Meta Llama: Muse Wonder
meta/muse-wonder
Try Muse Wonder 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-wonder"
}).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-wonder"
}).then(response => {
document.body.innerHTML = response.message.content;
});
</script>
</body>
</html>
Model Card
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.
Context Window 1M
tokens
Max Output 944K
tokens
Input Cost $4
per million tokens
Output Cost $20
per million tokens
Input text, image, video, audio, pdf
modalities
Tool Use Yes
Release Date Oct 12, 2026
Output Speed 95
tokens / sec
Latency 26.34s
time to first token
Try Muse Wonder for free
Try Muse Wonder instantly in your browser.
This playground uses the Puter.js AI API — no API keys or setup required.
Benchmarks
How Muse Wonder performs on standard evaluations.
| Benchmark | Score |
|---|---|
| Humanity's Last Exam Cross-domain reasoning | 50.1% |
| SciCode Scientific programming | 60.3% |
| LCR Long-context reasoning | 80.7% |
Scores sourced from Artificial Analysis.
Find other Meta Llama models →
Muse Glimmer 30B
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.
ChatMuse Spark 1.1
Muse Spark 1.1 is a multimodal reasoning model from Meta Superintelligence Labs, built for agentic workflows. It accepts text, images, video, audio, and PDF documents as input and returns text, with a 1,048,576-token context window. The model is designed to orchestrate multi-agent workflows, acting as either a main agent that plans and delegates tasks or as a subagent, and it generalizes zero-shot to new tools, MCP servers, and custom skills. It supports parallel function calling, structured output, built-in search with citations, and configurable reasoning effort, and Meta reports strong results on coding across large codebases, computer-use tasks, and visual-to-code generation. This is Meta's first model available through a paid API, priced at $1.25 per million input tokens and $4.25 per million output tokens, aimed at developers building agentic coding tools and enterprise workflow automation.
ChatLlama 4 Maverick
Llama 4 Maverick is Meta's 400 billion total parameter MoE model with 17B active parameters and 128 experts, supporting 1M token context. It's natively multimodal with state-of-the-art performance on coding, reasoning, and image understanding tasks.
Frequently Asked Questions
You can access Muse Wonder 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 Wonder 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 Wonder 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 | $4 |
| Output | $20 |
Muse Wonder was created by Meta Llama and released on Oct 12, 2026.
Muse Wonder supports a context window of 1M tokens. For reference, that is roughly equivalent to 2,097 pages of text.
Muse Wonder can generate up to 944K tokens in a single response.
Muse Wonder accepts the following input types: text, image, video, audio, pdf. It produces: text.
Yes, Muse Wonder supports tool use (function calling), allowing it to interact with external tools, APIs, and data sources as part of its response flow.
Muse Wonder scores 52.4 on the Artificial Analysis Intelligence Index, outperforming 98% of tracked models.
Yes — the Muse Wonder 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 Wonder to your app for free
Developers can integrate Muse Wonder for free using the Puter.js AI API.
With the User-Pays Model, each user covers their own AI usage instead of the developer.