Intelligence
Index v5.0
Muse Wonder overview
Muse Wonder by Meta
Muse Wonder takes text, image and video input and returns text through the chat completions endpoint.
Muse Wonder specs
- Model ID
meta/muse-wonder- Developer
- Meta
- Context window
- 1,048,576 tokens
- Max output
- 943,718 tokens
- Input
- Text, Image, Video
- Output
- Text
- API
- OpenAI-compatible, base URL
https://api.aimlapi.com/v1
What Is Muse Wonder?
Muse Wonder is Meta's frontier agentic model, released on 12 October 2026 and presented by Meta as its most capable model to date. It is aimed at work that runs long rather than work that answers fast: agents that plan a task, delegate parts of it to sub-agents, call tools in parallel and recover from their own errors instead of stopping at the first failure.
Meta ships it in two variants. Muse Wonder, which Meta serves through its own Model API as muse-wonder-1.0, is the one available here as meta/muse-wonder. Muse Wonder Full (muse-wonder-full-1.0) is the larger variant and is sold only inside Muse Code subscriptions, so it is not an API model.
What Meta Publishes
- Context length: 1,048,576 tokens
- Input: text, image and video, output: text. Meta's own model card also lists audio input.
- Reasoning effort: published results use the max setting; an xhigh setting is also reported
What It Is Built For
Meta names four headline capabilities: frontier coding across large repositories, with multi-file changes made and verified in parallel; long-running agents that plan work, delegate to sub-agents, call tools in parallel and recover from errors; advanced computer use, where an agent sees and operates browsers, desktop and mobile apps; and active memory that carries across conversations.
What Meta Reports
Meta's scorecard sets Muse Wonder and Muse Wonder Full against GPT-6 Astra, Claude Fable 5.1 and Claude Opus 5.5, all at max reasoning effort. Muse Wonder's strongest published figures are MemoryBench 85.6 and DeepSWE 1.1 at 78.8, both ahead of all three compared models, and SWE-Marathon at 53.1. It is reported behind Claude Opus 5.5 on Terminal-Bench 4.0-AA (52.0 against 59.6), on ProgramBench (50.0 against 70.5) and on HealthBench Pro (60.5 against 65.6). On Artificial Analysis's Intelligence Index v4.3 Meta reports 52 for Muse Wonder and 56 for Muse Wonder Full, against 58 for Claude Opus 5.5.
Meta's own methodology notes that some rows use third-party result sources while others are internal, that one coding row is not a controlled common-harness comparison, and that competitor runs may not be tuned to those vendors' strengths. None of the figures has been reproduced here.
What Meta Says About the Limits
Meta's model card names visual and spatial reasoning, especially 3D tasks, as a known limitation, and notes the model has not been evaluated on every language in its pre-training data. The card also records that, without mitigations, Meta does not rule out the model reaching the high-risk threshold in the chemical and biological domain under its Superintelligence Scaling Framework, which is why domain-specific safety post-training and system-level classifiers are applied.
Sources: dev.meta.ai, research.meta.ai
Use cases
Meta documents Muse Wonder for frontier coding across large repositories, where the model makes and verifies multi-file changes in parallel rather than editing one file at a time.
It is also documented for long-running agents: work that is planned, delegated to sub-agents, carried out with tools called in parallel, and resumed after an error rather than abandoned.
Meta lists advanced computer use, agents that see and operate browsers, desktop and mobile applications, and active memory that carries context from one conversation into the next.
In Meta's own framing the model is built for personal and professional agents handling multi-step real-world tasks, and for domain knowledge work in law, finance and health: its published scorecard includes legal, financial-analyst and professional health evaluations alongside the coding ones.
Capabilities
- File input
- Reasoning
- Streaming
- Structured outputs
- Function calling
- Vision
Muse Wonder benchmarks by 
Artificial Analysis
See how Muse Wonder compares in intelligence, generation speed, cost per task and latency. Data retrieved Nov 3, 2026.
Benchmark results
Compare 5 models available on AI/ML API across 4 independent benchmarks.
Humanity's Last Exam
Expert-written questions across academic subjects.
Terminal-Bench 4.0
Agentic tasks completed in a command-line environment.
AA-LCR
Reasoning across long documents of about 100k tokens.
SciCode
Python coding tasks drawn from scientific research.
Muse Wonder API pricing
| Usage | Price | Unit |
|---|---|---|
| Input | $4.23 | 1M tokens |
| Output | $21.16 | 1M tokens |
| Cached input | $0.2116 | 1M tokens |
Prices from the AI/ML API price list, checked on Nov 4, 2026.
Muse Wonder vs other models: token prices
AI/ML API input and output rates for 5 models, per million tokens.
| Model | Input | Output |
|---|---|---|
 Muse Wonder | $4.23 | $21.16 |
| Muse Glimmer 30B | $0.3703 | $1.59 |
| Muse Spark Open | $1.32 | $4.5 |
| Muse Spark 1.3 | $1.32 | $4.5 |
| Muse Spark 1.2 | $1.32 | $4.5 |
How to use the Muse Wonder API
View docs
Build with your agent
Give your agent the model details, code and documentation.
Paste it into your coding agent to build the integration.
1Get your API key
Create API keyexport AIMLAPI_KEY="8f2b4c7d9a1e6b3f…"2Make your first request
curl 'https://api.aimlapi.com/v1/chat/completions' \
-H "Authorization: Bearer $AIMLAPI_KEY" \
-H 'Content-Type: application/json' \
-d '{
"model": "meta/muse-wonder",
"messages": [
{
"role": "user",
"content": "Write a haiku about the ocean."
}
]
}'import os
import requests
headers = {
"Authorization": f"Bearer {os.environ['AIMLAPI_KEY']}",
"Content-Type": "application/json",
}
payload = {
"model": "meta/muse-wonder",
"messages": [
{
"role": "user",
"content": "Write a haiku about the ocean."
}
]
}
response = requests.post("https://api.aimlapi.com/v1/chat/completions", headers=headers, json=payload, timeout=120)
response.raise_for_status()
print(response.json())const response = await fetch("https://api.aimlapi.com/v1/chat/completions", {
method: "POST",
headers: {
Authorization: `Bearer ${process.env.AIMLAPI_KEY}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"model": "meta/muse-wonder",
"messages": [
{
"role": "user",
"content": "Write a haiku about the ocean."
}
]
}),
});
if (!response.ok) throw new Error(`HTTP ${response.status}`);
console.log(await response.json());Models similar to Muse Wonder
Muse Wonder FAQ
Is Muse Wonder available on AI/ML API?
Yes. Muse Wonder is live on AI/ML API as meta/muse-wonder, through the chat completions endpoint.
What is the context window of Muse Wonder?
Muse Wonder has a 1,048,576 tokens context window and can return up to 943,718 tokens.
What inputs and outputs does Muse Wonder support?
Muse Wonder takes image, text and video as input and returns text. Meta's own model card also lists audio input.
What is the difference between Muse Wonder and Muse Wonder Full?
Muse Wonder is the variant Meta serves through its Model API. Muse Wonder Full is the larger variant and is available only to Muse Code subscribers on Meta's Power Usage and Maximum Usage plans, so it is not offered as an API model. Meta reports Muse Wonder Full ahead of Muse Wonder on almost every published benchmark.
What is Muse Wonder good at?
Meta positions it for long-running agents, computer use and coding across large repositories, with memory that carries across conversations. Its strongest published figures are MemoryBench 85.6 and DeepSWE 1.1 at 78.8, both ahead of GPT-6 Astra, Claude Fable 5.1 and Claude Opus 5.5 in Meta's scorecard.
How much does Muse Wonder cost?
Muse Wonder costs $4.23 input and $21.16 output per 1M tokens on AI/ML API, billed as you go with no subscription.
Have Muse Wonder's benchmark results been independently verified?
No. The figures on this page come from Meta's own scorecard published at release, with the model at max reasoning effort. Meta's methodology notes that some rows use third-party result sources while others are internal, and that competitor runs may not be tuned to those vendors' strengths.
What is the Muse Wonder model ID?
Use meta/muse-wonder as the model parameter. Requests go to the OpenAI-compatible endpoint with your AI/ML API key.