Meta · AI model
Muse Glimmer
30B-class (29.6B including the vision encoder) dense multimodal open-weight model distilled from Muse Spark, released under Apache 2.0 for local and edge deployment. Text and image input, text output, 128K-token default context, selectable reasoning strength.
Last verified Available 5 sources
Key specs
- Provider
- Meta
- Family
- Muse Glimmer
- Availability
- Available
- Release date
- 2026-08-10
- License
- Open source (Apache 2.0)
- Context window
- 131,072 tokens
- Knowledge cutoff
- January 2026
- API access
- No
- Consumer app
- Unknown
Capabilities
Inputs
- Text input Yes
- Image input Yes
- Audio input No Audio input/output is not supported
- Video input Partial Not explicitly optimized for video; video input is processed as individual frames
- File input Unknown
Outputs
- Text output Yes
- Image output No Text output only
- Audio output No Audio input/output is not supported
Features
- Reasoning Native Reasons step by step before answering. Strength (low/medium/high/xhigh) is set in the system prompt
- Tool calling Yes
- Web search Unknown
"Unknown" means we have not verified this capability yet; it does not mean the model lacks it.
Pricing
No current prices listed.
Benchmarks
| Benchmark | Setting | Score | Reported by | Measured | Verified | Source |
|---|---|---|---|---|---|---|
| CharXiv Reasoning accuracy %, higher is better | reasoning questions, validation set (1,000 questions), tools not stated; high reasoning strength; gpt-oss-120b judge, mean of 4 attempts | 78.8 % | Self-reported | — | 2026-10-25 | meta-models/Muse-Glimmer-30B model card (Meta Superintelligence Lab) |
| GDPval-AA v2.1 Elo, higher is better | v2.1; high reasoning; Artificial Analysis Stirrup harness | 783 Elo | Third party | 2026-10-25 | 2026-10-25 | GDPval-AA v2.1 Leaderboard (Artificial Analysis) |
| Humanity's Last Exam accuracy %, higher is better | text-only subset (2,158 questions), no tools; high reasoning strength; result sourced by Meta from Artificial Analysis | 22 % | Self-reported | — | 2026-10-25 | meta-models/Muse-Glimmer-30B model card (Meta Superintelligence Lab) |
| Text Arena Arena score, higher is better | Overall, style control on (default); effort not stated | 1,425 | Third party | 2026-10-23 | 2026-10-25 | Text Arena leaderboard (Arena) |
There is no maintained public leaderboard (the project site says it is under construction), so almost all current numbers are self-reported by providers. Results with tools such as a code interpreter are much higher than without: Alibaba reports 83.7% without and 90.2% with tools for Qwen3.8-27B. Top models now exceed the human baseline, so differences at the top are small and may reflect label noise. Descriptive-question scores are a different metric and must not be recorded here.
Elo is a relative rating: values change as models are added and the scale is re-anchored, so always store the date a rating was read. For example, Anthropic's launch page gives 1846 for Claude Opus 5.5, while the leaderboard on 2026-10-12 shows 1866 (Max, Default Fallback). Earlier versions (Google cites "GDPVal-AA v2") are not comparable. Judging is done by an LLM, not by human experts. Anthropic models are listed with "Default Fallback", meaning refused steps are routed to a fallback model. AA-Briefcase and GDP.pdf are separate evaluations.
Results differ a lot by setting: with tools versus without tools (Anthropic reports 45.9% without tools and 57.4% with tools for Claude Haiku 5.5), and the full set versus the text-only subset. Providers use different judge models and harnesses (Scale uses o3-mini; Alibaba states GPT-4o). Derived sets such as HLE-Verified (a third-party revision with 1,811 scored items), HLE-Rolling and HLE-Diamond are different item sets and are not comparable. The questions are public, so contamination is possible, and community reviews have reported flawed items.
Scores measure voter preference, which can be influenced by answer style and length. The leaderboard offers a style-control adjustment, so record which view was used. Scores shift with every update, so a value is only meaningful together with its leaderboard date. Entries marked "Preliminary" or with few votes have wide confidence intervals. The same model can appear several times with different reasoning-effort suffixes (for example -high or -max). Category views (Coding, Math, Creative Writing and others) are different rankings from Overall.
One row per result, without rank numbers: each result was measured under its own setting (effort level, harness, tools, test version), as stated by the source, and scores compare only within the same setting. Self-reported results come from the provider and have not been reproduced independently.
Rankings
- #22 of 42 in Best AI API as of 2026-10-26
Derived from our published ranking snapshots; each ranking explains its criteria and weights.
Compare Muse Glimmer
Guides
About Muse Glimmer
Muse Glimmer is Meta's open-weight multimodal model, announced on 10 August 2026 and published on Hugging Face under the Apache 2.0 license. It is a dense causal transformer with a perception (vision) encoder, 29.6B parameters in total, distilled from Muse Spark and built for local and edge deployment. It accepts text and image input, returns text, and reasons step by step before answering at a selectable strength. It is not served on the Meta Model API; Meta lists third-party hosts such as Fireworks AI, Together AI and OpenRouter.
Strengths and weaknesses
Strengths
- Open weights under Apache 2.0, with bf16, GGUF and ExecuTorch builds on Hugging Face (Q4_K_M GGUF about 17 GB, which Meta says fits under 24 GB VRAM)
- Text and image input, plus documented function calling
- Reasoning strength selectable: low, medium, high or xhigh
- Trained on data from more than 100 languages
Weaknesses
- Not served on the Meta Model API; it runs self-hosted or through third-party hosts
- No audio input or output; video is not optimized and is processed only as individual frames
- 128K-token default context window, smaller than Muse Spark's 1,048,576 tokens
Sources
Last verified Page updated 2026-10-26