Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-5.2
94
GPT-4.1 nano
26
Verified leaderboard positions: GLM-5.2 #9 · GPT-4.1 nano unranked
Pick GLM-5.2 if you want the stronger benchmark profile. GPT-4.1 nano only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.
Knowledge
+16.9 difference
GLM-5.2
GPT-4.1 nano
$1.4 / $4.4
$0.1 / $0.4
N/A
181 t/s
N/A
0.63s
1M
1M
Pick GLM-5.2 if you want the stronger benchmark profile. GPT-4.1 nano only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.
GLM-5.2 is clearly ahead on the provisional aggregate, 94 to 26. 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 50.3. The single biggest benchmark swing on the page is GPQA, 91.2% to 50.3%.
GLM-5.2 is also the more expensive model on tokens at $1.40 input / $4.40 output per 1M tokens, versus $0.10 input / $0.40 output per 1M tokens for GPT-4.1 nano. That is roughly 11.0x on output cost alone. GLM-5.2 is the reasoning model in the pair, while GPT-4.1 nano is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use.
GLM-5.2 is ahead on BenchLM's provisional leaderboard, 94 to 26. The biggest single separator in this matchup is GPQA, where the scores are 91.2% and 50.3%.
GLM-5.2 has the edge for knowledge tasks in this comparison, averaging 67.2 versus 50.3. Inside this category, GPQA is the benchmark that creates the most daylight between them.
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