Compare models
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| Attribute | Grok 4.7grok-4.7 | Nova-3nova-3 | S2 Pros2-pro | Muse Spark 1.3muse-spark-1.3 |
|---|---|---|---|---|
| Pricing | ||||
| Input | $1.60 / 1M | $0 / 1M | $0 / 1M | $1.25 / 1M |
| Output | $4.80 / 1M | $0 / 1M | $0 / 1M | $4.25 / 1M |
| Cache Write (5m) | $1.60 / 1M | Not applicable | Not applicable | $1.25 / 1M |
| Cache Write (1h) | $1.60 / 1M | Not applicable | Not applicable | $1.25 / 1M |
| Cache Read | $1.60 / 1M | Not applicable | Not applicable | $1.25 / 1M |
| Web Search | $0 / 1M | $0 / 1M | $0 / 1M | $0 / 1M |
| Context | ||||
| Max context | 500K | 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 | SpaceXAI | 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 | Grok 4.7 is SpaceXAI's flagship model for coding, agentic workflows, and professional knowledge work, succeeding Grok 4.6. It is particularly strong at long-running software engineering, self-verification, and long-context execution, while improving capabilities in document drafting, presentations, and other professional tasks. Trained with extended reinforcement learning focused on multi-hour problems, Grok 4.7 is optimized for sustained, complex task execution and natively supports the Grok Bot harness for conversational workflows. It also introduces an enhanced safeguard stack designed to combine strong jailbreak resistance with low refusal rates for legitimate technical work. Reported benchmark results use xhigh reasoning effort. | 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. |