DeepSeek V3 vs Mixtral 8x7B

On shared benchmark coverage, HumanEval has DeepSeek V3 at 85.5 and Mixtral 8x7B at 80.5, with DeepSeek V3 ahead by 5 points; Massive Multitask Language Understanding has DeepSeek V3 at 88.5 and Mixtral 8x7B at 80.2, with DeepSeek V3 ahead by 8.3 points; HellaSwag has DeepSeek V3 at 95.7 and Mixtral 8x7B at 90.9, with DeepSeek V3 ahead by 4.8 points. The largest visible gap is 8.3 points on Massive Multitask Language Understanding, 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 function calling: DeepSeek V3, tool use: DeepSeek V3, and structured outputs: DeepSeek V3. 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 V3 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 V3 lower by about $0.08 per million blended tokens. Availability is 12 providers versus 18, so concentration risk also matters.

Choose DeepSeek V3 when long-context analysis, 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.