Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-5
67
MiniMax M2.7
62
Verified leaderboard positions: GLM-5 #17 · MiniMax M2.7 unranked
Pick GLM-5 if you want the stronger benchmark profile. MiniMax M2.7 only becomes the better choice if agentic is the priority or you want the cheaper token bill.
Agentic
+0.8 difference
Coding
+9.5 difference
GLM-5
MiniMax M2.7
$1 / $3.2
$0.3 / $1.2
74 t/s
45 t/s
1.64s
2.53s
200K
200K
Pick GLM-5 if you want the stronger benchmark profile. MiniMax M2.7 only becomes the better choice if agentic is the priority or you want the cheaper token bill.
GLM-5 is clearly ahead on the provisional aggregate, 67 to 62. 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 63.2 against 53.7. The single biggest benchmark swing on the page is SWE-Rebench, 62.8% to 51.9%. MiniMax M2.7 does hit back in agentic, so the answer changes if that is the part of the workload you care about most.
GLM-5 is also the more expensive model on tokens at $1.00 input / $3.20 output per 1M tokens, versus $0.30 input / $1.20 output per 1M tokens for MiniMax M2.7. That is roughly 2.7x on output cost alone.
GLM-5 is ahead on BenchLM's provisional leaderboard, 67 to 62. The biggest single separator in this matchup is SWE-Rebench, where the scores are 62.8% and 51.9%.
GLM-5 has the edge for coding in this comparison, averaging 63.2 versus 53.7. Inside this category, SWE-Rebench 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 56.2. Inside this category, Toolathlon is the benchmark that creates the most daylight between them.
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