Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
K-Exaone has the cleaner overall profile here, landing at 49 versus 47. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
K-Exaone's sharpest advantage is in coding, where it averages 49.4 against 25.9. The single biggest benchmark swing on the page is SWE-bench Verified, 49.4% to 34%.
K-Exaone is the reasoning model in the pair, while Moonshot v1 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 Moonshot v1.
Pick K-Exaone if you want the stronger benchmark profile. Moonshot v1 only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Benchmark data for this category is coming soon.
K-Exaone
49.4
Moonshot v1
25.9
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 47. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 49.4% and 34%.
K-Exaone has the edge for coding in this comparison, averaging 49.4 versus 25.9. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
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