Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
DeepSeek V3 is clearly ahead on the aggregate, 25 to 21. 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 42. The single biggest benchmark swing on the page is MMLU, 88.5% to 43%. Qwen2.5-VL-32B 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 Qwen2.5-VL-32B.
Pick DeepSeek V3 if you want the stronger benchmark profile. Qwen2.5-VL-32B only becomes the better choice if reasoning is the priority.
Benchmark data for this category is coming soon.
DeepSeek V3
42
Qwen2.5-VL-32B
35
Benchmark data for this category is coming soon.
DeepSeek V3
24.9
Qwen2.5-VL-32B
39.9
DeepSeek V3
69.6
Qwen2.5-VL-32B
41
Benchmark data for this category is coming soon.
Benchmark data for this category is coming soon.
DeepSeek V3
90.2
Qwen2.5-VL-32B
42
DeepSeek V3 is ahead overall, 25 to 21. The biggest single separator in this matchup is MMLU, where the scores are 88.5% and 43%.
DeepSeek V3 has the edge for knowledge tasks in this comparison, averaging 69.6 versus 41. 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 35. Qwen2.5-VL-32B 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 42. Inside this category, AIME 2024 is the benchmark that creates the most daylight between them.
Qwen2.5-VL-32B has the edge for reasoning in this comparison, averaging 39.9 versus 24.9. Inside this category, SimpleQA is the benchmark that creates the most daylight between them.
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