Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GLM-4.7 is clearly ahead on the aggregate, 69 to 49. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-4.7's sharpest advantage is in coding, where it averages 69.3 against 49.4. The single biggest benchmark swing on the page is SWE-bench Verified, 73.8% to 49.4%.
K-Exaone gives you the larger context window at 256K, compared with 200K for GLM-4.7.
Pick GLM-4.7 if you want the stronger benchmark profile. K-Exaone only becomes the better choice if you need the larger 256K context window.
Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.
GLM-4.7
69.3
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.
GLM-4.7 is ahead overall, 69 to 49. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 73.8% and 49.4%.
GLM-4.7 has the edge for coding in this comparison, averaging 69.3 versus 49.4. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
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