Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-5.2
94
GPT-4.1 mini
45
Verified leaderboard positions: GLM-5.2 #9 · GPT-4.1 mini unranked
Pick GLM-5.2 if you want the stronger benchmark profile. GPT-4.1 mini 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.
Coding
+38.5 difference
Knowledge
+3.0 difference
GLM-5.2
GPT-4.1 mini
$1.4 / $4.4
$0.4 / $1.6
N/A
80 t/s
N/A
0.76s
1M
1M
Pick GLM-5.2 if you want the stronger benchmark profile. GPT-4.1 mini 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 45. 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 23.6. The single biggest benchmark swing on the page is GPQA, 91.2% to 64.2%.
GLM-5.2 is also the more expensive model on tokens at $1.40 input / $4.40 output per 1M tokens, versus $0.40 input / $1.60 output per 1M tokens for GPT-4.1 mini. That is roughly 2.8x on output cost alone. GLM-5.2 is the reasoning model in the pair, while GPT-4.1 mini 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 45. The biggest single separator in this matchup is GPQA, where the scores are 91.2% and 64.2%.
GLM-5.2 has the edge for knowledge tasks in this comparison, averaging 67.2 versus 64.2. Inside this category, GPQA 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 23.6. GPT-4.1 mini stays close enough that the answer can still flip depending on your workload.
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