Claude 3.7 Sonnet vs Grok-3

On shared benchmark coverage, MMLU PRO has Claude 3.7 Sonnet at 80.3 and Grok-3 at 79.9, with Claude 3.7 Sonnet ahead by 0.4 points; Aider Polyglot has Claude 3.7 Sonnet at 64.9 and Grok-3 at 53.3, with Claude 3.7 Sonnet ahead by 11.6 points; Massive Multi-discipline Multimodal Understanding has Claude 3.7 Sonnet at 75 and Grok-3 at 78, with Grok-3 ahead by 3 points. The largest visible gap is 11.6 points on Aider Polyglot, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.

The capability footprint differs most on code execution: Claude 3.7 Sonnet. Both models share vision, multimodal input, reasoning mode, and function calling, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.

For cost, Claude 3.7 Sonnet lists $3/1M input and $15/1M output tokens, while Grok-3 lists $0.8/1M input and $2.4/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Grok-3 lower by about $5.32 per million blended tokens. Availability is 6 providers versus 3, so concentration risk also matters.

Choose Claude 3.7 Sonnet when coding workflow support and broader provider choice are central to the workload. Choose Grok-3 when long-context analysis, larger context windows, and lower input-token cost are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.