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 39. 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 58.1. The single biggest benchmark swing on the page is MMLU-Pro, 81.8% to 64%.
Exaone 4.0 32B is the reasoning model in the pair, while Gemini 2.5 Flash 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. Gemini 2.5 Flash gives you the larger context window at 1M, compared with 128K for Exaone 4.0 32B.
Pick Exaone 4.0 32B if you want the stronger benchmark profile. Gemini 2.5 Flash only becomes the better choice if you need the larger 1M context window or you would rather avoid the extra latency and token burn of a reasoning model.
Benchmark data for this category is coming soon.
Benchmark data for this category is coming soon.
Benchmark data for this category is coming soon.
Benchmark data for this category is coming soon.
Exaone 4.0 32B
81.8
Gemini 2.5 Flash
58.1
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 39. The biggest single separator in this matchup is MMLU-Pro, where the scores are 81.8% and 64%.
Exaone 4.0 32B has the edge for knowledge tasks in this comparison, averaging 81.8 versus 58.1. Inside this category, MMLU-Pro 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.