Meta has released Muse Wonder and Muse Wonder Full, its most capable models so far. Muse Wonder is a frontier model from Meta built for personal and professional AI agents. Both models target long-running agent work: coding, legal and finance tasks, and computer use. Muse Wonder is live now on the Meta Model API and Muse Code. Muse Wonder Full is limited to paid Muse Code plans.
TL;DR
- Size: 1M-token context, input of text, image, video and audio, output of text.
- Runs on: Meta’s cloud only, via the API or Muse Code CLI (macOS, Linux, Windows).
- Performance: Muse Wonder Full leads 6 of 14 rows in Meta’s benchmark table. It trails Claude Opus 5.5 on several.
- Best: Muse Wonder scores 85.6 on MemoryBench, the top result among all 5 models listed.
- Worst: Muse Wonder scores 50.0 on ProgramBench, against 70.5 for Claude Opus 5.5.
What is Muse Wonder?
Muse Wonder follows Muse Spark on Meta’s scaling ladder. Meta team states that it was trained at its Prometheus data center, with gains across pretraining and reinforcement learning. Meta credits a larger base model for better results on unfamiliar tasks.
Two named behaviors define the release:
- Active Memory: carries user context across conversations and pulls back the details a task needs.
- Active Focus: keeps working on hard tasks, tries alternatives, and checks in before important actions.
For developers, Muse Wonder plans long tasks and delegates to sub-agents. It also operates browsers, desktop and mobile apps.
How does Muse Wonder perform on benchmarks?
All scores below come from Meta’s published table at max reasoning effort. Several rows use third-party sources such as Artificial Analysis, Vals AI and Datacurve.
| Benchmark | Muse Wonder Full | Muse Wonder | Claude Fable 5.1 | GPT-6 Astra | Claude Opus 5.5 |
|---|---|---|---|---|---|
| Personal Superintelligence Index (internal) | 59.1 | 54.3 | 49.9 | 54.5 | 55.2 |
| Harvey LAB-AA | 13.1 | 10.0 | 6.4 | 8.6 | 4.2 |
| Vals Finance Agent V2 | 59.1 | 56.6 | 58.9 | 53.5 | 58.6 |
| AutomationBench-AA | 45.4 | 39.1 | 32.1 | 41.6 | 41.2 |
| OSWorld V2.1 (partial) | 78.4 | 72.6 | 80.7 | 75.8 | 81.8 |
| MemoryBench (internal) | 85.5 | 85.6 | 79.0 | 81.4 | 81.5 |
| Terminal-Bench 4.0-AA | 57.1 | 52.0 | 52.0 | 59.1 | 59.6 |
| DeepSWE 1.1 | 79.0 | 78.8 | 67.4 | 74.1 | 74.2 |
| SWE-Marathon | 57.5 (xhigh) | 53.1 | 45.6 | 42.5 | 46.3 |
| ProgramBench | 59.0 | 50.0 | 55.5 | 52.5 | 70.5 |
| AA Intelligence Index v4.3 | 56 | 52 | 53 | 53 | 58 |
Note: PSI, MemoryBench and FocusBench are internal Meta evaluations. The model card says competitor runs are best-effort and may not be tuned. SWE-Marathon results for rivals used Codex and Claude Code, so it is not a common-harness comparison.
