{
  "data": {
    "a": {
      "slug": "gemma-4-31b-it",
      "name": "Gemma 4 31B IT",
      "vendor": "Google DeepMind",
      "vendorUrl": "https://gemma.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Gemma 4 31B IT is Google DeepMind's open-weight dense multimodal model of 30.7B parameters, taking text and images with a 256K-token context, published on Hugging Face in BF16 with 4-bit QAT builds under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/gemma-4-31b-it",
      "markdownUrl": "https://www.anchorterminal.com/tools/gemma-4-31b-it.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/gemma-4-31b-it.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/gemma-4-31b-it.json",
      "repo": "https://huggingface.co/google/gemma-4-31B-it",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "google/gemma-4-31B-it"
        },
        {
          "registry": "huggingface",
          "name": "google/gemma-4-31B-it-qat-q4_0-gguf"
        },
        {
          "registry": "huggingface",
          "name": "google/gemma-4-31B-it-qat-w4a16-ct"
        },
        {
          "registry": "ollama",
          "name": "gemma4:31b"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository and Google's QAT repositories are not gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it yourself costs the hardware, about 69.9 GB at BF16 or 17.5 GB at Q4_0 per Google's memory table. Google also hosts it on the Gemini API's free tier, a separate listing (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/",
      "llmsTxt": "https://gemma.ai/llms.txt",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "multimodal",
        "reasoning",
        "tool-calling",
        "apache-2.0",
        "safetensors",
        "gguf",
        "qat",
        "self-hosted"
      ],
      "lastRelease": "2026-07-20",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 74.1,
        "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": 1,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "capability": 61,
          "interface": 85,
          "reach": 95,
          "risk": 70,
          "upkeep": 65
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A dense multimodal cousin with documented tool calling, optional thinking and a 256K context, for general agent work and image reading on one 24 GB GPU through Google's 4-bit build. It runs in every major runtime under plain Apache 2.0. The main risk is that Google publishes no safety results and has gone quiet on the repository since July.",
        "bestFor": "An agent with a 24 GB GPU or more that wants a dense multimodal cousin with tool calling under a plain licence",
        "strengths": [
          "Google's own 17.7 GB QAT Q4_0 GGUF fits a 24 GB GPU, and Ollama's gemma4:31b is 20 GB",
          "Released support in llama.cpp, Ollama, vLLM, SGLang and Transformers",
          "Apache 2.0 with nothing added, no gate, and hosted on Google's Gemini API too",
          "Tool calling documented by Google with a response schema Transformers parses, several calls per turn",
          "Thinking that can be switched on or off per request and 262,144 tokens of native context"
        ],
        "weaknesses": [
          "No safety evaluation results published",
          "No prompt-injection guidance and no security route tied to Gemma",
          "Structured output isn't documented by Google",
          "Training data described only in general terms",
          "Files unchanged since 20 July and no Google reply in the discussions since 30 July"
        ],
        "agentNotes": [
          "On the reference machine run `llama-server -hf google/gemma-4-31B-it-qat-q4_0-gguf --hf-file gemma-4-31B_q4_0-it.gguf --jinja -c 32768`, adding gemma-4-31B-it-mmproj.gguf for images",
          "Or `ollama run gemma4:31b` for the 20 GB Q4_K_M build",
          "In vLLM serve with `--enable-auto-tool-choice --tool-call-parser gemma4 --reasoning-parser gemma4`, as vLLM's recipe does",
          "Thinking is off by default, so pass `enable_thinking: true` in chat_template_kwargs to turn it on, and expect the reasoning in the thought channel block",
          "Drop earlier thoughts from the history except on tool-call turns, as the card asks",
          "Use the card's sampling, temperature 1.0, top_p 0.95, top_k 64",
          "Validate function names and arguments before running a call, and check JSON output yourself or use the runtime's guided decoding",
          "The w4a16 build relies on Marlin INT4 kernels that vLLM marks unsupported on AMD, so use the GGUF on the reference machine"
        ],
        "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": 74.1
          }
        ],
        "editorialScores": {
          "capability": 61,
          "interface": 85,
          "reach": 95,
          "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 gemma4:31b\n# or, with vLLM's recipe:\nvllm serve google/gemma-4-31B-it --max-model-len 32768 --enable-auto-tool-choice \\\n  --reasoning-parser gemma4 --tool-call-parser gemma4",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"gemma4:31b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/gemma-4-31b-it"
      },
      "sameCompany": [
        "embeddinggemma-300m",
        "diffusiongemma-26b-a4b-it",
        "gemma-4-12b-it",
        "gemma-4-26b-a4b-it",
        "gemma-4-e2b-it",
        "gemma-4-e4b-it"
      ],
      "sameModel": [
        "gemini-api"
      ],
      "weights": {
        "trainedBy": "Google DeepMind",
        "builtOn": "google/gemma-4-31B",
        "params": "30.7B",
        "context": 262144,
        "goodFor": "Dense general assistant and agent model with tool calling and optional thinking, taking text, images and video frames.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "gemma-4-31B_q4_0-it",
            "url": "https://huggingface.co/google/gemma-4-31B-it-qat-q4_0-gguf/blob/main/gemma-4-31B_q4_0-it.gguf",
            "who": "publisher",
            "precision": "q4_0",
            "sizeGB": 17.7,
            "memoryGB": 24,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)",
              "LM Studio"
            ],
            "note": "Quantisation-aware trained by Google. Add gemma-4-31B-it-mmproj.gguf (1.2 GB) for image input"
          },
          {
            "name": "ollama gemma4:31b",
            "url": "https://ollama.com/library/gemma4:31b",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 20,
            "memoryGB": 24,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Q4_K_M weights with a BF16 vision projector and a Q8_0 draft model (digest 17ba34c06c80). 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-4-31B-it-qat-w4a16-ct",
            "url": "https://huggingface.co/google/gemma-4-31B-it-qat-w4a16-ct",
            "who": "publisher",
            "precision": "w4a16",
            "sizeGB": 23.3,
            "memoryGB": 32,
            "format": "safetensors",
            "note": "Quantisation-aware trained, compressed-tensors format for vLLM. vLLM's quantisation table marks its Marlin INT4 kernels unsupported on AMD"
          },
          {
            "name": "gemma-4-31B-it",
            "url": "https://huggingface.co/google/gemma-4-31B-it",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 62.5,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          }
        ],
        "run": "llama-server -hf google/gemma-4-31B-it-qat-q4_0-gguf --hf-file gemma-4-31B_q4_0-it.gguf --jinja -c 32768",
        "runBuild": "gemma-4-31B_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-4-26B-A4B-it",
            "url": "https://huggingface.co/google/gemma-4-26B-A4B-it",
            "params": "25.2B total, 3.8B active",
            "listing": "https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it"
          },
          {
            "name": "gemma-4-31B",
            "url": "https://huggingface.co/google/gemma-4-31B",
            "params": "30.7B"
          },
          {
            "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 (the registrant of gemma.ai; the card names Google DeepMind as author)",
        "domain": "gemma.ai",
        "domainRegistered": "2017-12-16",
        "domainNote": "RDAP shows gemma.ai registered in 2017 and transferred on 15 April 2025, so the registration date may predate Google's use of the domain.",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://gemma.ai/models/releases/",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "RDAP names Google LLC as registrant of gemma.ai, with MarkMonitor as registrar. Google's ai.google.dev pages point to gemma.ai as the current documentation.",
          "Nothing is hosted under this listing, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is Google's Gemma releases page, which dates each release (Gemma 4 on 31 March 2026, its MTP drafters on 16 April). The Hugging Face commit history's messages also say what changed.",
          "gemma.ai/.well-known/security.txt answered 404 on 21 October 2026. www.google.com has a security.txt pointing to Google's vulnerability reward programme, which isn't linked from the card or gemma.ai."
        ],
        "score": 68
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/gemma-4-31b-it.json"
    },
    "answer": "Gemma 4 31B IT scores 74.1 (BB) on agent readiness against Muse Glimmer 30B's 70 (BB), and leads in 3 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": "yes",
        "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 31B IT scores 74.1 (BB) on agent readiness against Muse Glimmer 30B's 70 (BB), and leads in 3 of 5 scored categories. Muse Glimmer 30B leads on risk.",
        "question": "Which is better for AI agents, Gemma 4 31B IT or Muse Glimmer 30B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Capability, 61 against 50",
          "Reach, 95 against 90",
          "Agent interface, 85 against 75"
        ],
        "also": null,
        "goodFor": "An agent with a 24 GB GPU or more that wants a dense multimodal cousin with tool calling under a plain licence",
        "slug": "gemma-4-31b-it",
        "watchFor": "No safety evaluation results 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-4-31b-it.json",
        "title": "Agents-A1-4B vs Gemma 4 31B IT",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-31b-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-4-31b-it.json",
        "title": "Apertus v1.5 8B vs Gemma 4 31B IT",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-31b-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-4-31b-it.json",
        "title": "Apodex-1.1-mini vs Gemma 4 31B IT",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-31b-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-4-31b-it.json",
        "title": "BigBang-v1 vs Gemma 4 31B IT",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-31b-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-4-31b-it.json",
        "title": "Cosmos-Reason2-2B vs Gemma 4 31B IT",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-31b-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-4-31b-it.json",
        "title": "Darwin-36B-Opus vs Gemma 4 31B IT",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gemma-4-31b-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-4-31b-it.json",
        "title": "DeepSeek-V3.2 vs Gemma 4 31B IT",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-31b-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-4-31b-it.json",
        "title": "DeepSeek V4.1 Flash vs Gemma 4 31B IT",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-31b-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-4-31b-it.json",
        "title": "DeepSeek-V4-Flash-0731 vs Gemma 4 31B IT",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-31b-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-4-31b-it.json",
        "title": "DiffusionGemma 26B A4B IT vs Gemma 4 31B IT",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-gemma-4-31b-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-4-31b-it.json",
        "title": "Gemma 4 12B IT vs Gemma 4 31B IT",
        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-gemma-4-31b-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-muse-glimmer-30b.json",
        "title": "Gemma 4 26B A4B IT vs Muse Glimmer 30B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-muse-glimmer-30b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gemma-4-e2b-it.json",
        "title": "Gemma 4 31B IT vs Gemma 4 E2B IT",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gemma-4-e2b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gemma-4-e4b-it.json",
        "title": "Gemma 4 31B IT vs Gemma 4 E4B IT",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gemma-4-e4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-4-5-air.json",
        "title": "Gemma 4 31B IT vs GLM-4.5-Air",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-4-5-air"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-4-7-flash.json",
        "title": "Gemma 4 31B IT vs GLM-4.7-Flash",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-4-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-5-3.json",
        "title": "Gemma 4 31B IT vs GLM-5.3",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-5-3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-5-3-flash.json",
        "title": "Gemma 4 31B IT vs GLM-5.3-Flash",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-5-3-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gpt-oss-120b.json",
        "title": "Gemma 4 31B IT vs gpt-oss-120b",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gpt-oss-120b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gpt-oss-20b.json",
        "title": "Gemma 4 31B IT vs gpt-oss-20b",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gpt-oss-20b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-granite-4-2-3b.json",
        "title": "Gemma 4 31B IT vs Granite 4.2 3B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-granite-4-2-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-hy3.json",
        "title": "Gemma 4 31B IT vs Hy3",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-hy3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-inkling.json",
        "title": "Gemma 4 31B IT vs Inkling",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-inkling"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-inkling-small.json",
        "title": "Gemma 4 31B IT vs Inkling-Small",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-inkling-small"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-internvl3-5-gpt-oss-20b-a4b-preview.json",
        "title": "Gemma 4 31B IT vs InternVL3.5-GPT-OSS-20B-A4B-Preview",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-internvl3-5-gpt-oss-20b-a4b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-kimi-k3.json",
        "title": "Gemma 4 31B IT vs Kimi K3",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-kimi-k3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-kolibri-1.json",
        "title": "Gemma 4 31B IT vs Kolibri-1",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-kolibri-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-laguna-m-1.json",
        "title": "Gemma 4 31B IT vs Laguna M.1",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-laguna-m-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-laguna-s-2-1.json",
        "title": "Gemma 4 31B IT vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-laguna-s-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-laguna-xs-2-1.json",
        "title": "Gemma 4 31B IT vs Laguna XS 2.1",
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      },
      {
        "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": 11,
        "edge": "gemma-4-31b-it",
        "gemma-4-31b-it": 61,
        "key": "capability",
        "muse-glimmer-30b": 50,
        "name": "Capability",
        "weight": 35
      },
      {
        "by": 5,
        "edge": "gemma-4-31b-it",
        "gemma-4-31b-it": 95,
        "key": "reach",
        "muse-glimmer-30b": 90,
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 10,
        "edge": "muse-glimmer-30b",
        "gemma-4-31b-it": 70,
        "key": "risk",
        "muse-glimmer-30b": 80,
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 10,
        "edge": "gemma-4-31b-it",
        "gemma-4-31b-it": 85,
        "key": "interface",
        "muse-glimmer-30b": 75,
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 0,
        "edge": "",
        "gemma-4-31b-it": 65,
        "key": "upkeep",
        "muse-glimmer-30b": 65,
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "Gemma 4 31B IT scores 74.1 (BB) on agent readiness against Muse Glimmer 30B's 70 (BB), and leads in 3 of 5 scored categories. Muse Glimmer 30B leads on risk. Both do open-weight models you run yourself.",
    "tutorials": [],
    "verdicts": {
      "gemma-4-31b-it": "A dense multimodal cousin with documented tool calling, optional thinking and a 256K context, for general agent work and image reading on one 24 GB GPU through Google's 4-bit build. It runs in every major runtime under plain Apache 2.0. The main risk is that Google publishes no safety results and has gone quiet on the repository since July.",
      "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-4-31b-it-vs-muse-glimmer-30b",
    "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-muse-glimmer-30b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-muse-glimmer-30b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-muse-glimmer-30b.min.md"
  },
  "markdown": "Gemma 4 31B IT scores 74.1 (BB) on agent readiness against Muse Glimmer 30B's 70 (BB), and leads in 3 of 5 scored categories. Muse Glimmer 30B leads on risk. Both do open-weight models you run yourself.\n\n- Gemma 4 31B IT: grade BB, 74.1/100, rank #2 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/gemma-4-31b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/gemma-4-31b-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 31B IT (BB)\n\nGood for: An agent with a 24 GB GPU or more that wants a dense multimodal cousin with tool calling under a plain licence\n\nAhead on:\n- Capability, 61 against 50\n- Reach, 95 against 90\n- Agent interface, 85 against 75\n\nWatch for: No safety evaluation results 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 31B IT | Muse Glimmer 30B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 61 | 50 | Gemma 4 31B IT +11 |\n| Reach | 25% (25 this run) | 95 | 90 | Gemma 4 31B IT +5 |\n| Risk | 20% (20 this run) | 70 | 80 | Muse Glimmer 30B +10 |\n| Agent interface | 10% (10 this run) | 85 | 75 | Gemma 4 31B IT +10 |\n| Documentation and upkeep | 10% (10 this run) | 65 | 65 | even |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **74.1 · BB** | **70 · BB** | |\n\n## Facts side by side\n\n| Fact | Gemma 4 31B 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 | yes | 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 31B IT.** A dense multimodal cousin with documented tool calling, optional thinking and a 256K context, for general agent work and image reading on one 24 GB GPU through Google's 4-bit build. It runs in every major runtime under plain Apache 2.0. The main risk is that Google publishes no safety results and has gone quiet on the repository since July.\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 31B IT\n\n1. On the reference machine run `llama-server -hf google/gemma-4-31B-it-qat-q4_0-gguf --hf-file gemma-4-31B_q4_0-it.gguf --jinja -c 32768`, adding gemma-4-31B-it-mmproj.gguf for images\n2. Or `ollama run gemma4:31b` for the 20 GB Q4_K_M build\n3. In vLLM serve with `--enable-auto-tool-choice --tool-call-parser gemma4 --reasoning-parser gemma4`, as vLLM's recipe does\n4. Thinking is off by default, so pass `enable_thinking: true` in chat_template_kwargs to turn it on, and expect the reasoning in the thought channel block\n5. Drop earlier thoughts from the history except on tool-call turns, as the card asks\n6. Use the card's sampling, temperature 1.0, top_p 0.95, top_k 64\n7. Validate function names and arguments before running a call, and check JSON output yourself or use the runtime's guided decoding\n8. The w4a16 build relies on Marlin INT4 kernels that vLLM marks unsupported on AMD, so use the GGUF on the reference machine\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 31B IT or Muse Glimmer 30B?\n\nGemma 4 31B IT scores 74.1 (BB) on agent readiness against Muse Glimmer 30B's 70 (BB), and leads in 3 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-4-31b-it-vs-muse-glimmer-30b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-muse-glimmer-30b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"gemma-4-31b-it\", \"b\": \"muse-glimmer-30b\"}`. From a terminal: `anchor compare gemma-4-31b-it muse-glimmer-30b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gemma-4-31b-it.json and https://www.anchorterminal.com/api/v1/tools/muse-glimmer-30b.json\n\n## Other comparisons with Gemma 4 31B IT or Muse Glimmer 30B\n\n- [Agents-A1-4B vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-31b-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 31B IT](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-31b-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 31B IT](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-31b-it.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- [BigBang-v1 vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-31b-it.md)\n- [BigBang-v1 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/bigbang-v1-vs-muse-glimmer-30b.md)\n- [Cosmos-Reason2-2B vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-31b-it.md)\n- [Cosmos-Reason2-2B vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-muse-glimmer-30b.md)\n- [Darwin-36B-Opus vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gemma-4-31b-it.md)\n- [Darwin-36B-Opus vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-muse-glimmer-30b.md)\n- [DeepSeek-V3.2 vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-31b-it.md)\n- [DeepSeek-V3.2 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-muse-glimmer-30b.md)\n- [DeepSeek V4.1 Flash vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-31b-it.md)\n- [DeepSeek V4.1 Flash vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-muse-glimmer-30b.md)\n- [DeepSeek-V4-Flash-0731 vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-31b-it.md)\n- [DeepSeek-V4-Flash-0731 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-muse-glimmer-30b.md)\n- [DiffusionGemma 26B A4B IT vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-gemma-4-31b-it.md)\n- [DiffusionGemma 26B A4B IT vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-muse-glimmer-30b.md)\n- [Gemma 4 12B IT vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-gemma-4-31b-it.md)\n- [Gemma 4 12B IT vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-muse-glimmer-30b.md)\n- [Gemma 4 26B A4B IT vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gemma-4-31b-it.md)\n- [Gemma 4 26B A4B IT vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-muse-glimmer-30b.md)\n- [Gemma 4 31B IT vs Gemma 4 E2B IT](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gemma-4-e2b-it.md)\n- [Gemma 4 31B IT vs Gemma 4 E4B IT](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gemma-4-e4b-it.md)\n- [Gemma 4 31B IT vs GLM-4.5-Air](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-4-5-air.md)\n- [Gemma 4 31B IT vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-4-7-flash.md)\n- [Gemma 4 31B IT vs GLM-5.3](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-5-3.md)\n- [Gemma 4 31B IT vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-5-3-flash.md)\n- [Gemma 4 31B IT vs gpt-oss-120b](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gpt-oss-120b.md)\n- [Gemma 4 31B IT vs gpt-oss-20b](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gpt-oss-20b.md)\n- [Gemma 4 31B IT vs Granite 4.2 3B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-granite-4-2-3b.md)\n- [Gemma 4 31B IT vs Hy3](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-hy3.md)\n- [Gemma 4 31B IT vs Inkling](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-inkling.md)\n- [Gemma 4 31B IT vs Inkling-Small](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-inkling-small.md)\n- [Gemma 4 31B IT vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Gemma 4 31B IT vs Kimi K3](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-kimi-k3.md)\n- [Gemma 4 31B IT vs Kolibri-1](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-kolibri-1.md)\n- [Gemma 4 31B IT vs Laguna M.1](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-laguna-m-1.md)\n- [Gemma 4 31B IT vs Laguna S 2.1](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-laguna-s-2-1.md)\n- [Gemma 4 31B IT vs Laguna XS 2.1](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-laguna-xs-2-1.md)\n- [Gemma 4 31B IT vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-lfm2-5-2-6b.md)\n- [Gemma 4 31B IT vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ling-3-0-tiny.md)\n- [Gemma 4 31B IT vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-mimo-v2-6-distill-qwen-9b.md)\n- [Gemma 4 31B IT vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minicpm-o-4-5.md)\n- [Gemma 4 31B IT vs MiniCPM5-2B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minicpm5-2b.md)\n- [Gemma 4 31B IT vs MiniMax-M2.5](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minimax-m2-5.md)\n- 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occamy-1.0](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-occamy-1-0.md)\n- [Gemma 4 31B IT vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-olmo-3-7b-think.md)\n- [Gemma 4 31B IT vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-35b-a3b.md)\n- [Gemma 4 31B IT vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-397b.md)\n- [Gemma 4 31B IT vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-9b.md)\n- [Gemma 4 31B IT vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-30b-a3b-instruct-2507.md)\n- [Gemma 4 31B IT vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-4b-instruct-2507.md)\n- [Gemma 4 31B IT vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-0-8b.md)\n- [Gemma 4 31B IT vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-122b-a10b.md)\n- [Gemma 4 31B IT vs Qwen3.5-2B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-2b.md)\n- [Gemma 4 31B IT vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-397b-a17b.md)\n- [Gemma 4 31B IT vs Qwen3.5-4B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-4b.md)\n- [Gemma 4 31B IT vs Qwen3.5-9B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-9b.md)\n- [Gemma 4 31B IT vs Qwen3.6-27B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-6-27b.md)\n- [Gemma 4 31B IT vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-6-35b-a3b.md)\n- [Gemma 4 31B IT vs Qwen3.8-27B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-8-27b.md)\n- [Gemma 4 31B IT vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-8-flash-next.md)\n- [Gemma 4 31B IT vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-coder-30b-a3b-instruct.md)\n- [Gemma 4 31B IT vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-coder-next.md)\n- [Gemma 4 31B IT vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-omni-30b-a3b-instruct.md)\n- [Gemma 4 31B IT vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-2b-instruct.md)\n- [Gemma 4 31B IT vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-30b-a3b-instruct.md)\n- [Gemma 4 31B IT vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-32b-instruct.md)\n- [Gemma 4 31B IT vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-4b-instruct.md)\n- [Gemma 4 31B IT vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-8b-instruct.md)\n- [Gemma 4 31B IT vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwopus3-8-27b-flash-v2.md)\n- [Gemma 4 31B IT vs SmolLM3-3B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-smollm3-3b.md)\n- [Gemma 4 31B IT vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-spark-x2-6-4b.md)\n- [Gemma 4 31B IT vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-swift-1-5-qwen3-8-27b.md)\n- [Gemma 4 31B IT vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Gemma 4 31B IT vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-thinkingcap-qwen3-8-27b.md)\n- [Gemma 4 E2B IT vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-muse-glimmer-30b.md)\n- [Gemma 4 E4B IT vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-muse-glimmer-30b.md)\n- [GLM-4.5-Air vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-muse-glimmer-30b.md)\n- [GLM-4.7-Flash vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-muse-glimmer-30b.md)\n- [GLM-5.3-Flash vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-muse-glimmer-30b.md)\n- [GLM-5.3 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/glm-5-3-vs-muse-glimmer-30b.md)\n- [gpt-oss-120b vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-muse-glimmer-30b.md)\n- [gpt-oss-20b vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-muse-glimmer-30b.md)\n- [Granite 4.2 3B vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-muse-glimmer-30b.md)\n- [Hy3 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/hy3-vs-muse-glimmer-30b.md)\n- [Inkling-Small vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/inkling-small-vs-muse-glimmer-30b.md)\n- [Inkling vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/inkling-vs-muse-glimmer-30b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-muse-glimmer-30b.md)\n- [Kimi K3 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/kimi-k3-vs-muse-glimmer-30b.md)\n- [Kolibri-1 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/kolibri-1-vs-muse-glimmer-30b.md)\n- [Laguna M.1 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/laguna-m-1-vs-muse-glimmer-30b.md)\n- [Laguna S 2.1 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-muse-glimmer-30b.md)\n- [Laguna XS 2.1 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-muse-glimmer-30b.md)\n- [LFM2.5-2.6B vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-muse-glimmer-30b.md)\n- [Ling-3.0-tiny vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-muse-glimmer-30b.md)\n- [MiMo-V2.6 Distill Qwen 9B vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-muse-glimmer-30b.md)\n- [MiniCPM-o 4.5 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-muse-glimmer-30b.md)\n- [MiniCPM5-2B vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/minicpm5-2b-vs-muse-glimmer-30b.md)\n- [MiniMax-M2.5 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-muse-glimmer-30b.md)\n- [MiniMax-M2.7 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-muse-glimmer-30b.md)\n- [Muse Glimmer 30B vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Muse Glimmer 30B vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-nemotron-3-nano-30b-a3b.md)\n- [Muse Glimmer 30B vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-nemotron-3-nano-4b.md)\n- [Muse Glimmer 30B vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Muse Glimmer 30B vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-nemotron-3-super-120b-a12b.md)\n- [Muse Glimmer 30B vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-nemotron-3-ultra-550b-a55b.md)\n- [Muse Glimmer 30B vs occamy-1.0](https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-occamy-1-0.md)\n- [Muse Glimmer 30B vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-olmo-3-7b-think.md)\n- [Muse Glimmer 30B vs 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    "title": "Gemma 4 31B IT vs Muse Glimmer 30B for AI agents (2026)",
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