Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-4.7
70
GPT-4.1 nano
27
Pick GLM-4.7 if you want the stronger benchmark profile. GPT-4.1 nano only becomes the better choice if you need the larger 1M context window or you would rather avoid the extra latency and token burn of a reasoning model.
Knowledge
+10.3 difference
GLM-4.7
GPT-4.1 nano
$0 / $0
$0.1 / $0.4
82 t/s
181 t/s
1.10s
0.63s
200K
1M
Pick GLM-4.7 if you want the stronger benchmark profile. GPT-4.1 nano only becomes the better choice if you need the larger 1M context window or you would rather avoid the extra latency and token burn of a reasoning model.
GLM-4.7 is clearly ahead on the provisional aggregate, 70 to 27. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-4.7's sharpest advantage is in knowledge, where it averages 60.6 against 50.3. The single biggest benchmark swing on the page is GPQA, 85.7% to 50.3%.
GPT-4.1 nano is also the more expensive model on tokens at $0.10 input / $0.40 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for GLM-4.7. That is roughly Infinityx on output cost alone. GLM-4.7 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. GPT-4.1 nano gives you the larger context window at 1M, compared with 200K for GLM-4.7.
GLM-4.7 is ahead on BenchLM's provisional leaderboard, 70 to 27. The biggest single separator in this matchup is GPQA, where the scores are 85.7% and 50.3%.
GLM-4.7 has the edge for knowledge tasks in this comparison, averaging 60.6 versus 50.3. Inside this category, GPQA is the benchmark that creates the most daylight between them.
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