Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-5.2
94
MAI-Thinking-1
65
Verified leaderboard positions: GLM-5.2 #9 · MAI-Thinking-1 #26
Pick GLM-5.2 if you want the stronger benchmark profile. MAI-Thinking-1 only becomes the better choice if coding is the priority.
Agentic
+35.0 difference
Coding
+8.9 difference
Knowledge
+2.7 difference
GLM-5.2
MAI-Thinking-1
$1.4 / $4.4
N/A
N/A
N/A
N/A
N/A
1M
256K
Pick GLM-5.2 if you want the stronger benchmark profile. MAI-Thinking-1 only becomes the better choice if coding is the priority.
GLM-5.2 is clearly ahead on the provisional aggregate, 94 to 65. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5.2's sharpest advantage is in agentic, where it averages 81 against 46. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 81% to 46%. MAI-Thinking-1 does hit back in coding, so the answer changes if that is the part of the workload you care about most.
GLM-5.2 gives you the larger context window at 1M, compared with 256K for MAI-Thinking-1.
GLM-5.2 is ahead on BenchLM's provisional leaderboard, 94 to 65. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 81% and 46%.
MAI-Thinking-1 has the edge for knowledge tasks in this comparison, averaging 69.9 versus 67.2. Inside this category, GPQA is the benchmark that creates the most daylight between them.
MAI-Thinking-1 has the edge for coding in this comparison, averaging 71 versus 62.1. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
GLM-5.2 has the edge for agentic tasks in this comparison, averaging 81 versus 46. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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