Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-5.2
94
GPT-5.5
89
Verified leaderboard positions: GLM-5.2 #9 · GPT-5.5 #7
Pick GLM-5.2 if you want the stronger benchmark profile. GPT-5.5 only becomes the better choice if agentic is the priority.
Agentic
+0.5 difference
Coding
+3.5 difference
Knowledge
+0.8 difference
GLM-5.2
GPT-5.5
$1.4 / $4.4
$5 / $30
N/A
N/A
N/A
N/A
1M
1M
Pick GLM-5.2 if you want the stronger benchmark profile. GPT-5.5 only becomes the better choice if agentic is the priority.
GLM-5.2 is clearly ahead on the provisional aggregate, 94 to 89. 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 coding, where it averages 62.1 against 58.6. The single biggest benchmark swing on the page is SWE-bench Pro, 62.1% to 58.6%. GPT-5.5 does hit back in agentic, so the answer changes if that is the part of the workload you care about most.
GPT-5.5 is also the more expensive model on tokens at $5.00 input / $30.00 output per 1M tokens, versus $1.40 input / $4.40 output per 1M tokens for GLM-5.2. That is roughly 6.8x on output cost alone.
GLM-5.2 is ahead on BenchLM's provisional leaderboard, 94 to 89. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 62.1% and 58.6%.
GLM-5.2 has the edge for knowledge tasks in this comparison, averaging 67.2 versus 66.4. Inside this category, HLE is the benchmark that creates the most daylight between them.
GLM-5.2 has the edge for coding in this comparison, averaging 62.1 versus 58.6. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
GPT-5.5 has the edge for agentic tasks in this comparison, averaging 81.5 versus 81. Inside this category, Toolathlon is the benchmark that creates the most daylight between them.
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