Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-5.2
94
GPT-5.4 Pro
90
Verified leaderboard positions: GLM-5.2 #9 · GPT-5.4 Pro unranked
Pick GLM-5.2 if you want the stronger benchmark profile. GPT-5.4 Pro only becomes the better choice if agentic is the priority or you need the larger 1.05M context window.
Agentic
+8.3 difference
Knowledge
+18.2 difference
GLM-5.2
GPT-5.4 Pro
$1.4 / $4.4
$30 / $180
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 Pro only becomes the better choice if agentic is the priority or you need the larger 1.05M context window.
GLM-5.2 is clearly ahead on the provisional aggregate, 94 to 90. 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 knowledge, where it averages 67.2 against 49. The single biggest benchmark swing on the page is HLE, 54.7% to 58.7%. GPT-5.4 Pro does hit back in agentic, so the answer changes if that is the part of the workload you care about most.
GPT-5.4 Pro is also the more expensive model on tokens at $30.00 input / $180.00 output per 1M tokens, versus $1.40 input / $4.40 output per 1M tokens for GLM-5.2. That is roughly 40.9x on output cost alone. GPT-5.4 Pro 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 90. The biggest single separator in this matchup is HLE, where the scores are 54.7% and 58.7%.
GLM-5.2 has the edge for knowledge tasks in this comparison, averaging 67.2 versus 49. Inside this category, HLE is the benchmark that creates the most daylight between them.
GPT-5.4 Pro has the edge for agentic tasks in this comparison, averaging 89.3 versus 81. GLM-5.2 stays close enough that the answer can still flip depending on your workload.
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