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 | Aura-2aura-2 | MAI-Transcribe 2mai-transcribe-2 | GPT-6 Astragpt-6-astra | Muse Spark 1.3muse-spark-1.3 |
|---|---|---|---|---|
| Pricing | ||||
| Input | $0 / 1M | $0 / 1M | $10.00 / 1M | $1.25 / 1M |
| Output | $0 / 1M | $0 / 1M | $50.00 / 1M | $4.25 / 1M |
| Cache Write (5m) | Not applicable | Not applicable | $10.00 / 1M | $1.25 / 1M |
| Cache Write (1h) | Not applicable | Not applicable | $10.00 / 1M | $1.25 / 1M |
| Cache Read | Not applicable | Not applicable | $10.00 / 1M | $1.25 / 1M |
| Web Search | $0 / 1M | $0 / 1M | $0 / 1M | $0 / 1M |
| Context | ||||
| Max context | N/A | N/A | 1M | 1M |
| Max output | N/A | N/A | N/A | N/A |
| Capabilities | ||||
| Vision | No | No | Yes | Yes |
| Function Calling | No | No | Yes | Yes |
| JSON Mode | No | No | Yes | Yes |
| Streaming | No | No | Yes | Yes |
| Catalogue | ||||
| Provider | Deepgram | Microsoft | OpenAI | Meta |
| Category | voice | voice | chat | chat |
| Charge type | Pay As You Go | Pay As You Go | Pay As You Go | Pay As You Go |
| Released | 2026-07-16 | 2026-09-03 | — | — |
| Description | ||||
| Summary | Deepgram Aura-2 text-to-speech for real-time voice applications. Billed per input character. | Microsoft MAI-Transcribe-2 speech-to-text. 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 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. |