Meta has released two new artificial intelligence models, Muse Wonder and Muse Wonder Full, marking its first attempt to compete directly with leading frontier models from OpenAI and Anthropic as the company intensifies its push into more capable AI systems and personal assistants.
The models, unveiled on Monday by Meta’s Superintelligence Lab, represent a significant step beyond Muse Spark, the company’s earlier lightweight model designed to deliver responses with low latency. Meta says the new models are larger, achieve higher benchmark scores, and perform better on complex tasks, including coding.
According to testing results presented in Meta’s model card, Muse Wonder and Muse Wonder Full achieve benchmark performance comparable to Fable 5.1 and GPT-6 Astra. The company described the release as part of a broader effort to scale its AI capabilities toward more personalized systems that can understand users and assist them over extended periods.
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“Together, they mark the next step on our scaling ladder and a major leap toward personal superintelligence,” Meta said in its blog post announcing the models.
The launch signals a change in emphasis for Meta’s AI strategy. After introducing Muse Spark and the Muse agent, the company is now moving towards more powerful models designed to compete on capability rather than relying primarily on lightweight systems and fast inference.
The increased scale of the Wonder models is reflected in their pricing. Through Meta’s Model API, Muse Wonder will cost $4 per million input tokens and $20 per million output tokens, placing it at roughly the same price level as GPT-6 Sol and Opus 5.5.
The pricing positions Muse Wonder as a more expensive offering than a model designed mainly for lightweight, low-latency interactions. For developers and businesses, the commercial question will be whether its performance on demanding tasks justifies the additional inference costs.
Muse Wonder Full, meanwhile, will be available exclusively to Muse Code subscribers and will not be offered through the API. Meta has not made the model broadly accessible to developers through the same channel as Wonder, limiting its availability to users within the company’s coding product.
Both models will also power the Muse agent, Meta’s system for carrying out tasks on behalf of users. Integrating the models into that product gives Meta a way to deploy their capabilities in a consumer-facing assistant rather than treating them solely as standalone models for developers.
AI companies are increasingly competing to build systems that can do more than answer individual prompts. More capable models can potentially support longer workflows, complex coding assignments, and tasks requiring information to be retained across multiple interactions. However, stronger benchmark results alone do not establish how reliably a model will perform in everyday use or whether its capabilities will translate into sustainable commercial returns.
Meta Puts Memory At The Centre of Its AI Ambitions
One of the defining features Meta emphasized in the launch is what it calls “active memory”: the ability of a model to retain, retrieve and synthesize information supplied by a user over time.
The company is positioning this capability as an important component of personal AI assistants, which may need to remember preferences, ongoing projects and previous instructions across days, weeks or months. Better memory could reduce the need for users to repeatedly explain their circumstances and allow an assistant to provide more consistent support across extended tasks.
Meta highlighted MemoryBench, a benchmark designed to assess continual learning and memory-construction frameworks, as evidence of progress in this area. According to the company’s own testing, Muse Wonder scored 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,” research scientist Kavya Kopparapu said in a video accompanying the launch.
The emphasis on memory suggests that Meta is pursuing a broader definition of AI performance than conventional measures of reasoning, coding or general knowledge. For a personal assistant, the ability to maintain useful context over time could be as important as the ability to solve a difficult problem in a single interaction.
But that ambition also creates challenges. A system that retains information over long periods must distinguish useful context from irrelevant details, retrieve the right information when needed, and handle personal data appropriately. The launch announcement highlights Meta’s technical focus on memory, but the available benchmark claims do not by themselves establish how the models will perform across all real-world settings.
A New Phase in Meta’s Frontier-Model Push
The release is the first frontier-model launch since Scale AI founder Alexandr Wang restructured Meta’s AI operations in August last year. Since that reorganization, Meta has introduced the lightweight Muse Spark model and the Muse agent but has largely avoided the kind of high-profile, state-of-the-art model releases associated with OpenAI and Anthropic.
Muse Wonder and Muse Wonder Full therefore provide an early indication of how the restructured organization intends to compete at the upper end of the AI market.
Meta’s reported benchmark comparisons place the models in the same performance range as selected offerings from its rivals. However, those comparisons are based on the company’s own testing, and benchmark parity does not necessarily mean that models are interchangeable across coding, reasoning, agentic workflows, or other applications.
The infrastructure behind the launch is also significant. According to Meta, the models were trained at its Prometheus data center in central Ohio, which came online earlier this year. The facility provides part of the computing infrastructure supporting the company’s expanding AI development effort.
The challenge for Meta now extends beyond producing models with competitive benchmark scores. The company must demonstrate that its larger systems can deliver useful performance in products, sustain adoption among developers and users, and support a credible business model as inference costs rise.
The integration of Wonder into the Muse agent offers one route to achieving that goal. If the models can combine strong reasoning and coding capabilities with reliable long-term memory, Meta could strengthen its position in the emerging market for personal AI assistants.
The launch does not settle the contest with OpenAI and Anthropic, but it signals that Meta is moving more directly into frontier-model competition.