Meta Llama: Muse Glimmer 30B
meta/muse-glimmer-30b
Try Muse Glimmer 30B for free in your browser, and add it to your app for free with Puter.js AI API.
Try it free Add to your app// npm install @heyputer/puter.js
import { puter } from '@heyputer/puter.js';
puter.ai.chat("Explain quantum computing in simple terms", {
model: "meta/muse-glimmer-30b"
}).then(response => {
document.body.innerHTML = response.message.content;
});
<html>
<body>
<script src="https://js.puter.com/v2/"></script>
<script>
puter.ai.chat("Explain quantum computing in simple terms", {
model: "meta/muse-glimmer-30b"
}).then(response => {
document.body.innerHTML = response.message.content;
});
</script>
</body>
</html>
Model Card
Muse Glimmer 30B is a 30-billion-parameter dense model from Meta Superintelligence Labs, pairing a causal transformer with a 1.8-billion-parameter vision encoder for text and image input. It's released under an Apache 2.0 license, Meta's first fully open-weight model since Muse Spark moved to a paid API.
On Meta's own benchmarks, it scores 76.0 on SWE-Bench Verified, 51.2 on SWE-Bench Pro, 94.7 on AIME 2026, 83.5 on GPQA Diamond, 75.5 on MCP Atlas, and 74.6 on DeepSearch QA, ahead of similarly sized open models like Gemma4 31B and Qwen3.6 27B on Meta's reporting. These figures are vendor-reported and not independently verified.
Where Muse Spark targets large-scale multi-agent orchestration, Muse Glimmer sits a size tier down, aimed at tool use, multi-step reasoning, coding, and LLM-as-a-judge evaluation with a 131,072-token context window. It fits developers who want agentic and coding capability at lower cost than the larger Muse Spark models.
Context Window 131K
tokens
Max Output 16K
tokens
Input Cost $0.3
per million tokens
Output Cost $1.2
per million tokens
Input text, image
modalities
Tool Use Yes
Release Date Aug 9, 2026
Try Muse Glimmer 30B for free
Try Muse Glimmer 30B instantly in your browser.
This playground uses the Puter.js AI API — no API keys or setup required.
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Muse Ember
Muse Ember is Meta's smallest and most efficient model, released on November 9, 2026. It has a 1,048,576-token context window, accepts text, image, audio, and video input, and returns text. Meta built it for repetitive agent work such as tool calls, coding subtasks, routing, document extraction, and operating a computer from screenshots. Meta reports 37.8% on AutomationBench-AA (Claude Haiku 5.5 scores 13.9%, GPT-6 Luna 20.2%) and 40.9% on Terminal-Bench 4.0. Haiku 5.5 scores higher on FrontierCode 2.0-AA. Artificial Analysis gives it an Intelligence Index score of 36, against 37 for Muse Spark 1.3. These results are vendor-reported. It suits developers who run high-volume agent subtasks at low cost, with a larger model such as Muse Wonder handling planning.
ChatMuse Spark Open
Muse Spark Open is an open-weight multimodal model from Meta, released on October 15, 2026. It is a mixture-of-experts model with 816B total and 42B active parameters and a 1,048,576-token context window. It accepts text, image, video, and audio input and returns text. It is aimed at agentic and tool-use work. Meta reports that it leads the open models it compared against on the agentic benchmarks MCP-Atlas and JobBench, and that it trails some closed Chinese models such as Kimi K3 and GLM 5.3 on the coding benchmark Terminal-Bench 2.1. These results are vendor-reported. It supports tool calling, structured output, and configurable reasoning effort. It suits developers who want an agent-capable model with open weights from a US lab.
ChatMuse Wonder
Muse Wonder is a multimodal agent model from Meta Superintelligence Labs, built on Muse Spark and released on October 12, 2026. It accepts text, images, video, audio, and PDF input, returns text, and has a 1,048,576-token context window. It is designed for long, multi-step agent work such as coding, legal and financial documents, and tasks across several apps. It supports tool calling, structured output, and configurable reasoning effort. Meta reports 78.8 on DeepSWE 1.1 and 53.1 on SWE-Marathon, and Artificial Analysis gives it an Intelligence Index score of 52, up from 48 for Muse Spark 1.3. Its per-token price is below Claude Fable 5.1 ($10 input, $50 output), though Claude Opus 5.5 scores higher on the index and on OSWorld.
Frequently Asked Questions
You can access Muse Glimmer 30B by Meta Llama through Puter.js AI API. Include the library in your web app or Node.js project and start making calls with just a few lines of JavaScript — no backend and no configuration required. You can also use it with Python or cURL via Puter's OpenAI-compatible API.
Muse Glimmer 30B is free to try with a Puter account. Every account includes a free AI allowance, and you can chat with it in the playground on this page. You can upgrade your account anytime for a larger allowance.
Muse Glimmer 30B is free to integrate using the Puter.js AI API. With the User-Pays Model, you can add AI to your app for $0, since users cover their own AI usage through their Puter account.
| Price per 1M tokens | |
|---|---|
| Input | $0.3 |
| Output | $1.2 |
Muse Glimmer 30B was created by Meta Llama and released on Aug 9, 2026.
Muse Glimmer 30B supports a context window of 131K tokens. For reference, that is roughly equivalent to 262 pages of text.
Muse Glimmer 30B can generate up to 16K tokens in a single response.
Muse Glimmer 30B accepts the following input types: text, image. It produces: text.
Yes, Muse Glimmer 30B supports tool use (function calling), allowing it to interact with external tools, APIs, and data sources as part of its response flow.
Yes — the Muse Glimmer 30B API works with any JavaScript framework, Node.js, or plain HTML through Puter.js. Just include the library and start building. See the documentation for more details.
Add Muse Glimmer 30B to your app for free
Developers can integrate Muse Glimmer 30B for free using the Puter.js AI API.
With the User-Pays Model, each user covers their own AI usage instead of the developer.