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 | GPT-4o Audio Previewgpt-4o-audio-preview | Muse Wondermuse-wonder |
|---|---|---|
| Pricing | ||
| Input | $0.875 / 1M | $4.00 / 1M |
| Output | $3.50 / 1M | $20.00 / 1M |
| Cache Write (5m) | Not applicable | $4.00 / 1M |
| Cache Write (1h) | Not applicable | $4.00 / 1M |
| Cache Read | Not applicable | $4.00 / 1M |
| Web Search | $0 / 1M | $0 / 1M |
| Context | ||
| Max context | 128K | 1M |
| Max output | N/A | N/A |
| Capabilities | ||
| Vision | No | Yes |
| Function Calling | No | Yes |
| JSON Mode | No | Yes |
| Streaming | Yes | Yes |
| Catalogue | ||
| Provider | OpenAI | Meta |
| Category | voice | chat |
| Charge type | Pay As You Go | Pay As You Go |
| Released | — | — |
| Description | ||
| Summary | gpt-4o-audio-preview adds support for audio inputs, allowing the model to understand nuances in audio recordings and enrich responses. It currently does not generate audio outputs, and audio input is billed per million audio tokens. | 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. |