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 59. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Exaone 4.0 32B is the reasoning model in the pair, while Kimi K2.5 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 K2.5 only becomes the better choice if knowledge 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.
Benchmark data for this category is coming soon.
Exaone 4.0 32B
81.8
Kimi K2.5
83.6
Benchmark data for this category is coming soon.
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.
Exaone 4.0 32B is ahead overall, 83 to 59. The biggest single separator in this matchup is MMLU-Pro, where the scores are 81.8% and 87.1%.
Kimi K2.5 has the edge for knowledge tasks in this comparison, averaging 83.6 versus 81.8. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
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