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.
- GPT-6 AstraOpenAIRemove
- mai-ds-r1:freeNot in the catalogueRemove
- Gemini 3.5 TranscribeGoogleRemove
- Muse WonderMetaRemove
4 is the maximum. Remove one to add another.
| Attribute | GPT-6 Astragpt-6-astra | mai-ds-r1:freeNot in the catalogue | Gemini 3.5 Transcribegemini-3.5-transcribe | Muse Wondermuse-wonder |
|---|---|---|---|---|
| Pricing | ||||
| Input | $10.00 / 1M | — | — Not priced per input token | $4.00 / 1M |
| Output | $50.00 / 1M | — | — Not priced per output token | $20.00 / 1M |
| Cache Write (5m) | $10.00 / 1M | — | Not applicable | $4.00 / 1M |
| Cache Write (1h) | $10.00 / 1M | — | Not applicable | $4.00 / 1M |
| Cache Read | $10.00 / 1M | — | Not applicable | $4.00 / 1M |
| Web Search | $0 / 1M | — | $0 / 1M | $0 / 1M |
| Context | ||||
| Max context | 1M | — | 98.3K | 1M |
| Max output | N/A | — | N/A | N/A |
| Capabilities | ||||
| Vision | Yes | — | Yes | Yes |
| Function Calling | Yes | — | Yes | Yes |
| JSON Mode | Yes | — | No | Yes |
| Streaming | Yes | — | No | Yes |
| Catalogue | ||||
| Provider | OpenAI | — | Meta | |
| Category | chat | — | voice | chat |
| Charge type | Pay As You Go | — | Pay As You Go | Pay As You Go |
| Released | — | — | 2026-09-25 | — |
| Description | ||||
| Summary | 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. | — | Google Gemini 3.5 Transcribe speech-to-text. Billed per input and output token. | 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. |