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  1. Models
  2. Text
  3. Meta

Muse Wonder

Meta's Muse Wonder is a frontier reasoning model designed for complex personal and professional agentic tasks. It accepts text, images, audio, video, and PDF documents across a one-million-token context window, supports tool calling and structured output, and always reasons before answering.

  • Reasoning
  • Vision
  • Audio Input
  • Video Input
  • Native PDF input
  • Tool Calling
  • Structured Output

Added Oct 12, 2026

Pricing

Auto routing · per 1M tokens
Input
$4.00
Output
$20.00
Cache read
$0.20
Compare provider prices

Specifications

Context window
1M
Max output
N/A
Avg output (7d)
1.3K tokens
Longer than 80% of models

Benchmarks

Sourced from Artificial Analysis.

Intelligence Index

43.3

Intelligence Index: 43.3
Better than 75% of models compared

Agentic work

AutomationBench-AA

Workflow automation with guardrail penalties

68.1%

Better than 96% of models compared

AutomationBench-AA Tasks Completed

Fully completed workflows without guardrail violations

39.1%

Better than 96% of models compared

Harvey LAB-AA v1.1

Legal tasks fully passed without material hallucinations

10.0%

Better than 93% of models compared

AA-Briefcase v1.1

Agentic knowledge work (Elo)

1659 Elo

Better than 96% of models compared

GDPval-AA v2.1

Economically valuable tasks (Elo)

1770 Elo

Better than 98% of models compared

Document reasoning

GDP.pdf

Professional PDF reasoning: all-pass rate

30.6%

Better than 96% of models compared

AA-LCR v1.1

Long context reasoning with updated grading

80.7%

Better than 88% of models compared

MLCR-AA

Medical long-context reasoning

22.2%

Better than 76% of models compared

Reasoning

HLE

Humanity's Last Exam

50.1%

Better than 96% of models compared

Coding

Terminal-Bench v4.0

Practical coding and terminal tasks

52.0%

Better than 92% of models compared

SciCode

Python programming for scientific computing

60.3%

Better than 95% of models compared

Legacy benchmarks

AA-LCR (unversioned / legacy)

Long context reasoning evaluation

80.7%

Better than 88% of models compared

Last updated Nov 1, 2026

Artificial Analysis

Providers

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Related text models

Compare Muse Wonder with similar models from the same provider or model family.

Muse Spark Open

meta/muse-spark-open

Built with Muse Spark Open. Meta's open-weight 816B-A42B model for coding and agentic workflows. Text and image input, text output, tool calling and structured output. Always reasons at low, medium, high, or xhigh effort. Custom Muse Model License and Usage Policy apply.

Muse Spark 1.3

meta/muse-spark-1.3

Meta's Muse Spark 1.3 is a frontier multimodal reasoning model for long-horizon coding and agentic workflows, with strong gains in computer use, browsing, professional tool use, codebase understanding, instruction following, and million-token retrieval. It accepts text, images, audio, video, and files, supports tool calling and structured output, and always reasons before answering.

Muse Spark 1.3 Contributor

meta/muse-spark-1.3-contributor

Meta's Muse Spark 1.3 Contributor is a frontier multimodal reasoning model for long-horizon coding and agentic workflows, with strong gains in computer use, browsing, professional tool use, codebase understanding, and million-token retrieval. It accepts text, images, audio, video, and files, supports tool calling and structured output, and always reasons before answering. Prompts and outputs may be used by Meta for training and to improve its products.

Muse Glimmer 30B

meta/muse-glimmer-30b

Meta's Muse Glimmer 30B is a dense, open-weight multimodal model distilled from Muse Spark for long-horizon agents and coding workflows. It supports multi-step reasoning, reliable tool use, failure recovery, image understanding, and more than 100 languages.

Muse Spark 1.2

meta/muse-spark-1.2

Meta's Muse Spark 1.2 is a multimodal reasoning model for complex agentic and coding tasks, with tool calling, structured output, and a one-million-token context window.

Muse Spark 1.2 Contributor

meta/muse-spark-1.2-contributor

A much cheaper opt-in version of Muse Spark 1.2 with the same multimodal coding and agentic capabilities. Prompts and outputs sent to this Contributor model may be used by Meta for training and to improve its products; use the standard Muse Spark 1.2 model if you do not want your data used for training.