Meta's Muse Wonder is a frontier model family now. The full version is not for sale.
Meta's Superintelligence Lab shipped Muse Wonder and Wonder Full on Monday. Third-party evals now put them near the frontier; the full model is not for sale.
Vlad Makarovreviewed and publishedOn Monday Meta's Superintelligence Lab released its first frontier model family, fourteen months after it began a ground-up overhaul of its AI effort. Muse Wonder is the fast tier; Muse Wonder Full is the larger model. Both were trained at Meta's Prometheus data center in central Ohio, and Meta's launch post says the pair "mark the next step on our scaling ladder and a major leap toward personal superintelligence." Independent scoring mostly agrees the jump is real. It does not settle whether the full model is genuinely frontier class, because Meta will not sell it outside a subscription.
Meta shipped two models and put a wall between them
The launch is really two launches with a shared name, and the split decides what anyone outside Meta can check. Muse Wonder is served through the Meta Model API at $4 per million input tokens, $0.20 per million cached input and $20 per million output. TechCrunch notes that lands at roughly the same cost as GPT-6 Sol or Opus 5.5.
Muse Wonder Full does not appear on the API at all. Meta routes it only to Muse Code subscribers, whose plans run $50 and $200 a month; the Muse personal-agent app sells $20 and $100 tiers and gets Wonder first, with artifact generation and heavier reasoning work promised later.
| Item | Muse Wonder | Muse Wonder Full |
|---|---|---|
| Where it runs | Meta Model API, Muse Code | Muse Code only |
| Price per M tokens | $4 in, $20 out | Not published |
| Context window | 1M | 1M |
| Input | text, image, video, audio | text, image, video, audio |
| Output | text | text |
| AA Intelligence Index v4.3 | 52 | 56 |
A $20 monthly ceiling on the app is not a frontier-model price so much as a consumer subscription price, which is the bet CNBC framed as Meta testing whether users will pay for agents at all.
By Meta's own testing, it beats Opus 5.5 on sustained coding
The model card's headline numbers are Meta's, measured by Meta, and should be read that way. On SWE-Marathon, Meta reports 57.5% for Wonder Full against 46.3% for Opus 5.5 and 42.5% for GPT-6 Astra. On DeepSWE v1.1 it reports 79.0%, versus 74.2% and 74.1%. On Harvey LAB, a legal benchmark, 13.1 against 8.6 for Astra. MemoryBench lands at 85.5 against 81.5 for Opus 5.5.
The last one is the release's real thesis. Meta's pitch is active memory and active focus: state that persists across days, weeks and months, and a model trained to route around blockers instead of stalling. Meta cites MemoryBench, which is on arXiv rather than in a vendor repository, so the metric at least exists independently of the launch. Whether a single long-horizon memory score transfers to the medium-horizon tasks most subscribers actually run is a different question, and Meta does not answer it.
Artificial Analysis scores it 56, two points off Opus 5.5
The most useful third-party read is Artificial Analysis, whose Intelligence Index v4.3 at maximum reasoning effort puts Muse Wonder Full at 56 and Muse Wonder at 52, against 48 for Muse Spark 1.3 six weeks earlier. Opus 5.5 still leads at 58. That gap is small enough to be inside the noise the index has shown across recent releases, and it is the gap the whole "is this really frontier" fight is about.
The supporting detail matters more than the headline. Wonder Full improved on all ten index evals, with the biggest gains in agentic work: Terminal-Bench 4.0 from 33% to 57%, GDPval-AA v2.1 from 1681 to 1857 Elo, AA-Briefcase from 1581 to 1729. It ranks top three on six of ten, first on GDP.pdf and CritPt at 33% each. On the Coding Agents Index, Wonder Full inside Muse Code scores 68, tying Claude Code with Sonnet 5.5 and clearing Opus 5.5 at 66.
Cost is where the fast tier looks less impressive. Artificial Analysis measures Wonder at $4.52 per index task, driven by heavy input consumption, against $0.72 for GPT-6.1 Sol, $1.99 for Gemini 4 Argon and $3.26 for GPT-6 Astra. Vals AI adds a second opinion: Wonder Full (Max) at fifth on its index, up 6.4 points over the predecessor and ahead of every OpenAI model, with ProofBench at 94 of 100 against 58 for Spark 1.3, where the top models sit at 99 to 100.
The top Reddit post on launch day was titled "Big model smell"
r/singularity's most-upvoted post on the day read "Big model smell: muse Wonder," and it drew 680 points and 159 comments, ahead of the launch announcement itself at 468 and 203. The phrase is Aidan McLaughlin's term for a model that behaves as though it is larger than its benchmark scores suggest, and the thread treated it as a compliment with a caveat rather than a complaint.
The caveat is the interesting part. Commenters argued that Meta's own plots, which compare performance on post-trained against non-post-trained tasks, are evidence against benchmark gaming, because a model tuned for a scorecard should show the opposite pattern. What the plots cannot show is the model's class. Latent Space's roundup makes the same point from the outside: with varying degrees of post-training, it is unclear whether Wonder is a Fable and Astra-class model or something a rung below it. Meta also declined to state a parameter count, which is its right and also the reason the argument cannot be closed by inspection.
A quota bug drained Muse Code on day one
Launch days are a stress test, and this one produced a small one. Muse Code quotas drained abnormally fast, which Alexandr Wang said the team fixed the same day, with a quota reset for every user. He added that there are "definitely still issues we're trying to iron out" — the kind of sentence that reads as candour and as a warning at the same time.
Safety is thinner. No detailed system card was published with the release, though Meta says the family "offers our strongest safeguards to date" against cyber misuse and that those protections ship with the paid model for now. The company's own post promises a full report with additional safety evaluations "soon." That is a notable posture for a lab that has spent the year arguing its agents should have broad authority over a user's devices, and it is a fair contrast with the household-authority prompt it gives Muse in domestic settings.
What would move Wonder out of the ambiguous tier
Three things, none of them requiring Meta's cooperation. The first is whether Muse Wonder Full reaches the API, which would let outsiders benchmark the tier that scores 56 instead of the tier that scores 52. The second is the promised safety report and any system card attached to it, since "strongest safeguards to date" is currently an unquantified claim. The third is Arena results, which are pending for both models.
Until then the honest reading is narrow: Meta has a model that third parties place close to the frontier on agentic and coding work, priced like a frontier model on the tier anyone can rent, with the better tier held back as a subscription feature. Reuters reported that Meta shares rose about 4% in afternoon trading on the day. Markets can price a launch without resolving what it is.
