Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-5.1
82
MAI-Thinking-1
65
Verified leaderboard positions: GLM-5.1 #28 · MAI-Thinking-1 #23
Pick GLM-5.1 if you want the stronger benchmark profile. MAI-Thinking-1 only becomes the better choice if knowledge is the priority or you need the larger 256K context window.
Agentic
+19.3 difference
Coding
+10.1 difference
Knowledge
+17.6 difference
GLM-5.1
MAI-Thinking-1
$1.4 / $4.4
N/A
N/A
N/A
N/A
N/A
203K
256K
Pick GLM-5.1 if you want the stronger benchmark profile. MAI-Thinking-1 only becomes the better choice if knowledge is the priority or you need the larger 256K context window.
GLM-5.1 is clearly ahead on the provisional aggregate, 82 to 65. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5.1's sharpest advantage is in agentic, where it averages 65.3 against 46. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 63.5% to 46%. MAI-Thinking-1 does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
MAI-Thinking-1 gives you the larger context window at 256K, compared with 203K for GLM-5.1.
GLM-5.1 is ahead on BenchLM's provisional leaderboard, 82 to 65. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 63.5% and 46%.
MAI-Thinking-1 has the edge for knowledge tasks in this comparison, averaging 69.9 versus 52.3. Inside this category, GPQA-D is the benchmark that creates the most daylight between them.
MAI-Thinking-1 has the edge for coding in this comparison, averaging 71 versus 60.9. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
GLM-5.1 has the edge for agentic tasks in this comparison, averaging 65.3 versus 46. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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