Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Qwen2.5-1M is clearly ahead on the aggregate, 67 to 49. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
K-Exaone is the reasoning model in the pair, while Qwen2.5-1M 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. Qwen2.5-1M gives you the larger context window at 1M, compared with 256K for K-Exaone.
Pick Qwen2.5-1M if you want the stronger benchmark profile. K-Exaone only becomes the better choice if coding is the priority 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.
K-Exaone
49.4
Qwen2.5-1M
45.1
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.
Qwen2.5-1M is ahead overall, 67 to 49. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 49.4% and 47%.
K-Exaone has the edge for coding in this comparison, averaging 49.4 versus 45.1. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
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