer 30B.** A dense 30B multimodal cousin for local agent and coding work with images, documented tool calls and adjustable reasoning. Meta's 17 GB GGUF fits a 24 GB card and runs in llama.cpp, Ollama, vLLM, SGLang and Transformers. The main risks are one tool call per turn, no documented JSON output and a usage policy that adds limits to Apache 2.0.\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### Muse Glimmer 30B\n\n1. Start it with `llama serve -hf meta-models/Muse-Glimmer-30B-GGUF --hf-file Muse-Glimmer-30B-KQuant-17GB-Q4_K_M.gguf -ngl 99 -c 131072 --host 127.0.0.1 --port 8080 --chat-template-kwargs '{\"reasoning_strength\":\"high\"}'` on llama.cpp b10353 or later, or `ollama run muse-glimmer` on Ollama 0.32.7 or later\n2. In vLLM pass `--tool-call-parser muse_glimmer --reasoning-parser muse_glimmer`, in SGLang `muse` for both, in ExecuTorch `--tool-parser atem`. Without them tool calls come back as plain text\n3. Set reasoning per request with `chat_template_kwargs.reasoning_strength` (low, medium, high or xhigh). llama.cpp and ExecuTorch don't read `reasoning_effort`, and ExecuTorch rejects it with a 400\n4. Expect one tool call per turn and return each result before the next. ExecuTorch's server rejects `tool_choice=\"required\"`\n5. Keep temperature 1.0, top_p 0.95 and top_k 64, don't run greedy, and never stop on `\u003c|eom|\u003e`\n6. Leave max_tokens room for reasoning, since in llama.cpp a request cut off mid-thought returns empty content with finish_reason length\n7. For images add Meta's 1.4 GB mmproj GGUF, which takes the build to about 22 GB with the runtime's fifth\n\n## Questions\n\n### Which is better for AI agents, Granite 4.2 8B or Muse Glimmer 30B?\n\nMuse Glimmer 30B scores 70 (BB) on agent readiness against Granite 4.2 8B's 61 (C), and leads in 1 of 4 scored categories. Granite 4.2 8B leads on reach, agent interface and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/granite-4-2-8b-vs-muse-glimmer-30b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/granite-4-2-8b-vs-muse-glimmer-30b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"granite-4-2-8b\", \"b\": \"muse-glimmer-30b\"}`. From a terminal: `anchor compare granite-4-2-8b muse-glimmer-30b`\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/muse-glimmer-30b.json\n\n## Other comparisons with Granite 4.2 8B or Muse Glimmer 30B\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 Muse Glimmer 30B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-muse-glimmer-30b.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 Muse Glimmer 30B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-muse-glimmer-30b.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 Muse Glimmer 30B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-muse-glimmer-30b.md)\n- [Arete-27B 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