Muse Glimmer 30B

Facts

Released

2026-08-09 the day the repository was first published on Hugging Face Hugging Face, read 2026-09-24

Licence

apache-2.0 the licence the model card declares Hugging Face, read 2026-09-24

Open weights

Yes the weights are published in this Hugging Face repository Hugging Face, read 2026-09-24

Parameters

29.8 billion counted from the safetensors weight files Hugging Face, read 2026-09-24

Active parameters

Unknown

Knowledge cutoff

Unknown

Context window

• 131,072 tokens the maximum position embeddings in the model configuration model configuration, read 2026-09-24
• 131,072 tokens the context length OpenRouter lists OpenRouter, read 2026-09-24

Longest output

• 16,384 tokens the largest output OpenRouter's first provider allows OpenRouter, read 2026-09-24

Tool calling

• Yes OpenRouter lists tools among the supported parameters OpenRouter, read 2026-09-24

Structured output

• Yes OpenRouter lists structured outputs among the supported parameters OpenRouter, read 2026-09-24

Reasoning controls

• Yes OpenRouter lists reasoning controls, with the efforts xhigh, high, medium, low OpenRouter, read 2026-09-24

Inputs and outputs

text, image in, text out Hugging Face, read 2026-09-24

Good for

• Vision: Hugging Face files it under image-text-to-text Hugging Face, read 2026-09-24
• Reasoning: OpenRouter lists reasoning controls for it OpenRouter, read 2026-09-24

Prices

Run it on your own hardware

Weights are the sizes of the files a source lists, or an estimate from the parameter count where none does. Memory is an estimate: weights plus KV cache plus 512 MiB and 5 percent of the weights for runtime buffers. Check it against your hardware.

Start it with a local runtime

• Ollama

  ollama run hf.co/meta-models/Muse-Glimmer-30B-GGUF:Q4_K_M

• LM Studio

  lms get meta-models/Muse-Glimmer-30B-GGUF
  lms server start

• llama.cpp server

  llama-server -hf meta-models/Muse-Glimmer-30B-GGUF:Q4_K_M --jinja

• vLLM

  vllm serve meta-models/Muse-Glimmer-30B

• SGLang

  sglang serve --model-path meta-models/Muse-Glimmer-30B --port 30000

Use it from your harness

Set up for OpenRouter with the model meta/muse-glimmer-30b. Each endpoint page has the same setup for its own address.

{
  "$schema": "https://opencode.ai/config.json",
  "provider": {
    "openrouter": {
      "npm": "@ai-sdk/openai-compatible",
      "name": "OpenRouter",
      "options": {
        "baseURL": "https://openrouter.ai/api/v1",
        "apiKey": "{env:OPENROUTER_API_KEY}"
      },
      "models": {
        "meta/muse-glimmer-30b": {
          "name": "Muse Glimmer 30B",
          "limit": {
            "context": 131072,
            "output": 16384
          }
        }
      }
    }
  }
}

{
  "providers": {
    "openrouter": {
      "baseUrl": "https://openrouter.ai/api/v1",
      "api": "openai-completions",
      "apiKey": "$OPENROUTER_API_KEY",
      "models": [
        {
          "id": "meta/muse-glimmer-30b"
        }
      ]
    }
  }
}

model = "meta/muse-glimmer-30b"
model_provider = "openrouter"

[model_providers.openrouter]
name = "OpenRouter"
base_url = "https://openrouter.ai/api/v1"
env_key = "OPENROUTER_API_KEY"

export ANTHROPIC_BASE_URL="https://openrouter.ai/api"
export ANTHROPIC_AUTH_TOKEN="$OPENROUTER_API_KEY"
export ANTHROPIC_MODEL="meta/muse-glimmer-30b"
claude

Other ways to reach it

• Ollama, model hf.co/meta-models/Muse-Glimmer-30B-GGUF:Q4_K_M

Published results

These are results other people published. Baltor did not run them and does not rank models by them.

• Results the maker published on the model card published by meta-models Hugging Face, read 2026-09-24