Meta’s Muse Wonder and Muse Wonder Full are designed for agent-style work such as coding and working with documents, with features Meta calls Active Memory and Active Focus. The practical difference is access: Muse Wonder is available through Meta’s API and Muse Code, while Muse Wonder Full is offered to eligible Muse Code subscribers. Meta has announced API prices for Muse Wonder, but not separate token pricing for Muse Wonder Full.
What are Muse Wonder and Muse Wonder Full?
Meta announced the two models on October 12, 2026, describing them as systems for personal and professional agentic work. Rather than only answering a prompt, an agent can be designed to pursue a task through multiple steps, use tools, and continue after an error. Meta’s examples include navigating an insurance claim, understanding a lease, handling personal-finance information, and building software. These are company-described use cases, not independently verified outcomes. Meta’s announcement and its Muse Wonder overview describe the models and their intended behavior.
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Active Memory and Active Focus
Meta calls the models’ two distinguishing experience features Active Memory and Active Focus. Active Memory is intended to carry useful context across conversations. Active Focus is intended to keep working toward a goal, recover from errors, try alternatives, and ask for clarification when needed. These descriptions indicate design goals; they do not guarantee that a model will recall every relevant detail or complete a task without supervision.
How creators and developers can access them
| Model | Access route | Billing information | Context window |
|---|---|---|---|
| Muse Wonder | Meta Model API and Muse Code | API token rates published; Muse Code access also available | 1 million tokens, according to Meta’s model listing |
| Muse Wonder Full | Muse Code subscribers on Power Usage and Maximum Usage plans, according to Meta’s developer post | Subscription access; separate API token pricing is not stated in the retrieved Meta materials | 1 million tokens, according to Meta’s model listing |
Meta says Muse Wonder will later power Muse experiences, with rollout starting for subscribers. That is a future integration rather than a claim that it is already available in every Muse experience. The access details are in Meta’s developer announcement and the Muse Wonder model listing.
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What Muse Wonder API costs
Meta announced Muse Wonder API rates of $4 per million input tokens and $20 per million output tokens on October 12, 2026. The model listing, accessed October 13, also gives a cached-input rate of $0.20 per million tokens. These are usage-based rates, not a fixed price for a project or a typical creator workflow; actual spend depends on the amount and type of tokens processed. Meta’s retrieved materials do not give a representative workflow cost.
The cited listing gives a 1-million-token context window for the Muse Wonder models. Context capacity is not the same as a guarantee that every long document or extended task will be handled accurately: creators and developers still need to validate outputs and account for how their application supplies context.
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What the models could mean for creator and developer workflows
Creators working with documents and personal context
Meta’s examples suggest potential uses such as interpreting a lease or organizing information related to a claim. For creators, an agent that can work across documents and retain relevant context could reduce repeated setup in a multi-step task. But users should check important interpretations and be cautious with sensitive material, particularly when connecting messages, calendars, or documents to an agent.
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Developers building agents and coding workflows
Meta describes Muse Wonder as supporting multi-step reasoning, long-running agents, and advanced computer use. Its developer materials also describe tool invocation, failure recovery, and coding work in agent scaffolds. Those are capabilities Meta says the models are designed to support, not independent test results. Developers evaluating them should test their own tools, permissions, recovery paths, and human-review requirements against representative tasks.
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What Meta says about safety—and what remains unestablished
Meta says it trains the models to avoid sharing private information without permission, turn down requests to assist with harmful acts, and improve security and cyber defenses. It also says the models are developed under its Superintelligence Scaling Framework. These are descriptions of Meta’s training goals and approach, not independently verified safety outcomes. Meta’s announcement says a fuller report with additional safety evaluations will be available soon, but does not give a publication date.
Meta characterizes the models as frontier or state-of-the-art and refers to benchmark performance. The available official materials do not provide enough independent evidence or methodology to establish a comparative ranking, creator productivity gain, developer outcome, adoption level, or user-satisfaction result. Treat such superlatives as Meta’s claims rather than settled comparisons.
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