Muse Wonder
Neural Network
Muse Wonder is a Meta reasoning model for complex agent tasks: accepts text, images, video, audio, and PDF, responds with text.
Specifications
- Input
- text, images, documents
- Output
- text
- Context size
- 1 048 576 tokens
- Max answer length
- 943 718 tokens
- Reasoning effort
- minimal, low, medium, high, extra high, max
- Capabilities
- reasoning, function calling
Prompt cost
(per 1M tokens)
Answer cost
(per 1M tokens)
How it works Muse Wonder?
Muse Wonder is a Meta reasoning model for complex agent tasks: it breaks down requests into steps, maintains reasoning logic, and completes multi-stage work. It accepts text, images, video, audio, and PDF documents, responding with text, allowing you to assign scans, meeting recordings, or videos to a single model. BotHub provides access without VPN or foreign cards: pay with Russian cards in rubles only for used tokens that do not expire. Compare answers from over 250 neural networks in one window. A unified OpenAI-compatible API allows integrating the model into your services and swapping it without rewriting integrations. Data is encrypted via AES-GCM, and companies have access to contracts, invoices, EDI, and an admin panel with limits. Analysts can use it for PDF reports and presentations, developers for multi-step agent scenarios, managers for transcribing calls and summarizing decisions, marketers for analyzing visual materials, and researchers for working with various source formats.Frequently asked questions about Muse Wonder
You can use generated results for commercial purposes. You own all rights to the content you create. The only restriction: make sure your prompt does not include copyrighted third-party material. You are responsible for respecting the rights to any input data.
This is a text model from Meta with a context of 1,048,576 tokens. It is suitable for working with long documents and large codebases: analysis, refactoring, debugging, analytics. It can reason before answering, accepts images and documents as input, and calls external functions.
The output limit is 943,718 tokens per response, which is the volume of an entire book or a large code module without splitting. This eliminates the need to stitch answers from dozens of requests when generating documentation, reports, or large files.
Yes, the reasoning mode is confirmed. The model first builds a solution path and then provides the result — this helps with STEM tasks, mathematics, parsing complex logic in code, and situations where a chain of reasoning is more important than a quick response.
Yes, both function calling and structured JSON output are supported. The model can be embedded into agent scenarios and connected to external services and databases. A unified OpenAI-compatible API is available on BotHub, so changing models won't require rewriting integrations.
According to our data, the model accepts images and documents as input, including native file processing. You can send an interface screenshot, diagram, chart, PDF, or table and get a text analysis. The model itself generates text.
We do not have data on the model's performance with specific languages, so we cannot provide exact estimates — please test it with your task. The BotHub interface and payment with ruble cards are available from Russia without VPN or foreign cards; you only pay for used tokens.