Muse Wonder Full
MultimodalMuse Wonder Full is a proprietary reasoning model from Meta Superintelligence Labs, released on October 12, 2026 for personal and professional agents and long-running coding tasks. Active Memory retains relevant context across interactions, and Active Focus supports sustained task execution and error recovery. Muse Wonder Full is the higher-capability variant, available to Muse Code Power Usage and Maximum Usage subscribers. It is not available through the Meta Model API. The model card specifies text, image, video, and audio inputs, text output, and a 1M-token inference context for both variants.
Key Specifications
Parameters
-
Context
-
Release Date
October 12, 2026
Average Score
57.1%
Timeline
Key dates in the model's history
Announcement
October 12, 2026
Last Update
October 13, 2026
Today
October 17, 2026
Technical Specifications
Parameters
-
Training Tokens
-
Knowledge Cutoff
-
Family
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Capabilities
MultimodalZeroEval
Benchmark Results
Model performance metrics across various tests and benchmarks
Other Tests
Specialized benchmarks
Artificial Analysis
Meta Muse Wonder model card, October 12, 2026; max reasoning effort. Artificial Analysis Intelligence Index v4.3, independently evaluated by Artificial Analysis and reproduced in Meta's model card. Reported on a 0-100 scale, stored as a fraction. • Self-reported
AutomationBench-AA
Meta Muse Wonder model card, October 12, 2026; max reasoning effort. Artificial Analysis strict pass@1; every deterministic end-state assertion must pass. Reported on a 0-100 scale, stored as a fraction. • Self-reported
DeepSWE 1.1
Meta Muse Wonder model card, October 12, 2026; max reasoning effort. Datacurve third-party pass@1 results; 113 tasks across 91 repositories and five languages, graded with functional and regression tests. Reported on a 0-100 scale, stored as a fraction. • Self-reported
FocusBench
Meta Muse Wonder model card, October 12, 2026; max reasoning effort. Internal personal agent evaluation across housing, travel, IT, and finance; persistence through blockers and adaptation across interactions and changing environments. Reported on a 0-100 scale, stored as a fraction. Score from the research blog chart; methodology: https://research.meta.ai/static/muse-wonder-model-card. • Self-reported
GDP.pdf
Meta Muse Wonder model card, October 12, 2026; max reasoning effort. Professional tasks grounded in supplied PDF material; score reported by Surge. Reported on a 0-100 scale, stored as a fraction. • Self-reported
Harvey LAB-AA
Meta Muse Wonder model card, October 12, 2026; max reasoning effort. Artificial Analysis result, version 1.1; share of legal deliverables meeting every criterion and passing hallucination checks. Reported on a 0-100 scale, stored as a fraction. • Self-reported
HealthBench Professional
Meta Muse Wonder model card, October 12, 2026; max reasoning effort. 525 professional health questions, no tools, GPT-5.4 rubric grader; length-normalized weighted rubric score in a common internal framework. Reported on a 0-100 scale, stored as a fraction. • Self-reported
Job Bench
Meta Muse Wonder model card, October 12, 2026; max reasoning effort. 65 professional tasks across 35 occupations; mean task-specific rubric reward. Reported on a 0-100 scale, stored as a fraction. • Self-reported
MemoryBench
Meta Muse Wonder model card, October 12, 2026; max reasoning effort. Internal recall evaluation over 64K-100K-token conversations containing distractors, conflicting facts, and later updates; LLM judge with weighted question-specific rubrics. Reported on a 0-100 scale, stored as a fraction. • Self-reported
OSWorld 2.1 (partial)
Meta Muse Wonder model card, October 12, 2026; max reasoning effort. 108 desktop workflows in a common internal framework with official environments and graders; mean per-task partial reward. Reported on a 0-100 scale, stored as a fraction. • Self-reported
OSWorld 2.1 (strict)
Meta Muse Wonder model card, October 12, 2026; max reasoning effort. 108 desktop workflows in a common internal framework with official environments and graders; strict binary completion, separate from partial reward. Reported on a 0-100 scale, stored as a fraction. • Self-reported
Personal Superintelligence Index
Meta Muse Wonder model card, October 12, 2026; max reasoning effort. Internal composite of Agents' Last Exam, AutomationBench-AA, FocusBench, JobBench, GDP.pdf, VBT, and DRACO. Reported on a 0-100 scale, stored as a fraction. • Self-reported
Program Bench
Meta Muse Wonder model card, October 12, 2026; max reasoning effort. All models use mini-swe-agent; share of rollouts passing at least 95% of hidden behavioral tests; internet disabled after setup. Reported on a 0-100 scale, stored as a fraction. • Self-reported
SWE-Marathon
Meta Muse Wonder model card, October 12, 2026; xhigh reasoning effort. Abundant AI pass@1 results on long-running software projects. External results retain their reporting harnesses; not a controlled common-harness comparison. Reported on a 0-100 scale, stored as a fraction. • Self-reported
Terminal-Bench 4.0-AA
Meta Muse Wonder model card, October 12, 2026; max reasoning effort. Artificial Analysis reported accuracy on terminal tasks with executable final-container-state verifiers. Reported on a 0-100 scale, stored as a fraction. • Self-reported
Vals Finance Agent v2
Meta Muse Wonder model card, October 12, 2026; max reasoning effort. Vals AI private-test-set accuracy on financial analyst research tasks. Reported on a 0-100 scale, stored as a fraction. • Self-reported
License & Metadata
License
proprietary
Announcement Date
October 12, 2026
Last Updated
October 13, 2026
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