Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-5.2
94
GPT-5.4 nano
59
Verified leaderboard positions: GLM-5.2 #9 · GPT-5.4 nano unranked
Pick GLM-5.2 if you want the stronger benchmark profile. GPT-5.4 nano only becomes the better choice if you want the cheaper token bill.
Agentic
+38.1 difference
Knowledge
+14.0 difference
GLM-5.2
GPT-5.4 nano
$1.4 / $4.4
$0.2 / $1.25
N/A
191 t/s
N/A
3.64s
1M
400K
Pick GLM-5.2 if you want the stronger benchmark profile. GPT-5.4 nano only becomes the better choice if you want the cheaper token bill.
GLM-5.2 is clearly ahead on the provisional aggregate, 94 to 59. 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 42.9. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 81% to 46.3%.
GLM-5.2 is also the more expensive model on tokens at $1.40 input / $4.40 output per 1M tokens, versus $0.20 input / $1.25 output per 1M tokens for GPT-5.4 nano. That is roughly 3.5x on output cost alone. GLM-5.2 gives you the larger context window at 1M, compared with 400K for GPT-5.4 nano.
GLM-5.2 is ahead on BenchLM's provisional leaderboard, 94 to 59. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 81% and 46.3%.
GLM-5.2 has the edge for knowledge tasks in this comparison, averaging 67.2 versus 53.2. 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 42.9. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
For engineers, researchers, and the plain curious — a weekly brief on new models, ranking shifts, and pricing changes.
Free. No spam. Unsubscribe anytime.