Muse Glimmer is a 30-billion-parameter causal language model with a dedicated perception encoder, distilled from Muse Spark and purpose-built for autonomous agentic tasks on consumer hardware. The model integrates multi-step reasoning, reliable tool use, multimodal understanding, and failure recovery into a single model that runs locally without requiring cloud infrastructure or network access.
Get started
To download and run Muse Glimmer with Ollama, use:
ollama run muse-glimmer
Ollama’s MLX engine provides state-of-the-art performance on Apple Silicon, with support for DFlash and image input:
ollama run muse-glimmer:30b-mlx
Model information
- End-to-end Agentic Task Completion. Muse Glimmer achieves strong success rates on full-task benchmarks including DeepSearch QA, MCP-Atlas, 𝛕3-Bench and SWE-Bench, which measure its ability to work within scaffolds, write and debug code, and resolve multi-turn requests from start to finish.
- Reliable Tool Use. The model handles a wide range of function calls, invoking tools with precise schemas throughout extended workflows.
- Multi-Step Reasoning. Muse Glimmer chains reasoning over long horizons, sustaining coherent plans across complex, extended workflows.
- Failure Recovery. When a tool call fails or returns an unexpected result, the model diagnoses the error and retries rather than halt.
- Multimodal Input and Reasoning. Through a dedicated perception encoder, the model accepts interleaved text and images. This enables agents to interpret screenshots, charts, and documents alongside conversation.
- Scaffold Compatibility. Muse Glimmer works across OpenClaw, Hermes Agent, and other agentic orchestration patterns.
- Controllable Effort. The model supports different reasoning strengths to select the right balance between quality and speed.
- Multilingual. Muse Glimmer is trained on data from more than 100 languages.
Benchmarks
Meta evaluated Muse Glimmer across a broad range of benchmarks to assess the diverse capabilities required for effective autonomous agent behavior. Compared with Gemma4-31B and Qwen3.6-27B, Muse Glimmer performs strongly for its size class on several widely used LLM benchmarks.
| Category | Benchmark | Muse Glimmer-30BHigh Reasoning | Gemma4-31B Thinking Mode | Qwen3.6-27B Thinking Mode |
|---|---|---|---|---|
| General Agentic | MCP Atlas (Public) | 75.5 | 54.2 | 62.5 |
| DeepSearch QA | 74.6 | 61.7 | 71.1 | |
| 𝛕3-Banking | 23.5 | 15.1 | 16.7 | |
| WildClawBench | 47.6 | 37.6 | 43.2 | |
| GDPVal-AA v2 | 953 | 811 | 1141 | |
| Gaia2 | 43.3 | 36.4 | 40.0 | |
| SkillsBench (with skills) | 44.3 | 32.4 | 46.6 | |
| OSWorld-Verified | 65.9 | 58.5 | 75.6 | |
| Agentic Coding | SWE-Bench Pro | 51.2 | 36.9 | 50.2 |
| SWE-Bench Verified | 76.0 | 66.6 | 77.2 | |
| TerminalBench 2.1 (with terminus2) | 51.7 | 43.4 | 60.7 | |
| SciCode | 43.6 | 43.4 | 39.8 | |
| Multimodal | Charxiv Reasoning | 78.8 | 77.7 | 78.4 |
| ScreenSpot Pro | 75.4 | 75.9 | 76.1 | |
| OmniDocBench v1.5 | 75.8 | 72.5 | 77.8 | |
| MMMU Pro | 74 | 73 | 75 | |
| Safety | CI Memories | Violation (↓): 26.4 Coverage: 64.8 |
Violation (↓): 12.1 Coverage: 53.0 |
Violation (↓): 53.4 Coverage: 66.9 |
| Siren AgentDojo | Attack Success Rate (↓): 28.4 Utility: 94.2 |
Attack Success Rate (↓): 25.6 Utility: 90.8 |
Attack Success Rate (↓): 40.3 Utility: 92.7 |
|
| General Capabilities and Reasoning | IFBench | 77.0 | 76.0 | 70.8 |
| AIME 2026 | 94.7 | 89.2 | 94.1 | |
| GPQA Diamond (AA) | 83.5 | 85.7 | 84.2 | |
| HLE Text (AA) | 22.0 | 23.6 | 23.1 | |
| AA-LCR | 80.0 | 68.3 | 73.3 | |
| Beam128K | 65.1 | 58.2 | 63.0 |
For more detail about Meta’s evaluations, see Meta’s report.