Muse Glimmer 30B
Muse Glimmer is a 30-billion-parameter open-weight multimodal model from Meta Superintelligence Labs, distilled from Muse Spark for always-on local agents, tool use, coding, and image understanding.
Quality & performance
Artificial Analysis doesn't cover this model (267 of 2059 have data). Quality data comes from independent evals covering widely used models.
Available at 12 providers10 with public prices · 2 free
| Provider | Tier | Input | Output | Cache read | Cache write | Context | Output limit | Status |
|---|---|---|---|---|---|---|---|---|
| Nvidia | First-party | Free | — | — | 131,072 | 131,072 | ||
| Requesty | Gateway | Free | — | — | 131,072 | 20,480 | ||
| EmpirioLabs AI | Gateway | $0.20 | $0.80 | $0.05 | — | 131,072 | 32,768 | |
| OpenRouter | Gateway | $0.30 | $1.10 | $0.04 | — | 131,072 | 131,072 | |
| Kilo Gateway | Gateway | $0.30 | $1.10 | $0.04 | — | 131,072 | 131,072 | |
| Eden AI deepinfra/meta-models/Muse-Glimmer-30B | Gateway | $0.30 | $1.20 | $0.04 | — | 131,072 | 131,072 | |
| Eden AI flexai/Muse-Glimmer-30B | Gateway | $0.30 | $1.20 | — | — | 131,072 | 131,072 | |
| NanoGPT | Gateway | $0.35 | $1.50 | $0.04 | — | 131,072 | 131,072 | |
| Vercel AI Gateway | Cloud | $0.35 | $1.50 | $0.04 | — | 131,072 | 131,072 | |
| Eden AI together_ai/meta-models/Muse-Glimmer-30B | Gateway | $0.35 | $1.50 | $0.04 | — | 131,072 | 131,072 | |
| Eden AI | Gateway | $0.35 | $1.50 | $0.04 | — | 131,072 | 131,072 | |
| Fireworks AI | Cloud | $0.35 | $1.50 | $0.04 | — | 131,072 | 131,072 |
Sorted by blended price (input×0.75 + output×0.25) asc. The official channel always shows regardless of rank. Whether a gateway's low price is actually usable can't be verified.
Your usage cost
2 more channels offer $0 (Nvidia, Requesty); free tiers usually have rate limits and no SLA, excluded from ranking.
Benchmark9 items
| Name | Conditions | Score | Metric | Source |
|---|---|---|---|---|
| MCP Atlas | variant: public | 75.5 | success rate | Source ↗ |
| DeepSearch QA | — | 74.6 | — | Source ↗ |
| SWE-Bench Pro | — | 51.2 | resolve rate | Source ↗ |
| SWE-Bench Verified | — | 76 | resolve rate | Source ↗ |
| Terminal-Bench | variant: with terminus2 · v2.1 | 51.7 | success rate | Source ↗ |
| OSWorld-Verified | — | 65.9 | success rate | Source ↗ |
| AIME 2026 | — | 94.7 | accuracy | Source ↗ |
| GPQA Diamond | variant: AA | 83.5 | accuracy | Source ↗ |
| CharXiv Reasoning | — | 78.8 | accuracy | Source ↗ |
The same benchmark scores very differently across harness / dataset, so the qualifying conditions must be shown together.