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 56. 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 70.1. The single biggest benchmark swing on the page is MMLU-Pro, 81.8% to 77.2%. Mistral Medium 3 does hit back in mathematics, so the answer changes if that is the part of the workload you care about most.
Exaone 4.0 32B is the reasoning model in the pair, while Mistral Medium 3 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. Mistral Medium 3 only becomes the better choice if mathematics 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
Mistral Medium 3
70.1
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
Mistral Medium 3
91
Exaone 4.0 32B is ahead overall, 83 to 56. The biggest single separator in this matchup is MMLU-Pro, where the scores are 81.8% and 77.2%.
Exaone 4.0 32B has the edge for knowledge tasks in this comparison, averaging 81.8 versus 70.1. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
Mistral Medium 3 has the edge for math in this comparison, averaging 91 versus 85.3. Exaone 4.0 32B stays close enough that the answer can still flip depending on your workload.
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