Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V3
36
Exaone 4.0 32B
65
Pick Exaone 4.0 32B if you want the stronger benchmark profile. DeepSeek V3 only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Knowledge
+11.8 difference
DeepSeek V3
Exaone 4.0 32B
$0.27 / $1.1
N/A
N/A
N/A
N/A
N/A
128K
128K
Pick Exaone 4.0 32B if you want the stronger benchmark profile. DeepSeek V3 only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Exaone 4.0 32B is clearly ahead on the provisional aggregate, 65 to 36. 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 70. The single biggest benchmark swing on the page is MMLU-Pro, 75.9% to 81.8%.
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.
Exaone 4.0 32B is ahead on BenchLM's provisional leaderboard, 65 to 36. 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 70. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
For engineers, researchers, and the plain curious — a weekly brief on new models, ranking shifts, and pricing changes.
Free. No spam. Unsubscribe anytime.