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 35. 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 mathematics, where it averages 85.3 against 33.1. The single biggest benchmark swing on the page is AIME 2025, 34% to 85.3%.
Exaone 4.0 32B is the reasoning model in the pair, while DeepSeek V3.1 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 V3.1 only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.
Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.
Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.
Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.
DeepSeek V3.1
30.3
Exaone 4.0 32B
81.8
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 V3.1
33.1
Exaone 4.0 32B
85.3
Exaone 4.0 32B is ahead overall, 83 to 35. The biggest single separator in this matchup is AIME 2025, where the scores are 34% and 85.3%.
Exaone 4.0 32B has the edge for knowledge tasks in this comparison, averaging 81.8 versus 30.3. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
Exaone 4.0 32B has the edge for math in this comparison, averaging 85.3 versus 33.1. Inside this category, AIME 2025 is the benchmark that creates the most daylight between them.
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