Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-5.2
94
GPT-5.4 mini
68
Verified leaderboard positions: GLM-5.2 #9 · GPT-5.4 mini unranked
Pick GLM-5.2 if you want the stronger benchmark profile. GPT-5.4 mini only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
Agentic
+15.4 difference
Knowledge
+9.8 difference
GLM-5.2
GPT-5.4 mini
$1.4 / $4.4
$0.75 / $4.5
N/A
201 t/s
N/A
3.85s
1M
400K
Pick GLM-5.2 if you want the stronger benchmark profile. GPT-5.4 mini only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
GLM-5.2 is clearly ahead on the provisional aggregate, 94 to 68. 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 agentic, where it averages 81 against 65.6. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 81% to 60%.
GPT-5.4 mini is also the more expensive model on tokens at $0.75 input / $4.50 output per 1M tokens, versus $1.40 input / $4.40 output per 1M tokens for GLM-5.2. GLM-5.2 gives you the larger context window at 1M, compared with 400K for GPT-5.4 mini.
GLM-5.2 is ahead on BenchLM's provisional leaderboard, 94 to 68. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 81% and 60%.
GLM-5.2 has the edge for knowledge tasks in this comparison, averaging 67.2 versus 57.4. Inside this category, HLE 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 65.6. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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