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.
- Transcribe 1 ProFish AudioRemove
- GPT-6 AstraOpenAIRemove
- Muse WonderMetaRemove
- mai-ds-r1:freeNot in the catalogueRemove
4 is the maximum. Remove one to add another.
| Attribute | Transcribe 1 Protranscribe-1-pro | GPT-6 Astragpt-6-astra | Muse Wondermuse-wonder | mai-ds-r1:freeNot in the catalogue |
|---|---|---|---|---|
| Pricing | ||||
| Input | — Not priced per input token | $10.00 / 1M | $4.00 / 1M | — |
| Output | — Not priced per output token | $50.00 / 1M | $20.00 / 1M | — |
| Cache Write (5m) | Not applicable | $10.00 / 1M | $4.00 / 1M | — |
| Cache Write (1h) | Not applicable | $10.00 / 1M | $4.00 / 1M | — |
| Cache Read | Not applicable | $10.00 / 1M | $4.00 / 1M | — |
| Web Search | $0 / 1M | $0 / 1M | $0 / 1M | — |
| Context | ||||
| Max context | N/A | 1M | 1M | — |
| Max output | N/A | N/A | N/A | — |
| Capabilities | ||||
| Vision | No | Yes | Yes | — |
| Function Calling | No | Yes | Yes | — |
| JSON Mode | No | Yes | Yes | — |
| Streaming | No | Yes | Yes | — |
| Catalogue | ||||
| Provider | Fish Audio | OpenAI | Meta | — |
| Category | voice | chat | chat | — |
| Charge type | Pay As You Go | Pay As You Go | Pay As You Go | — |
| Released | 2026-09-24 | — | — | — |
| Description | ||||
| Summary | Fish Audio Transcribe 1 Pro speech-to-text with speaker labels; transcripts include speaker tags such as <|speaker:0|>. Billed per second of audio. | GPT-6 Astra is OpenAI's flagship model for demanding end-to-end professional work, designed for advanced analysis, software engineering, deep research, scientific tasks, and document creation. It is particularly strong in long-horizon agentic workflows, including tasks that require sustained reasoning, tool orchestration, and computer and browser use, making it well suited for complex autonomous workflows and production-grade knowledge work. | 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. | — |