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 63. 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 62. The single biggest benchmark swing on the page is AIME 2025, 85.3% to 93.3%. GLM-5 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 GLM-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. GLM-5 gives you the larger context window at 200K, compared with 128K for Exaone 4.0 32B.
Pick Exaone 4.0 32B if you want the stronger benchmark profile. GLM-5 only becomes the better choice if mathematics is the priority or you need the larger 200K context window.
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.
Benchmark data for this category is coming soon.
Exaone 4.0 32B
81.8
GLM-5
62
Benchmark data for this category is coming soon.
Benchmark data for this category is coming soon.
Exaone 4.0 32B
85.3
GLM-5
94.9
Exaone 4.0 32B is ahead overall, 83 to 63. The biggest single separator in this matchup is AIME 2025, where the scores are 85.3% and 93.3%.
Exaone 4.0 32B has the edge for knowledge tasks in this comparison, averaging 81.8 versus 62. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
GLM-5 has the edge for math in this comparison, averaging 94.9 versus 85.3. Inside this category, AIME 2025 is the benchmark that creates the most daylight between them.
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