# Muse Glimmer 30B

Meta's open-weight return, licensed Apache 2.0. Muse Glimmer 30B is a 30B dense model distilled from the Muse Spark flagship, small enough to run on a single consumer GPU and tuned for agentic orchestration.

- **Model ID:** `meta/muse-glimmer-30b`
- **Type:** chat
- **Providers:** togetherai
- **Context window:** 131,072
- **Maximum output tokens:** 131,072
- **Pricing:** $0.35/1M input tokens, $1.50/1M output tokens
- **Canonical page:** https://vercel.com/ai-gateway/models/muse-glimmer-30b

## Supported parameters

Detailed capability metadata has not been reported for this model.

## Example

```ts
import { streamText } from 'ai'

const result = streamText({
  model: 'meta/muse-glimmer-30b',
  prompt: 'Why is the sky blue?'
})
```

## About

Muse Glimmer 30B was released August 10, 2026 as Meta's open-weight model for agentic work, licensed under Apache 2.0. It is a dense causal transformer of roughly 29.6 billion parameters across 52 layers, including a perception encoder that lets it accept interleaved text and images, and it supports more than 100 languages within a context window of 131.1K tokens.

The attention design matters for how it behaves at length: grouped-query attention with a repeating local-local-local-global pattern and a sliding window, so most layers attend locally and periodic global layers carry longer-range structure. Reasoning effort is selectable across low, medium, high, and xhigh through the system prompt.

Muse Glimmer 30B is a distillation of Meta's flagship, which shapes where it is strong. On agentic orchestration and reasoning it leads comparable open models, with 75.5 on MCP Atlas, 74.6 on DeepSearch QA, and 94.7 on AIME 2026. It trails on computer-use and terminal work, where Qwen3.6-27B stays ahead on OSWorld-Verified and TerminalBench 2.1. The knowledge cutoff is January 4, 2026.

Quantized to roughly 4 bits the model drops under 20 GB, which leaves room on a 24 to 32 GB machine for the KV cache and encoder. Through AI Gateway you can call it over an API instead, and keep the option of self-hosting later.

You can integrate Muse Glimmer 30B through AI SDK, Chat Completions API, Responses API, Messages API, or other API formats, from TypeScript or Python.

## What to consider

Muse Glimmer 30B is deliberately less capable than the Muse Spark flagship it was distilled from, and Meta says it does not meet the Frontier AI definition in its own scaling framework. Treat it as a capable open model rather than a flagship substitute.

Strength is uneven by task type. Agentic orchestration and reasoning lead the comparable open models, while computer-use and terminal work trail them. Match it to the first kind of workload rather than assuming the lead generalises.

On CI Memories, a privacy benchmark where a lower score is better, Muse Glimmer 30B scores worse than Gemma and better than Qwen, so it is not uniformly stronger than its peers. Meta recommends deploying it with guardrails, including human confirmation before irreversible actions.

The knowledge cutoff is January 4, 2026, so pair it with web search or retrieval for anything more recent.

## When to use

### Best For

- **Agentic orchestration** where it leads comparable open models on MCP Atlas
- **Apache 2.0 licensing** with no revenue threshold or commercial gate
- **Local deployment** at roughly 4-bit on a 24 to 32 GB machine
- **Multilingual work** across more than 100 languages
- **Interleaved text and images** handled by its built-in perception encoder

### Consider Alternatives When

- **Frontier capability** is required, where Muse Spark 1.2 is the flagship
- **Computer-use or terminal work** dominates, where comparable open models score higher
- **Privacy benchmarks** are a selection criterion, since its CI Memories result is mid-pack
- **Post-cutoff facts** are needed beyond January 2026 without retrieval

## Best for

- **Agentic Orchestration:** Leading comparable open models on MCP Atlas
- **Apache 2.0 Licensing:** No revenue threshold or commercial gate
- **Local Deployment:** Roughly 4-bit on a 24 to 32 GB machine
- **Multilingual Work:** More than 100 languages supported
- **Interleaved Text And Images:** Handled by a built-in perception encoder

## Consider alternatives

- **Frontier Capability:** Muse Spark 1.2 is the flagship it was distilled from
- **Computer-Use And Terminal Work:** Comparable open models score higher
- **Privacy Benchmark Selection:** Its CI Memories result is mid-pack
- **Post-Cutoff Facts:** January 2026 knowledge needs web search or retrieval

## Frequently asked questions

### What licence does Muse Glimmer 30B use?

Apache 2.0, with no revenue threshold or separate commercial licence required.

### How large is Muse Glimmer 30B?

Roughly 29.6 billion parameters in a dense transformer across 52 layers, including a perception encoder for image input.

### Can I run Muse Glimmer 30B locally?

Yes. At roughly 4-bit quantization it drops under 20 GB, leaving room on a 24 to 32 GB machine for the KV cache and encoder. Through AI Gateway you can call it over an API instead.

### What is Muse Glimmer 30B strongest at?

Agentic orchestration and reasoning. It reaches 75.5 on MCP Atlas, 74.6 on DeepSearch QA, and 94.7 on AIME 2026, ahead of comparable open models. It trails them on computer-use and terminal work.

### How does Muse Glimmer 30B relate to Muse Spark?

It is a distillation of the flagship. Meta states it does not meet the Frontier AI definition in its own scaling framework, so treat it as a capable open model rather than a flagship substitute.

### What is the context window for Muse Glimmer 30B?

The context window is 131.1K tokens, with up to 131.1K tokens per response.

### What safety guidance does Meta give?

Deploy with guardrails, including human confirmation before irreversible actions. On CI Memories, a privacy benchmark where lower is better, it scores mid-pack against comparable open models.

### Does Muse Glimmer 30B support Zero Data Retention?

Yes, Zero Data Retention is available for this model. Zero Data Retention is offered on a per-provider basis. See https://vercel.com/docs/ai-gateway/capabilities/zdr for details.

## Links

- [Model page](https://vercel.com/ai-gateway/models/muse-glimmer-30b)
- [AI Gateway documentation](https://vercel.com/docs/ai-gateway)
- [Provider model documentation](https://api.together.ai/models/meta-models/Muse-Glimmer-30B)