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.
4 is the maximum. Remove one to add another.
| Attribute | GPT-6 Astragpt-6-astra | Nova-3nova-3 | S2 Pros2-pro | Muse Spark 1.3muse-spark-1.3 |
|---|---|---|---|---|
| Pricing | ||||
| Input | $10.00 / 1M | $0 / 1M | $0 / 1M | $1.25 / 1M |
| Output | $50.00 / 1M | $0 / 1M | $0 / 1M | $4.25 / 1M |
| Cache Write (5m) | $10.00 / 1M | Not applicable | Not applicable | $1.25 / 1M |
| Cache Write (1h) | $10.00 / 1M | Not applicable | Not applicable | $1.25 / 1M |
| Cache Read | $10.00 / 1M | Not applicable | Not applicable | $1.25 / 1M |
| Web Search | $0 / 1M | $0 / 1M | $0 / 1M | $0 / 1M |
| Context | ||||
| Max context | 1M | N/A | N/A | 1M |
| Max output | N/A | N/A | N/A | N/A |
| Capabilities | ||||
| Vision | Yes | No | No | Yes |
| Function Calling | Yes | No | No | Yes |
| JSON Mode | Yes | No | No | Yes |
| Streaming | Yes | No | No | Yes |
| Catalogue | ||||
| Provider | OpenAI | Deepgram | Fish Audio | Meta |
| Category | chat | voice | voice | chat |
| Charge type | Pay As You Go | Pay As You Go | Pay As You Go | Pay As You Go |
| Released | — | 2026-07-15 | 2026-07-29 | — |
| 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. | Deepgram Nova-3 speech-to-text. Billed per second of audio. | Fish Audio S2 Pro text-to-speech. Billed per UTF-8 byte of input text. | Muse Spark 1.3 is Meta's multimodal reasoning model designed for long-running agentic, multi-agent, and coding workflows. It maintains context and information across extended tasks, enabling reliable execution in complex, multi-step environments. The model is optimized to resolve conflicting information, seek clarification or confirmation when necessary, and execute concisely, making it well suited for autonomous agents, collaborative multi-agent systems, and long-horizon software engineering workflows. |