{
  "data": {
    "a": {
      "slug": "gemma-4-26b-a4b-it",
      "name": "Gemma 4 26B A4B IT",
      "vendor": "Google DeepMind",
      "vendorUrl": "https://gemma.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Gemma 4 26B A4B IT is Google DeepMind's open-weight instruction-tuned mixture-of-experts model, 25.2B parameters with 3.8B active, taking text and images with a 262,144-token context, published on Hugging Face under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it",
      "markdownUrl": "https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/gemma-4-26b-a4b-it.json",
      "repo": "https://huggingface.co/google/gemma-4-26B-A4B-it",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "google/gemma-4-26B-A4B-it"
        },
        {
          "registry": "huggingface",
          "name": "google/gemma-4-26B-A4B-it-qat-q4_0-gguf"
        },
        {
          "registry": "ollama",
          "name": "gemma4:26b"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Google also hosts it as gemma-4-26b-a4b-it on the Gemini API, which needs an API key. Running it yourself costs the hardware, 57.7 GB at BF16 and 14.4 GB at Q4_0 per Google (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://gemma.ai/models/gemma-4/",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "reasoning",
        "tool-calling",
        "apache-2.0",
        "safetensors",
        "gguf",
        "self-hosted"
      ],
      "lastRelease": "2026-07-20",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 72.2,
        "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": 1197,
        "categoryRank": 2,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "capability": 59,
          "interface": 85,
          "reach": 90,
          "risk": 70,
          "upkeep": 65
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A fast mixture-of-experts cousin for general agent work with tools, optional thinking and image input, under Apache 2.0. Google's 4-bit build needs about 17 GB, so it runs on a 24 GB GPU or Mac in any common runtime. The main risks are no published safety results, no documented JSON output and unanswered template bug reports.",
        "bestFor": "Agents on a 24 GB or larger GPU or Mac that want a fast, permissively licensed tool-calling model with images and long context",
        "strengths": [
          "Plain Apache 2.0, no account or gate, safetensors and GGUF only with a checksum on every file",
          "Google's own 4-bit QAT GGUF needs about 17 GB with working space",
          "Released support in vLLM, SGLang, llama.cpp, Ollama, LM Studio and Transformers",
          "Tool declaration and call format documented by Google, with parallel calls and named parsers",
          "262,144 tokens of native context and thinking that turns on or off per request",
          "Also served by the Gemini API and four inference providers"
        ],
        "weaknesses": [
          "No safety evaluation figures published",
          "No documented way to ask for JSON or schema output",
          "Only one quantised precision from Google, no FP8",
          "Files unchanged since 20 July 2026, with chat-template bug reports unanswered",
          "Training data described only by type and cut-off"
        ],
        "agentNotes": [
          "On the reference machine run `llama-server -hf google/gemma-4-26B-A4B-it-qat-q4_0-gguf --hf-file gemma-4-26B_q4_0-it.gguf --jinja -c 65536`, and add the mmproj file for images",
          "With Ollama run `ollama run gemma4:26b` and raise num_ctx or OLLAMA_CONTEXT_LENGTH, since Google's guide says it defaults to 4K below 24 GiB of VRAM and agents want at least 64K",
          "In vLLM or SGLang pass `--tool-call-parser gemma4 --reasoning-parser gemma4` (vLLM also `--enable-auto-tool-choice`), or tool calls come back as text",
          "Thinking is off by default; pass enable_thinking=True or start the system prompt with \u003c|think|\u003e, and drop earlier turns' thinking from history except on tool-call turns",
          "Validate tool names and arguments before running them, and check any JSON yourself"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 3.8,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "BB",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 72.2
          }
        ],
        "editorialScores": {
          "capability": 59,
          "interface": 85,
          "reach": 90,
          "risk": 70,
          "upkeep": 65
        },
        "provenanceScore": 68,
        "checked": {
          "categories": {
            "capability": "2026-10-21",
            "interface": "2026-10-21",
            "reach": "2026-10-21",
            "risk": "2026-10-21",
            "upkeep": "2026-10-21"
          },
          "last": "2026-10-21",
          "oldest": "2026-10-21",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama run gemma4:26b\n# or, with vLLM:\nvllm serve google/gemma-4-26B-A4B-it --max-model-len 262144 --reasoning-parser gemma4 \\\n  --enable-auto-tool-choice --tool-call-parser gemma4",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"gemma4:26b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"options\": {\"num_ctx\": 65536}}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/gemma-4-26b-a4b-it"
      },
      "sameCompany": [
        "embeddinggemma-300m",
        "diffusiongemma-26b-a4b-it",
        "gemma-4-12b-it",
        "gemma-4-31b-it",
        "gemma-4-e2b-it",
        "gemma-4-e4b-it"
      ],
      "sameModel": [
        "gemini-api"
      ],
      "weights": {
        "trainedBy": "Google DeepMind",
        "builtOn": "google/gemma-4-26B-A4B",
        "params": "25.2B",
        "active": "3.8B",
        "context": 262144,
        "goodFor": "General assistant and agent model with tool calling and optional thinking, taking text, images and video frames, fast for its size as a mixture of experts.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "LM Studio",
          "Transformers"
        ],
        "builds": [
          {
            "name": "gemma-4-26B_q4_0-it",
            "url": "https://huggingface.co/google/gemma-4-26B-A4B-it-qat-q4_0-gguf/blob/main/gemma-4-26B_q4_0-it.gguf",
            "who": "publisher",
            "precision": "q4_0",
            "sizeGB": 14.4,
            "memoryGB": 24,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)",
              "LM Studio"
            ],
            "note": "Quantisation-aware trained by Google. Add gemma-4-26B-it-mmproj.gguf (1.2 GB) for image input"
          },
          {
            "name": "ollama gemma4:26b",
            "url": "https://ollama.com/library/gemma4:26b",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 19,
            "memoryGB": 24,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Q4_K_M weights with a BF16 vision projector and a Q8_0 draft model (digest 001e5dafc3c7). Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant"
          },
          {
            "name": "gemma-4-26B-A4B-it",
            "url": "https://huggingface.co/google/gemma-4-26B-A4B-it",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 51.6,
            "memoryGB": 64,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          }
        ],
        "run": "llama-server -hf google/gemma-4-26B-A4B-it-qat-q4_0-gguf --hf-file gemma-4-26B_q4_0-it.gguf --jinja -c 65536",
        "runBuild": "gemma-4-26B_q4_0-it",
        "checked": "2026-10-21",
        "minMemoryGB": 24
      },
      "family": {
        "lab": "Google (Gemma)",
        "family": "Gemma 4",
        "url": "https://www.anchorterminal.com/open-weights/#google",
        "others": [
          {
            "name": "gemma-4-26B-A4B",
            "url": "https://huggingface.co/google/gemma-4-26B-A4B",
            "params": "25.2B total, 3.8B active"
          },
          {
            "name": "gemma-4-31B",
            "url": "https://huggingface.co/google/gemma-4-31B",
            "params": "30.7B"
          },
          {
            "name": "gemma-4-31B-it",
            "url": "https://huggingface.co/google/gemma-4-31B-it",
            "params": "30.7B",
            "listing": "https://www.anchorterminal.com/tools/gemma-4-31b-it"
          },
          {
            "name": "gemma-4-E2B",
            "url": "https://huggingface.co/google/gemma-4-E2B",
            "params": "2.3B effective (5.1B with embeddings)"
          },
          {
            "name": "gemma-4-E2B-it",
            "url": "https://huggingface.co/google/gemma-4-E2B-it",
            "params": "2.3B effective (5.1B with embeddings)",
            "listing": "https://www.anchorterminal.com/tools/gemma-4-e2b-it"
          },
          {
            "name": "gemma-4-E4B",
            "url": "https://huggingface.co/google/gemma-4-E4B",
            "params": "4.5B effective (8B with embeddings)"
          },
          {
            "name": "gemma-4-E4B-it",
            "url": "https://huggingface.co/google/gemma-4-E4B-it",
            "params": "4.5B effective (8B with embeddings)",
            "listing": "https://www.anchorterminal.com/tools/gemma-4-e4b-it"
          },
          {
            "name": "gemma-4-26B-A4B-it-assistant",
            "url": "https://huggingface.co/google/gemma-4-26B-A4B-it-assistant",
            "params": "420M (file metadata on Hugging Face; drafter)"
          },
          {
            "name": "gemma-4-31B-it-assistant",
            "url": "https://huggingface.co/google/gemma-4-31B-it-assistant",
            "params": "470M (file metadata on Hugging Face; drafter)"
          },
          {
            "name": "gemma-4-E2B-it-assistant",
            "url": "https://huggingface.co/google/gemma-4-E2B-it-assistant",
            "params": "78M (file metadata on Hugging Face; drafter)"
          },
          {
            "name": "gemma-4-E4B-it-assistant",
            "url": "https://huggingface.co/google/gemma-4-E4B-it-assistant",
            "params": "79M (file metadata on Hugging Face; drafter)"
          },
          {
            "name": "gemma-4-12B",
            "url": "https://huggingface.co/google/gemma-4-12B",
            "params": "11.95B"
          },
          {
            "name": "gemma-4-12B-it",
            "url": "https://huggingface.co/google/gemma-4-12B-it",
            "params": "11.95B",
            "listing": "https://www.anchorterminal.com/tools/gemma-4-12b-it"
          },
          {
            "name": "gemma-4-12B-it-assistant",
            "url": "https://huggingface.co/google/gemma-4-12B-it-assistant",
            "params": "423M (file metadata on Hugging Face; drafter)"
          },
          {
            "name": "diffusiongemma-26B-A4B-it",
            "url": "https://huggingface.co/google/diffusiongemma-26B-A4B-it",
            "params": "25.2B total, 3.8B active",
            "listing": "https://www.anchorterminal.com/tools/diffusiongemma-26b-a4b-it"
          },
          {
            "name": "embeddinggemma-2",
            "url": "https://huggingface.co/google/embeddinggemma-2",
            "params": "740M"
          }
        ],
        "olderCount": 171,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Google LLC (RDAP registrant of gemma.ai; the card names Google DeepMind as author)",
        "domain": "gemma.ai",
        "domainRegistered": "2017-12-16",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://gemma.ai/models/releases/",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "RDAP names Google LLC as the registrant of gemma.ai, registered 16 December 2017, with MarkMonitor as registrar and Google's name servers. ai.google.dev's Gemma pages say the documentation moved to gemma.ai.",
          "Nothing is hosted under this listing, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.",
          "gemma.ai keeps a dated releases page for the Gemma family (Gemma 4 on 31 March 2026). The Hugging Face commit history says what each change fixed.",
          "gemma.ai/.well-known/security.txt answers 404. Google's company-wide security.txt at google.com points to its vulnerability reward programme."
        ],
        "score": 68
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it.json"
    },
    "answer": "Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against Muse Glimmer 30B's 70 (BB), and leads in 2 of 5 scored categories. Muse Glimmer 30B leads on risk.",
    "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",
      "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": 1197,
        "categoryRank": 5,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "capability": 50,
          "interface": 75,
          "reach": 90,
          "risk": 80,
          "upkeep": 65
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-10-21"
        },
        "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": "0.6",
            "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": 68,
        "checked": {
          "categories": {
            "capability": "2026-10-21",
            "interface": "2026-10-21",
            "reach": "2026-10-21",
            "risk": "2026-10-21",
            "upkeep": "2026-10-21"
          },
          "last": "2026-10-21",
          "oldest": "2026-10-21",
          "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-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": 68
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/muse-glimmer-30b.json"
    },
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Google DeepMind",
        "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": "2026-07-20",
        "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": "",
        "b": "",
        "name": "Customer content may train models"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict automated access"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict benchmarking"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms or service can change without notice"
      },
      {
        "a": "",
        "b": "",
        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "3.8/5 (4)",
        "b": "3.8/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against Muse Glimmer 30B's 70 (BB), and leads in 2 of 5 scored categories. Muse Glimmer 30B leads on risk.",
        "question": "Which is better for AI agents, Gemma 4 26B A4B IT or Muse Glimmer 30B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Capability, 59 against 50",
          "Agent interface, 85 against 75"
        ],
        "also": null,
        "goodFor": "Agents on a 24 GB or larger GPU or Mac that want a fast, permissively licensed tool-calling model with images and long context",
        "slug": "gemma-4-26b-a4b-it",
        "watchFor": "No safety evaluation figures published"
      },
      {
        "aheadOn": [
          "Risk, 80 against 70"
        ],
        "also": null,
        "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"
      }
    ],
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-26b-a4b-it.json",
        "title": "Agents-A1-4B vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-26b-a4b-it"
      },
      {
        "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-gemma-4-26b-a4b-it.json",
        "title": "Apertus v1.5 8B vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-26b-a4b-it"
      },
      {
        "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-gemma-4-26b-a4b-it.json",
        "title": "Apodex-1.1-mini vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-26b-a4b-it"
      },
      {
        "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/bigbang-v1-vs-gemma-4-26b-a4b-it.json",
        "title": "BigBang-v1 vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-muse-glimmer-30b.json",
        "title": "BigBang-v1 vs Muse Glimmer 30B",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-muse-glimmer-30b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-26b-a4b-it.json",
        "title": "Cosmos-Reason2-2B vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-muse-glimmer-30b.json",
        "title": "Cosmos-Reason2-2B vs Muse Glimmer 30B",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-muse-glimmer-30b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gemma-4-26b-a4b-it.json",
        "title": "Darwin-36B-Opus vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-muse-glimmer-30b.json",
        "title": "Darwin-36B-Opus vs Muse Glimmer 30B",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-muse-glimmer-30b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-26b-a4b-it.json",
        "title": "DeepSeek-V3.2 vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-muse-glimmer-30b.json",
        "title": "DeepSeek-V3.2 vs Muse Glimmer 30B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-muse-glimmer-30b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-26b-a4b-it.json",
        "title": "DeepSeek V4.1 Flash vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-muse-glimmer-30b.json",
        "title": "DeepSeek V4.1 Flash vs Muse Glimmer 30B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-muse-glimmer-30b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-26b-a4b-it.json",
        "title": "DeepSeek-V4-Flash-0731 vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-muse-glimmer-30b.json",
        "title": "DeepSeek-V4-Flash-0731 vs Muse Glimmer 30B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-muse-glimmer-30b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-gemma-4-26b-a4b-it.json",
        "title": "DiffusionGemma 26B A4B IT vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-muse-glimmer-30b.json",
        "title": "DiffusionGemma 26B A4B IT vs Muse Glimmer 30B",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-muse-glimmer-30b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-gemma-4-26b-a4b-it.json",
        "title": "Gemma 4 12B IT vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-muse-glimmer-30b.json",
        "title": "Gemma 4 12B IT vs Muse Glimmer 30B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-muse-glimmer-30b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gemma-4-31b-it.json",
        "title": "Gemma 4 26B A4B IT vs Gemma 4 31B IT",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gemma-4-31b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gemma-4-e2b-it.json",
        "title": "Gemma 4 26B A4B IT vs Gemma 4 E2B IT",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gemma-4-e2b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gemma-4-e4b-it.json",
        "title": "Gemma 4 26B A4B IT vs Gemma 4 E4B IT",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gemma-4-e4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-4-5-air.json",
        "title": "Gemma 4 26B A4B IT vs GLM-4.5-Air",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-4-5-air"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-4-7-flash.json",
        "title": "Gemma 4 26B A4B IT vs GLM-4.7-Flash",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-4-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-5-3.json",
        "title": "Gemma 4 26B A4B IT vs GLM-5.3",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-5-3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-5-3-flash.json",
        "title": "Gemma 4 26B A4B IT vs GLM-5.3-Flash",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-5-3-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gpt-oss-120b.json",
        "title": "Gemma 4 26B A4B IT vs gpt-oss-120b",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gpt-oss-120b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gpt-oss-20b.json",
        "title": "Gemma 4 26B A4B IT vs gpt-oss-20b",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gpt-oss-20b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-granite-4-2-3b.json",
        "title": "Gemma 4 26B A4B IT vs Granite 4.2 3B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-granite-4-2-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-hy3.json",
        "title": "Gemma 4 26B A4B IT vs Hy3",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-hy3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-inkling.json",
        "title": "Gemma 4 26B A4B IT vs Inkling",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-inkling"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-inkling-small.json",
        "title": "Gemma 4 26B A4B IT vs Inkling-Small",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-inkling-small"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-internvl3-5-gpt-oss-20b-a4b-preview.json",
        "title": "Gemma 4 26B A4B IT vs InternVL3.5-GPT-OSS-20B-A4B-Preview",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-internvl3-5-gpt-oss-20b-a4b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-kimi-k3.json",
        "title": "Gemma 4 26B A4B IT vs Kimi K3",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-kimi-k3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-kolibri-1.json",
        "title": "Gemma 4 26B A4B IT vs Kolibri-1",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-kolibri-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-laguna-m-1.json",
        "title": "Gemma 4 26B A4B IT vs Laguna M.1",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-laguna-m-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-laguna-s-2-1.json",
        "title": "Gemma 4 26B A4B IT vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-laguna-s-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-laguna-xs-2-1.json",
        "title": "Gemma 4 26B A4B IT vs Laguna XS 2.1",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-laguna-xs-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lfm2-5-2-6b.json",
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        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-muse-glimmer-30b.json",
        "title": "MiniCPM-o 4.5 vs Muse Glimmer 30B",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-muse-glimmer-30b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-muse-glimmer-30b.json",
        "title": "MiniCPM5-2B vs Muse Glimmer 30B",
        "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-muse-glimmer-30b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-muse-glimmer-30b.json",
        "title": "MiniMax-M2.5 vs Muse Glimmer 30B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-muse-glimmer-30b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-muse-glimmer-30b.json",
        "title": "MiniMax-M2.7 vs Muse Glimmer 30B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-muse-glimmer-30b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-nemotron-3-5-lightning-30b-a3b.json",
        "title": "Muse Glimmer 30B vs Nemotron 3.5 Lightning 30B A3B",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-nemotron-3-5-lightning-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-nemotron-3-nano-30b-a3b.json",
        "title": "Muse Glimmer 30B vs Nemotron 3 Nano 30B A3B",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-nemotron-3-nano-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-nemotron-3-nano-4b.json",
        "title": "Muse Glimmer 30B vs Nemotron 3 Nano 4B",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-nemotron-3-nano-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "Muse Glimmer 30B vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-nemotron-3-super-120b-a12b.json",
        "title": "Muse Glimmer 30B vs Nemotron 3 Super 120B A12B",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-nemotron-3-super-120b-a12b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-nemotron-3-ultra-550b-a55b.json",
        "title": "Muse Glimmer 30B vs Nemotron 3 Ultra 550B A55B",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-nemotron-3-ultra-550b-a55b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-occamy-1-0.json",
        "title": "Muse Glimmer 30B vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-occamy-1-0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-olmo-3-7b-think.json",
        "title": "Muse Glimmer 30B vs Olmo 3 7B Think",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-olmo-3-7b-think"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-ornith-1-5-35b-a3b.json",
        "title": "Muse Glimmer 30B vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-ornith-1-5-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-ornith-1-5-397b.json",
        "title": "Muse Glimmer 30B vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-ornith-1-5-397b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-ornith-1-5-9b.json",
        "title": "Muse Glimmer 30B vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-ornith-1-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-30b-a3b-instruct-2507.json",
        "title": "Muse Glimmer 30B vs Qwen3-30B-A3B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-30b-a3b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-4b-instruct-2507.json",
        "title": "Muse Glimmer 30B vs Qwen3-4B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-4b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-0-8b.json",
        "title": "Muse Glimmer 30B vs Qwen3.5-0.8B",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-0-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-122b-a10b.json",
        "title": "Muse Glimmer 30B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-122b-a10b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-2b.json",
        "title": "Muse Glimmer 30B vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-397b-a17b.json",
        "title": "Muse Glimmer 30B vs Qwen3.5-397B-A17B",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-397b-a17b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-4b.json",
        "title": "Muse Glimmer 30B vs Qwen3.5-4B",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-9b.json",
        "title": "Muse Glimmer 30B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-6-27b.json",
        "title": "Muse Glimmer 30B vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-6-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-6-35b-a3b.json",
        "title": "Muse Glimmer 30B vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-6-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-8-27b.json",
        "title": "Muse Glimmer 30B vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-8-flash-next.json",
        "title": "Muse Glimmer 30B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "Muse Glimmer 30B vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-coder-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-coder-next.json",
        "title": "Muse Glimmer 30B vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Muse Glimmer 30B vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-omni-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-2b-instruct.json",
        "title": "Muse Glimmer 30B vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-2b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Muse Glimmer 30B vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-32b-instruct.json",
        "title": "Muse Glimmer 30B vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-4b-instruct.json",
        "title": "Muse Glimmer 30B vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-8b-instruct.json",
        "title": "Muse Glimmer 30B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Muse Glimmer 30B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-smollm3-3b.json",
        "title": "Muse Glimmer 30B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-spark-x2-6-4b.json",
        "title": "Muse Glimmer 30B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Muse Glimmer 30B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Muse Glimmer 30B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Muse Glimmer 30B vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-thinkingcap-qwen3-8-27b"
      }
    ],
    "scores": [
      {
        "by": 9,
        "edge": "gemma-4-26b-a4b-it",
        "gemma-4-26b-a4b-it": 59,
        "key": "capability",
        "muse-glimmer-30b": 50,
        "name": "Capability",
        "weight": 35
      },
      {
        "by": 0,
        "edge": "",
        "gemma-4-26b-a4b-it": 90,
        "key": "reach",
        "muse-glimmer-30b": 90,
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 10,
        "edge": "muse-glimmer-30b",
        "gemma-4-26b-a4b-it": 70,
        "key": "risk",
        "muse-glimmer-30b": 80,
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 10,
        "edge": "gemma-4-26b-a4b-it",
        "gemma-4-26b-a4b-it": 85,
        "key": "interface",
        "muse-glimmer-30b": 75,
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 0,
        "edge": "",
        "gemma-4-26b-a4b-it": 65,
        "key": "upkeep",
        "muse-glimmer-30b": 65,
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against Muse Glimmer 30B's 70 (BB), and leads in 2 of 5 scored categories. Muse Glimmer 30B leads on risk. Both do open-weight models you run yourself.",
    "tutorials": [],
    "verdicts": {
      "gemma-4-26b-a4b-it": "A fast mixture-of-experts cousin for general agent work with tools, optional thinking and image input, under Apache 2.0. Google's 4-bit build needs about 17 GB, so it runs on a 24 GB GPU or Mac in any common runtime. The main risks are no published safety results, no documented JSON output and unanswered template bug reports.",
      "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."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-muse-glimmer-30b",
    "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-muse-glimmer-30b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-muse-glimmer-30b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-muse-glimmer-30b.min.md"
  },
  "markdown": "Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against Muse Glimmer 30B's 70 (BB), and leads in 2 of 5 scored categories. Muse Glimmer 30B leads on risk. Both do open-weight models you run yourself.\n\n- Gemma 4 26B A4B IT: grade BB, 72.2/100, rank #4 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/gemma-4-26b-a4b-it.json\n- Muse Glimmer 30B: grade BB, 70/100, rank #7 of 100 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 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Gemma 4 26B A4B IT (BB)\n\nGood for: Agents on a 24 GB or larger GPU or Mac that want a fast, permissively licensed tool-calling model with images and long context\n\nAhead on:\n- Capability, 59 against 50\n- Agent interface, 85 against 75\n\nWatch for: No safety evaluation figures published\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 70\n\nWatch for: One tool call per turn and no parallel calls\n\n\n## Score by category\n\n| Category | Weight | Gemma 4 26B A4B IT | Muse Glimmer 30B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 59 | 50 | Gemma 4 26B A4B IT +9 |\n| Reach | 25% (25 this run) | 90 | 90 | even |\n| Risk | 20% (20 this run) | 70 | 80 | Muse Glimmer 30B +10 |\n| Agent interface | 10% (10 this run) | 85 | 75 | Gemma 4 26B A4B IT +10 |\n| Documentation and upkeep | 10% (10 this run) | 65 | 65 | even |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **72.2 · BB** | **70 · BB** | |\n\n## Facts side by side\n\n| Fact | Gemma 4 26B A4B IT | Muse Glimmer 30B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Google DeepMind | 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 | 2026-07-20 | 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| Customer content may train models |  |  |\n| Terms restrict automated access |  |  |\n| Terms restrict benchmarking |  |  |\n| Terms or service can change without notice |  |  |\n| Arbitration or class-action waiver |  |  |\n| Agent reviews | 3.8/5 (4) | 3.8/5 (4) |\n\n## Verdicts\n\n**Gemma 4 26B A4B IT.** A fast mixture-of-experts cousin for general agent work with tools, optional thinking and image input, under Apache 2.0. Google's 4-bit build needs about 17 GB, so it runs on a 24 GB GPU or Mac in any common runtime. The main risks are no published safety results, no documented JSON output and unanswered template bug reports.\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### Gemma 4 26B A4B IT\n\n1. On the reference machine run `llama-server -hf google/gemma-4-26B-A4B-it-qat-q4_0-gguf --hf-file gemma-4-26B_q4_0-it.gguf --jinja -c 65536`, and add the mmproj file for images\n2. With Ollama run `ollama run gemma4:26b` and raise num_ctx or OLLAMA_CONTEXT_LENGTH, since Google's guide says it defaults to 4K below 24 GiB of VRAM and agents want at least 64K\n3. In vLLM or SGLang pass `--tool-call-parser gemma4 --reasoning-parser gemma4` (vLLM also `--enable-auto-tool-choice`), or tool calls come back as text\n4. Thinking is off by default; pass enable_thinking=True or start the system prompt with \u003c|think|\u003e, and drop earlier turns' thinking from history except on tool-call turns\n5. Validate tool names and arguments before running them, and check any JSON yourself\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, Gemma 4 26B A4B IT or Muse Glimmer 30B?\n\nGemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against Muse Glimmer 30B's 70 (BB), and leads in 2 of 5 scored categories. Muse Glimmer 30B leads on risk.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-muse-glimmer-30b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-muse-glimmer-30b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"gemma-4-26b-a4b-it\", \"b\": \"muse-glimmer-30b\"}`. From a terminal: `anchor compare gemma-4-26b-a4b-it muse-glimmer-30b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gemma-4-26b-a4b-it.json and https://www.anchorterminal.com/api/v1/tools/muse-glimmer-30b.json\n\n## Other comparisons with Gemma 4 26B A4B IT or Muse Glimmer 30B\n\n- [Agents-A1-4B vs Gemma 4 26B A4B IT](https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-26b-a4b-it.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 Gemma 4 26B A4B IT](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-26b-a4b-it.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 Gemma 4 26B A4B IT](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-26b-a4b-it.md)\n- [Apodex-1.1-mini vs Muse Glimmer 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    "title": "Gemma 4 26B A4B IT vs Muse Glimmer 30B for AI agents (2026)",
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