Meta’s Superintelligence Lab released a pair of new state-of-the-art models on Monday, Muse Wonder and Muse Wonder Full, representing the company’s first attempt at a frontier model.
Where Muse Spark aimed to be a lightweight model that could perform at low latency, the Wonder models are larger and said to be more capable, with higher benchmark scores and better results on complex tasks like coding.
According to Meta’s testing (presented in the model card), Wonder and Wonder Full achieve comparable benchmark scores to Fable 5.1 and GPT-6 Astra.
Accessed through the Meta Model API, Wonder will cost 20 per million output tokens, roughly equivalent to GPT-6 Sol or Opus 5.5.
Wonder Full will only be available to Muse Code subscribers and will not be served through the API. Both models will power the Muse agent.
Meta emphasized “active memory” and MemoryBench scores for long-horizon recall. Research scientist Kavya Kopparapu: “We put a tremendous amount of effort into making sure your model can remember what’s important to you over days, weeks, months, and beyond.”
The release follows Alexandr Wang’s restructuring of Meta AI efforts; models were trained at Meta’s Prometheus data center in central Ohio.