Compare models
Put up to 4 models beside each other — token prices, context windows, capabilities and provider, from the same catalogue the model pages read.
| Attribute | Muse Wondermuse-wonder | GPT-4o Transcribegpt-4o-transcribe |
|---|---|---|
| Pricing | ||
| Input | $4.00 / 1M | $1.25 / 1M |
| Output | $20.00 / 1M | — Not priced per output token |
| Cache Write (5m) | $4.00 / 1M | Not applicable |
| Cache Write (1h) | $4.00 / 1M | Not applicable |
| Cache Read | $4.00 / 1M | Not applicable |
| Web Search | $0 / 1M | $0 / 1M |
| Context | ||
| Max context | 1M | 128K |
| Max output | N/A | N/A |
| Capabilities | ||
| Vision | Yes | No |
| Function Calling | Yes | No |
| JSON Mode | Yes | Yes |
| Streaming | Yes | No |
| Catalogue | ||
| Provider | Meta | OpenAI |
| Category | chat | voice |
| Charge type | Pay As You Go | Pay As You Go |
| Released | — | — |
| Description | ||
| Summary | Muse Wonder is Meta’s frontier multimodal reasoning model, designed for complex personal and professional agentic workflows. It supports text, image, video, audio, and PDF inputs with text output and features a 1M-token context window for sustained reasoning across long-running tasks. Optimized for autonomous execution and multi-agent orchestration, Muse Wonder can plan, delegate to subagents, coordinate parallel tool use, recover from errors, and operate browsers and applications across desktop and mobile environments. With cross-conversation context retrieval and its Active Focus training approach, it is well suited for complex software engineering, multi-file refactoring, computer use, and end-to-end workflows requiring persistence, adaptability, and minimal supervision. | GPT-4o Transcribe is OpenAI's high-quality speech-to-text model built on GPT-4o's audio capabilities. It delivers accurate transcription with strong language understanding, making it suitable for a wide range of audio processing tasks. Priced per token (input and output), it offers transparent, fine-grained billing, making it well suited for workflows that require scalable transcription, integration with LLM pipelines, and cost-aware processing. |