Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Exaone 4.0 32B is clearly ahead on the aggregate, 83 to 54. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Exaone 4.0 32B's sharpest advantage is in knowledge, where it averages 81.8 against 57.5. The single biggest benchmark swing on the page is MMLU-Pro, 75.9% to 81.8%. DeepSeek V3 does hit back in mathematics, so the answer changes if that is the part of the workload you care about most.
Exaone 4.0 32B is the reasoning model in the pair, while DeepSeek V3 is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use.
Pick Exaone 4.0 32B if you want the stronger benchmark profile. DeepSeek V3 only becomes the better choice if mathematics is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Benchmark data for this category is coming soon.
Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.
Benchmark data for this category is coming soon.
Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.
DeepSeek V3
57.5
Exaone 4.0 32B
81.8
Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.
Benchmark data for this category is coming soon.
DeepSeek V3
90.2
Exaone 4.0 32B
85.3
Exaone 4.0 32B is ahead overall, 83 to 54. The biggest single separator in this matchup is MMLU-Pro, where the scores are 75.9% and 81.8%.
Exaone 4.0 32B has the edge for knowledge tasks in this comparison, averaging 81.8 versus 57.5. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
DeepSeek V3 has the edge for math in this comparison, averaging 90.2 versus 85.3. Exaone 4.0 32B stays close enough that the answer can still flip depending on your workload.
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