DeepSeek R1 vs Mistral Large 2

On shared benchmark coverage, HumanEval has DeepSeek R1 at 89.9 and Mistral Large 2 at 84.8, with DeepSeek R1 ahead by 5.1 points; Chatbot Arena has DeepSeek R1 at 1372 and Mistral Large 2 at 1265, with DeepSeek R1 ahead by 107 points. The largest visible gap is 107 points on Chatbot Arena, 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, multimodal input: Mistral Large 2, reasoning mode: DeepSeek R1, function calling: Mistral Large 2, tool use: Mistral Large 2, and code execution: DeepSeek R1. Both models share 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, DeepSeek R1 lists $0.1/1M input and $0.3/1M 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 DeepSeek R1 lower by about $0.9 per million blended tokens. Availability is 13 providers versus 4, so concentration risk also matters.

Choose DeepSeek R1 when coding workflow support, lower input-token cost, and broader provider choice are central to the workload. Choose Mistral Large 2 when vision-heavy evaluation are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.