Fourteen months after a ground-up overhaul of its AI stack, Meta has delivered its first "frontier-grade" answer: Muse Wonder and Muse Wonder Full. The company claims state-of-the-art results on key benchmarks for personal and professional agents, positioning the series as its next step toward "personal superintelligence" (Meta's official blog).
The real stars are memory and focus
Both headline capabilities of Muse Wonder point at the same goal — making agents genuinely usable. Active Memory means the model carries important context about you across conversations and actively retrieves the details relevant to the task at hand. Active Focus describes sustained effort toward a challenging goal: trying alternative approaches, working through blockers, adapting to new information, and asking for clarification or checking in before important actions. The evaluation suite was built for exactly this — FocusBench quantifies focus, SWE-Marathon tests sustained execution on long-running software projects, and the legal domain uses Harvey's Legal Agent Benchmark, which requires models to work from source documents and produce legal work products. These are not your traditional leaderboard runs; they target whether a "digital worker" can survive real work.
The hidden dividend of a stronger base
The most informative part of the blog is a generalization analysis. Meta compared Muse Spark 1.3 and Wonder Full on how performance scales with test-time compute, across tasks targeted during post-training ("in-distribution") versus tasks that were not ("out-of-distribution"). Wonder Full outperforms unfamiliar tasks beyond what the Muse Spark 1.3 trend would predict — an upward shift Meta attributes to larger, stronger base models: a stronger base lets post-training gains carry over more effectively to unfamiliar tasks. This is a quantified version of what the community calls "big model smell": two models with near-identical benchmark scores can diverge sharply on unseen tasks, and a personal agent faces endless unanticipated combinations of goals, tools, and constraints that no training run can fully rehearse.
The commercial signal: frontier capability gets a price tag
Muse Wonder is available through the Meta Model API and Muse Code; the stronger Wonder Full is currently reserved for Muse Code subscribers — CNBC reports the plans run $50 or $200 per month. TechXplore's coverage notes Meta claims the new models perform at a level comparable to those of OpenAI and Anthropic. The training-side investment is stated plainly: the models were trained at Meta's gigawatt-scale Prometheus data center. On safety, Meta calls this its most aligned model family to date, with its strongest safeguards yet against CBRN (chemical, biological, radiological, nuclear) and cyber misuse, while preserving frontier cyber capabilities for defensive use under its Superintelligence Scaling Framework.
So what
Meta has shipped "remembers you, works for you, keeps working" as both benchmark and product — a more meaningful shift than another leaderboard shuffle, because the agent race is being re-scored from "answers correctly" to "holds up under load." For Meta itself, the story of revenue beyond advertising now has a footnote starting at $50 a month. The next time a model release crosses your feed, ask one question first: what is its evaluation actually measuring?