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 65. 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 64. The single biggest benchmark swing on the page is AIME 2025, 85.3% to 49.5%.
Exaone 4.0 32B is the reasoning model in the pair, while Kimi K2 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. Kimi K2 only becomes the better choice if 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.
Benchmark data for this category is coming soon.
Exaone 4.0 32B
81.8
Kimi K2
64
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.
Exaone 4.0 32B
85.3
Kimi K2
67.9
Exaone 4.0 32B is ahead overall, 83 to 65. The biggest single separator in this matchup is AIME 2025, where the scores are 85.3% and 49.5%.
Exaone 4.0 32B has the edge for knowledge tasks in this comparison, averaging 81.8 versus 64. 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 67.9. Inside this category, AIME 2025 is the benchmark that creates the most daylight between them.
Get notified when new models drop, benchmark scores change, or the leaderboard shifts. One email per week.
Free. No spam. Unsubscribe anytime. We only store derived location metadata for consent routing.