Muse Glimmer
Open-weight agentic AI that runs on consumer GPUs and laptops
Context window
128K
Input / 1M tokens
Free
Output / 1M tokens
Free
Provider
Meta
Data verified 2026-08-13
Muse Glimmer is a 30-billion-parameter open-source multimodal model distilled from Meta's proprietary Muse Spark. Optimized for local agentic workflows, it runs on consumer hardware with a single GPU, handles tool use and multi-step reasoning, and supports text and image input. Released under Apache 2.0, the most permissive license Meta has used.
Capability index
Relative estimates (0-100) to place this model against its peers, grounded in published benchmarks.
How to access it
Download weights from Hugging Face under Apache 2.0 license. Self-host via supported runtimes (llama.cpp, MLX, ExecuTorch, Ollama, LM Studio, Together AI, Fireworks AI) or use third-party API providers.
Strengths
- ✓Runs locally on single consumer GPU or laptop
- ✓Multimodal (text and image input)
- ✓Strong agentic capabilities with tool use and failure recovery
- ✓128K+ extendable context window
- ✓Most permissive open-source license (Apache 2.0)
- ✓Efficient 4-bit quantization (17GB)
Best for developers who...
When to choose it (and when not to)
Reach for Muse Glimmer when...
- →When you need local inference with full control
- →For agentic tasks requiring multi-step reasoning
- →When working with documents, screenshots, or images alongside text
- →To avoid per-token API costs
Look elsewhere if...
- ✕If you need the largest frontier model capability
- ✕For applications requiring extremely fast inference at scale
- ✕If you cannot self-host or manage infrastructure
- ✕For real-time applications requiring minimal latency on limited hardware
How to use it
- ›Leverage multi-step reasoning by structuring prompts for planning before action
- ›Provide clear tool/function definitions for reliable tool use
- ›Include context windows effectively (supports up to 128K+ tokens)
- ›For images, specify exactly what visual elements matter for the task
Quickstart
Pythonfrom together import Together
client = Together()
response = client.chat.completions.create(
model='meta-models/Muse-Glimmer-30B',
messages=[{'role': 'user', 'content': 'What are some fun things to do in New York?'}]
)
print(response.choices[0].message.content)Example uses Together AI hosted endpoint. For local deployment, use llama.cpp, MLX, or Ollama with Hugging Face weights.
API model id: meta-models/Muse-Glimmer-30B
Benchmarks
| Benchmark | Score | Notes |
|---|---|---|
| MCP-Atlas | 75.5 | Tool-use and multi-turn task completion |
| SWE-Bench Verified | 76.0 | Software engineering task completion |
| DeepSearch QA | 74.6 | Question answering and reasoning |
| AIME 2026 | 94.7 | Mathematical reasoning |
| MMMU-Pro | 74% | Visual reasoning (vision encoder) |
Source: MarkTechPost
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