Meta releases its first frontier model, Muse Wonder
Meta released frontier models Muse Wonder and Muse Wonder Full, claiming parity with Fable 5.1 and GPT-6 Astra.
Meta's Superintelligence Lab released Muse Wonder and Muse Wonder Full, its first frontier models, trained at the Prometheus data center in Ohio. Company testing claims scores comparable to Fable 5.1 and GPT-6 Astra, with higher MemoryBench results than Fable, Opus, or Astra. Through the Meta Model API, Wonder costs $4 per million input tokens and $20 per million output tokens; Wonder Full is limited to Muse Code subscribers. Both models will power the Muse agent and emphasize long-term "active memory."
- Muse Wonder and Wonder Full are Meta's first frontier models.
- Company tests claim parity with Fable 5.1 and GPT-6 Astra.
- API pricing is $4 input and $20 output per million tokens.
- Wonder Full is Muse Code only and not offered via the API.
- Models emphasize active memory and lead MemoryBench versus rivals.
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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.
“Together, they mark the next step on our scaling ladder and a major leap toward personal superintelligence,” Meta’s blog post about the models reads.
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.
“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,” said research scientist Kavya Kopparapu in a video accompanying the launch.
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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Russell Brandom has been covering the tech industry since 2012, with a focus on platform policy and emerging technologies. He previously worked at The Verge and Rest of World, and has written for Wired, The Awl and MIT’s Technology Review. He can be reached at [email protected] or on Signal at 412-401-5489.