Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
MiniMax M2.7 is clearly ahead on the aggregate, 61 to 49. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
MiniMax M2.7's sharpest advantage is in coding, where it averages 64.4 against 49.4. The single biggest benchmark swing on the page is SWE-bench Verified, 49.4% to 78%.
K-Exaone is the reasoning model in the pair, while MiniMax M2.7 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 MiniMax M2.7.
Pick MiniMax M2.7 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.
K-Exaone
49.4
MiniMax M2.7
64.4
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.
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.
Benchmark data for this category is coming soon.
MiniMax M2.7 is ahead overall, 61 to 49. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 49.4% and 78%.
MiniMax M2.7 has the edge for coding in this comparison, averaging 64.4 versus 49.4. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
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