# Muse Glimmer 30B Usage, Cost & Rank | OpenCode Data

> Muse Glimmer 30B costs $0.00 per 1M input tokens and $0.00 per 1M output tokens.

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.

- Page: https://opencode.ai/data/meta/muse-glimmer-30b
- JSON: https://opencode.ai/data/meta/muse-glimmer-30b.json
- Model ID: meta/muse-glimmer-30b
- Lab: Meta

## Model facts

| Fact | Value |
| --- | --- |
| Context window | 131K |
| Max output | 131K |
| Knowledge cutoff | 2026-01-04 |
| Release date | 2026-08-10 |
| Input modalities | text, image |
| Output modalities | text |
| Reasoning | Yes |
| Tool calling | Yes |
| Open weights | Yes |
| Weights | [Hugging Face](https://huggingface.co/meta-models/Muse-Glimmer-30B) |

## Pricing: USD per 1M tokens

| Input | Output | Cached input | Cache write |
| --- | --- | --- | --- |
| $0.00 | $0.00 | - | - |

## Benchmarks

| Benchmark | Score | Metric | Source |
| --- | --- | --- | --- |
| MCP Atlas | 75.5 | success rate | https://huggingface.co/meta-models/Muse-Glimmer-30B |
| DeepSearch QA | 74.6 |  | https://huggingface.co/meta-models/Muse-Glimmer-30B |
| SWE-Bench Pro | 51.2 | resolve rate | https://huggingface.co/meta-models/Muse-Glimmer-30B |
| SWE-Bench Verified | 76 | resolve rate | https://huggingface.co/meta-models/Muse-Glimmer-30B |
| Terminal-Bench | 51.7 | success rate | https://huggingface.co/meta-models/Muse-Glimmer-30B |
| OSWorld-Verified | 65.9 | success rate | https://huggingface.co/meta-models/Muse-Glimmer-30B |
| AIME 2026 | 94.7 | accuracy | https://huggingface.co/meta-models/Muse-Glimmer-30B |
| GPQA Diamond | 83.5 | accuracy | https://huggingface.co/meta-models/Muse-Glimmer-30B |
| CharXiv Reasoning | 78.8 | accuracy | https://huggingface.co/meta-models/Muse-Glimmer-30B |

## Methodology

- Updates: Aggregated every hour. Days and weeks use UTC.
- Tokens: Input, output, reasoning, and cached tokens for each request.
- Users and sessions: Approximate counts of distinct users and OpenCode sessions.
- Cost: Session cost is the average cost per OpenCode session. Token prices are list prices from the OpenCode model catalog.
- Retention: The share of a model's users in one week who use it again the next week.
- Citation: Cite OpenCode Data (opencode.ai/data) with the update time shown at the top of the page.