Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-5
67
MiniMax M2.7
54
Verified leaderboard positions: GLM-5 #21 · 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 54. 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 54. 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, GDPval-AA is the benchmark that creates the most daylight between them.
For engineers, researchers, and the plain curious — a weekly brief on new models, ranking shifts, and pricing changes.
Free. No spam. Unsubscribe anytime.