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Meta announced Muse Wonder and Muse Wonder Full on October 12, 2026. At launch, developers could try Muse Wonder through Meta Model API or Muse Code; Muse Wonder Full was available to Muse Code subscribers on the Power Usage and Maximum Usage plans. Meta said the models would soon power its Muse experience, beginning with subscribers, but gave no rollout date or country list. Here’s how the available routes differ and what Meta says the models can do.
How to access Muse Wonder and Muse Wonder Full
The two models do not have identical access routes. Muse Wonder is offered through both a metered API and Muse Code. Muse Wonder Full is listed for selected Muse Code subscription plans. Meta also said Wonder would come to its Muse consumer experience soon, but the launch announcement did not specify when or where.
| Route | Model and access | Published price or terms |
|---|---|---|
| Meta Model API | Muse Wonder; intended for developers integrating the model into applications and tools. The model identifier listed by Meta is muse-wonder-1.0. |
Meta’s October 2026 launch post lists $4 per million input tokens and $20 per million output tokens. Meta’s model page lists cached input at $0.20 per million tokens. Prices can change; confirm on the live pages. |
| Muse Code | Muse Wonder, in Meta’s coding-agent environment. | Access is through Muse Code; the launch information does not establish a separate per-token price for using Wonder in this environment. |
| Muse Code, Power Usage or Maximum Usage | Muse Wonder Full. Meta listed it for subscribers on these two plans. | Meta’s 2026 model page listed Power Usage at $50/month and Maximum Usage at $200/month. These date-sensitive subscription prices should be checked on the live page. |
| Muse consumer experience | Meta said Wonder would soon power Muse, rolling out first to subscribers. | No launch date, country list, or detailed eligibility terms were stated in the reviewed announcement. |
Meta lists a one-million-token context window for both models. Context capacity is not the same as a guarantee that every detail in a long input will be used correctly.
Try Muse Wonder through the API
Meta’s developer post says developers can use an OpenAI-compatible client by configuring the API base URL and key. If you already have a project built around that SDK, the integration may require changing those settings and using the model identifier muse-wonder-1.0. Follow Meta’s current setup instructions for the precise endpoint, credentials, and request format: Meta’s API launch guide and Muse Wonder model page.
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API billing is usage-based, so the total depends on input and output token volume. Before sending production traffic, check the live pricing and limits; a long prompt or large generated response can cost more than a short test.
Try Wonder in Muse Code, or Wonder Full on an eligible plan
Meta lists Muse Code as a way to use Wonder in a coding-agent workflow. The launch announcement restricted Wonder Full to Muse Code subscribers on Power Usage and Maximum Usage plans. See Meta’s launch post and Muse Code information for current availability and plan terms. Do not assume Wonder Full is necessary for an initial test: Wonder has its own API and Muse Code access routes.
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Check other integration options
Meta’s developer model page also listed OpenRouter as an option for using Wonder in existing tools. Confirm that the model is currently listed there and check its terms, pricing, and regional availability before relying on that route.
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Meta positions the Wonder family as agentic models for multi-step personal and professional tasks. Its descriptions include interpreting documents, navigating an insurance claim, understanding a lease, working with financial or health information, and coding projects such as an application or game. Those examples describe intended capabilities, not a guarantee that the model will reach a correct result in every case.
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Memory, sustained work, and tool use
Meta calls the models’ ability to carry relevant context across conversations “Active Memory.” It describes “Active Focus” as continuing toward a goal when a task gets difficult, trying different approaches, recovering from errors, asking for clarification, and checking before important actions. Meta’s developer materials also describe sequential tool use and long-horizon task completion. How well these behaviors work depends on the task, the available tools, and the surrounding product.
Coding and computer interaction
Meta describes use cases that include coding across large repositories and computer use. Its developer post also lists interpreting screenshots, charts, documents, images, and video. Audio-dependent workflows require confirmation that audio is supported on the specific release surface; the general launch material does not establish that support everywhere.
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How to assess performance claims
Meta says the models performed strongly across its evaluation suite, and its developer announcement reports third-party runs on selected coding benchmarks. These are company-reported results, not a universal ranking or an independent guarantee for your workload. To judge whether a model is useful, test representative tasks and compare the quality and completeness of results, reliability across repeated attempts, latency, and cost. A benchmark is most informative when its task and conditions resemble the work you need done.
Choosing the right way to try them
- Choose the API if you are building an integration and want metered usage. Estimate costs from the input and output your application is likely to generate, then test with representative requests.
- Choose Muse Code if you want to explore Wonder inside Meta’s coding-agent workflow rather than wiring an API into your own software.
- Consider Wonder Full if you specifically need that model and have access to one of the Muse Code plans Meta listed for it. The launch materials do not establish that it is required for ordinary evaluation.
- Check the consumer app separately if you want to use Muse rather than a developer or coding workflow. Meta announced a forthcoming subscriber-first rollout, not current availability on a particular date or in a particular country.
- Compare results on your own tasks rather than relying only on broad capability descriptions or benchmark headlines.
Privacy, safety, and consequential decisions
Meta says the models were trained to avoid sharing private information without permission, refuse harmful requests, and support defensive cybersecurity work. Meta also described safeguards around checking before important actions. These are the company’s stated safeguards; the reviewed launch material said a fuller report with additional safety evaluations would be available soon, so it does not establish that such a report was already available or independently assessed.
Before connecting a model to messages, calendars, documents, or other sensitive information, review the current privacy terms for the product you are using and understand what data it handles. For legal, financial, or health matters, use a model to organize or explain information, not as a substitute for a qualified professional making a consequential decision.
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