Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
MiniMax M2.7 is clearly ahead on the aggregate, 57 to 35. 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 56.2 against 14.6. The single biggest benchmark swing on the page is SWE-bench Pro, 15% to 56.2%.
MiniMax M2.7 gives you the larger context window at 200K, compared with 128K for GLM-4.5.
Pick MiniMax M2.7 if you want the stronger benchmark profile. GLM-4.5 only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
GLM-4.5
31.3
MiniMax M2.7
57
GLM-4.5
14.6
MiniMax M2.7
56.2
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.
MiniMax M2.7 is ahead overall, 57 to 35. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 15% and 56.2%.
MiniMax M2.7 has the edge for coding in this comparison, averaging 56.2 versus 14.6. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
MiniMax M2.7 has the edge for agentic tasks in this comparison, averaging 57 versus 31.3. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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