DeepSeek R1 0528 vs Mixtral 8x22B v0.1

On shared benchmark coverage, Google-Proof Q&A has DeepSeek R1 0528 at 81 and Mixtral 8x22B v0.1 at 60.1, with DeepSeek R1 0528 ahead by 20.9 points. The largest visible gap is 20.9 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 0528, structured outputs: DeepSeek R1 0528, and code execution: DeepSeek R1 0528. 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 0528 lists $0.1/1M input and $0.3/1M output tokens, while Mixtral 8x22B v0.1 lists $0.3/1M input and $0.9/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts DeepSeek R1 0528 lower by about $0.32 per million blended tokens. Availability is 5 providers versus 8, so concentration risk also matters.

Choose DeepSeek R1 0528 when coding workflow support, larger context windows, and lower input-token cost are central to the workload. Choose Mixtral 8x22B v0.1 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.