Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-5.2
94
GPT-5.4
88
Verified leaderboard positions: GLM-5.2 #9 · GPT-5.4 #19
Pick GLM-5.2 if you want the stronger benchmark profile. GPT-5.4 only becomes the better choice if you need the larger 1.05M context window.
Agentic
+4.0 difference
Coding
+4.4 difference
Knowledge
+1.1 difference
GLM-5.2
GPT-5.4
$1.4 / $4.4
$2.5 / $15
N/A
74 t/s
N/A
151.79s
1M
1.05M
Pick GLM-5.2 if you want the stronger benchmark profile. GPT-5.4 only becomes the better choice if you need the larger 1.05M context window.
GLM-5.2 is clearly ahead on the provisional aggregate, 94 to 88. 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 57.7. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 81% to 75.1%.
GPT-5.4 is also the more expensive model on tokens at $2.50 input / $15.00 output per 1M tokens, versus $1.40 input / $4.40 output per 1M tokens for GLM-5.2. That is roughly 3.4x on output cost alone. GPT-5.4 gives you the larger context window at 1.05M, compared with 1M for GLM-5.2.
GLM-5.2 is ahead on BenchLM's provisional leaderboard, 94 to 88. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 81% and 75.1%.
GLM-5.2 has the edge for knowledge tasks in this comparison, averaging 67.2 versus 66.1. 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 57.7. Inside this category, SWE-bench Pro 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 77. Inside this category, Toolathlon is the benchmark that creates the most daylight between them.
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