Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-5.2
94
GPT-4.1
57
Verified leaderboard positions: GLM-5.2 #9 · GPT-4.1 unranked
Pick GLM-5.2 if you want the stronger benchmark profile. GPT-4.1 only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Coding
+7.5 difference
Knowledge
+0.9 difference
GLM-5.2
GPT-4.1
$1.4 / $4.4
$2 / $8
N/A
108 t/s
N/A
1.02s
1M
1M
Pick GLM-5.2 if you want the stronger benchmark profile. GPT-4.1 only becomes the better choice if 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 57. 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 54.6. The single biggest benchmark swing on the page is GPQA, 91.2% to 66.3%.
GPT-4.1 is also the more expensive model on tokens at $2.00 input / $8.00 output per 1M tokens, versus $1.40 input / $4.40 output per 1M tokens for GLM-5.2. GLM-5.2 is the reasoning model in the pair, while GPT-4.1 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 57. The biggest single separator in this matchup is GPQA, where the scores are 91.2% and 66.3%.
GLM-5.2 has the edge for knowledge tasks in this comparison, averaging 67.2 versus 66.3. 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 54.6. GPT-4.1 stays close enough that the answer can still flip depending on your workload.
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