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 | Grok 4 Fastgrok-4-fast | GPT-6 Astragpt-6-astra | Muse Spark 1.3muse-spark-1.3 |
|---|---|---|---|
| Pricing | |||
| Input | $0.07 / 1M | $10.00 / 1M | $1.25 / 1M |
| Output | $0.175 / 1M | $50.00 / 1M | $4.25 / 1M |
| Cache Write (5m) | $0.07 / 1M | $10.00 / 1M | $1.25 / 1M |
| Cache Write (1h) | $0.07 / 1M | $10.00 / 1M | $1.25 / 1M |
| Cache Read | $0.07 / 1M | $10.00 / 1M | $1.25 / 1M |
| Web Search | $0 / 1M | $0 / 1M | $0 / 1M |
| Context | |||
| Max context | 2M | 1M | 1M |
| Max output | N/A | N/A | N/A |
| Capabilities | |||
| Vision | No | Yes | Yes |
| Function Calling | Yes | Yes | Yes |
| JSON Mode | Yes | Yes | Yes |
| Streaming | Yes | Yes | Yes |
| Catalogue | |||
| Provider | xAI | OpenAI | Meta |
| Category | chat | chat | chat |
| Charge type | Pay As You Go | Pay As You Go | Pay As You Go |
| Released | — | — | — |
| Description | |||
| Summary | Grok 4 Fast is xAI's cost-efficient multimodal model with a massive 2M-token context window. It’s available in both reasoning and non-reasoning modes, allowing developers to toggle deeper thinking when needed. Designed for scalable performance, it balances speed, capability, and price — with reasoning controllable via the reasoning_enabled API parameter. | 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. |