Everything about Muse Wonder

Muse Wonder at a glance

Muse Wonder is a language model from Meta, released on October 12, 2026. It is a very recent release. It is a closed model: you access it through an API, from Meta or one of the 9 providers listed on models.dev. It has a reasoning mode: it can "think" before answering, which helps with multi-step problems but uses more tokens, and therefore more money.

We file it under frontier models (24 models in this category on the site).

Measured performance

Epoch AI publishes 2 results for Muse Wonder on our basket's benchmarks: too few to compute a reliable capability index.

• OTIS Mock AIME (competition mathematics problems): 99.4% — the best tracked model, GPT-5.5 Pro, reaches 100%
• FrontierMath (very hard research-level mathematics): 82.1% — the best tracked model, GPT-6 Astra, reaches 93.7%

What it can do

Its context window reaches 1,048,576 tokens, roughly 1,573 pages of text at 500 words per page: enough to load a whole book or a mid-sized codebase. A single answer can run to 131,072 tokens.

It accepts as input: text, image, video, PDF and audio.

It can call tools (functions, APIs) and return structured JSON output, which makes it a possible foundation for an AI agent.

How much does Muse Wonder cost?

Muse Wonder costs $4 per million input tokens and $20 per million output tokens (developer's price, checked on October 22, 2026).

To put it in perspective: reading 20,000 tokens (about thirty pages) and writing 2,000 costs roughly $0.12; a thousand of those tasks, $120.

That is 1.4 times the median output price of frontier models ($14.5).

Tokens read back from cache cost $0.20 per million, which helps an agent that keeps resending the same context.

To estimate your own budget, use the agent cost calculator.

What is it good for?

Given its category, Muse Wonder is mainly aimed at hard tasks where quality matters more than cost: deep reasoning, complex code, long analyses.

Its very large context window makes it worth considering for analysing large sets of documents in a single request.

Before adopting it, test it on your own examples: benchmarks measure standard tasks, not your job.

Comparable models

In the same category, the models closest to Muse Wonder are:

• Claude Opus 5.5 (Anthropic, index 95.9)
• GPT-5.6 Sol (OpenAI, index 87.3)
• Claude Opus 5 (Anthropic, index 92.7)
• GPT-5.4 (OpenAI, index 87.2)