Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GPT-4.1 nano
27
o1
58
Pick o1 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 need the larger 1M context window.
Knowledge
+25.4 difference
Inst. Following
+9.0 difference
GPT-4.1 nano
o1
$0.1 / $0.4
$15 / $60
181 t/s
98 t/s
0.63s
32.29s
1M
200K
Pick o1 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 need the larger 1M context window.
o1 is clearly ahead on the provisional aggregate, 58 to 27. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
o1's sharpest advantage is in knowledge, where it averages 75.7 against 50.3. The single biggest benchmark swing on the page is GPQA, 50.3% to 75.7%.
o1 is also the more expensive model on tokens at $15.00 input / $60.00 output per 1M tokens, versus $0.10 input / $0.40 output per 1M tokens for GPT-4.1 nano. That is roughly 150.0x on output cost alone. o1 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 o1.
o1 is ahead on BenchLM's provisional leaderboard, 58 to 27. The biggest single separator in this matchup is GPQA, where the scores are 50.3% and 75.7%.
o1 has the edge for knowledge tasks in this comparison, averaging 75.7 versus 50.3. Inside this category, GPQA is the benchmark that creates the most daylight between them.
o1 has the edge for instruction following in this comparison, averaging 92.2 versus 83.2. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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