Head-to-head comparison across 4benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-5
67
MAI-Thinking-1
65
Verified leaderboard positions: GLM-5 #22 · MAI-Thinking-1 #23
Pick GLM-5 if you want the stronger benchmark profile. MAI-Thinking-1 only becomes the better choice if coding is the priority or you need the larger 256K context window.
Agentic
+10.2 difference
Coding
+7.8 difference
Knowledge
+0.8 difference
Inst. Following
+7.6 difference
GLM-5
MAI-Thinking-1
$1 / $3.2
N/A
74 t/s
N/A
1.64s
N/A
200K
256K
Pick GLM-5 if you want the stronger benchmark profile. MAI-Thinking-1 only becomes the better choice if coding is the priority or you need the larger 256K context window.
GLM-5 has the cleaner provisional overall profile here, landing at 67 versus 65. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
GLM-5's sharpest advantage is in agentic, where it averages 56.2 against 46. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 56.2% 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.
MAI-Thinking-1 is the reasoning model in the pair, while GLM-5 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. MAI-Thinking-1 gives you the larger context window at 256K, compared with 200K for GLM-5.
GLM-5 is ahead on BenchLM's provisional leaderboard, 67 to 65. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 56.2% and 46%.
GLM-5 has the edge for knowledge tasks in this comparison, averaging 70.7 versus 69.9. 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 63.2. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
GLM-5 has the edge for agentic tasks in this comparison, averaging 56.2 versus 46. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
GLM-5 has the edge for instruction following in this comparison, averaging 92.6 versus 85. MAI-Thinking-1 stays close enough that the answer can still flip depending on your workload.
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