Grok 4 vs Mistral Large 2

On shared benchmark coverage, MMLU PRO has Grok 4 at 87 and Mistral Large 2 at 69.7, with Grok 4 ahead by 17.3 points. The largest visible gap is 17.3 points on MMLU PRO, 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 vision: Mistral Large 2, reasoning mode: Grok 4, function calling: Mistral Large 2, tool use: Mistral Large 2, and code execution: Grok 4. Both models share multimodal input and structured outputs, 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, Grok 4 lists $3/1M input and $15/1M output tokens, while Mistral Large 2 lists $0.48/1M input and $2.4/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Mistral Large 2 lower by about $5.54 per million blended tokens. Availability is 3 providers versus 4, so concentration risk also matters.

Choose Grok 4 when coding workflow support and larger context windows are central to the workload. Choose Mistral Large 2 when vision-heavy evaluation, lower input-token cost, and broader provider choice are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.