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 55. 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 60.8. The single biggest benchmark swing on the page is MMLU-Pro, 81.8% to 68.8%.
Exaone 4.0 32B is the reasoning model in the pair, while Qwen2.5-VL-32B 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 gives you the larger context window at 128K, compared with 32K for Qwen2.5-VL-32B.
Pick Exaone 4.0 32B if you want the stronger benchmark profile. Qwen2.5-VL-32B 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.
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
81.8
Qwen2.5-VL-32B
60.8
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 55. The biggest single separator in this matchup is MMLU-Pro, where the scores are 81.8% and 68.8%.
Exaone 4.0 32B has the edge for knowledge tasks in this comparison, averaging 81.8 versus 60.8. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
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