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.
That scale is reflected in the pricing: Accessed through the Meta Model API, Wonder will cost $4.00 per million input tokens and $20.00 per million output tokens, making it roughly equivalent in cost to GPT-6 Sol or Opus 5.5. Notably, Wonder Full will only be available to Muse Code subscribers, and will not be served through the API. Both models will also be used to power the Muse agent.
In keeping with Meta’s interest in developing personal assistants that can perform tasks over a long period, the company placed particular emphasis on the new models’ ability to retain and synthesize information given by the user, a property Meta calls “active memory.” To achieve better recall, Meta highlighted the MemoryBench benchmark, which challenges continual learning and memory construction frameworks. According to the company’s testing, Muse Wonder now scores higher on MemoryBench than Fable, Opus or Astra. The new models are the first frontier model release since Scale AI founder Alexandr Wang restructured Meta’s AI efforts in August of last year.
Since then, Meta has released its lightweight Muse Spark model and Muse agent, but has avoided the kind of state-of-the-art model releases OpenAI and Anthropic are known for. According to the post, the models were trained at Meta’s Prometheus data center in central Ohio, which came online earlier this year.
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