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Meta's latest open model built for always-on local agents. 30B parameters, licensed under Apache 2.0 and runs on a single GPU — tuned for tool use, long tasks, and failure recovery.
ollama run muse-glimmer:30b-q8_0
Details
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Note: Muse Glimmer is currently available via initial support via Ollama’s MLX engine on Apple Silicon. Support for NVIDIA, AMD, and other platforms will be available in the coming days.
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.
Building effective agents requires key capabilities working together to achieve the user’s goals. Muse Glimmer is trained and evaluated on these capabilities:
- 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-30B High 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.