DeepSeek R1 vs Mixtral 8x7B

On shared benchmark coverage, Google-Proof Q&A has DeepSeek R1 at 71.5 and Mixtral 8x7B at 54.8, with DeepSeek R1 ahead by 16.7 points; HumanEval has DeepSeek R1 at 89.9 and Mixtral 8x7B at 80.5, with DeepSeek R1 ahead by 9.4 points. The largest visible gap is 16.7 points on Google-Proof Q&A, 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 reasoning mode: DeepSeek R1, structured outputs: DeepSeek R1, and code execution: DeepSeek R1. Both models share the core language-model surface, 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 Mixtral 8x7B lists $0.15/1M input and $0.45/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts DeepSeek R1 lower by about $0.08 per million blended tokens. Availability is 13 providers versus 18, so concentration risk also matters.

Choose DeepSeek R1 when coding workflow support, larger context windows, and lower input-token cost are central to the workload. Choose Mixtral 8x7B when provider fit 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.