Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
K-Exaone is clearly ahead on the aggregate, 49 to 36. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
K-Exaone's sharpest advantage is in coding, where it averages 49.4 against 14.9. The single biggest benchmark swing on the page is SWE-bench Verified, 18% to 49.4%.
K-Exaone is the reasoning model in the pair, while GLM-4.5 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. K-Exaone gives you the larger context window at 256K, compared with 128K for GLM-4.5.
Pick K-Exaone if you want the stronger benchmark profile. GLM-4.5 only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.
GLM-4.5
14.9
K-Exaone
49.4
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.
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.
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.
K-Exaone is ahead overall, 49 to 36. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 18% and 49.4%.
K-Exaone has the edge for coding in this comparison, averaging 49.4 versus 14.9. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
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