Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GLM-5 is clearly ahead on the aggregate, 63 to 49. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5's sharpest advantage is in coding, where it averages 62.2 against 49.4. The single biggest benchmark swing on the page is SWE-bench Verified, 77.8% to 49.4%.
K-Exaone 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. K-Exaone gives you the larger context window at 256K, compared with 200K for GLM-5.
Pick GLM-5 if you want the stronger benchmark profile. K-Exaone only becomes the better choice if you need the larger 256K context window or you want the stronger reasoning-first profile.
Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.
GLM-5
62.2
K-Exaone
49.4
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.
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.
GLM-5 is ahead overall, 63 to 49. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 77.8% and 49.4%.
GLM-5 has the edge for coding in this comparison, averaging 62.2 versus 49.4. Inside this category, SWE-bench Verified 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.