Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
DeepSeek V3 is clearly ahead on the aggregate, 25 to 13. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
DeepSeek V3's sharpest advantage is in mathematics, where it averages 90.2 against 28. The single biggest benchmark swing on the page is MMLU, 88.5% to 29%. Mistral 8x7B v0.2 does hit back in reasoning, so the answer changes if that is the part of the workload you care about most.
DeepSeek V3 gives you the larger context window at 128K, compared with 32K for Mistral 8x7B v0.2.
Pick DeepSeek V3 if you want the stronger benchmark profile. Mistral 8x7B v0.2 only becomes the better choice if reasoning is the priority.
Benchmark data for this category is coming soon.
DeepSeek V3
42
Mistral 8x7B v0.2
21
Benchmark data for this category is coming soon.
DeepSeek V3
24.9
Mistral 8x7B v0.2
25.9
DeepSeek V3
69.6
Mistral 8x7B v0.2
27
Benchmark data for this category is coming soon.
Benchmark data for this category is coming soon.
DeepSeek V3
90.2
Mistral 8x7B v0.2
28
DeepSeek V3 is ahead overall, 25 to 13. The biggest single separator in this matchup is MMLU, where the scores are 88.5% and 29%.
DeepSeek V3 has the edge for knowledge tasks in this comparison, averaging 69.6 versus 27. Inside this category, MMLU is the benchmark that creates the most daylight between them.
DeepSeek V3 has the edge for coding in this comparison, averaging 42 versus 21. Mistral 8x7B v0.2 stays close enough that the answer can still flip depending on your workload.
DeepSeek V3 has the edge for math in this comparison, averaging 90.2 versus 28. Inside this category, AIME 2024 is the benchmark that creates the most daylight between them.
Mistral 8x7B v0.2 has the edge for reasoning in this comparison, averaging 25.9 versus 24.9. Inside this category, SimpleQA is the benchmark that creates the most daylight between them.
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