Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
o1 is clearly ahead on the aggregate, 51 to 23. 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 mathematics, where it averages 74.3 against 9.8. The single biggest benchmark swing on the page is AIME 2024, 74.3 to 9.8.
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.
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
83.8
GPT-4.1 nano
65.2
o1
74.3
GPT-4.1 nano
9.8
o1
92.2
GPT-4.1 nano
83.2
o1 is ahead overall, 51 to 23. The biggest single separator in this matchup is AIME 2024, where the scores are 74.3 and 9.8.
o1 has the edge for knowledge tasks in this comparison, averaging 83.8 versus 65.2. Inside this category, GPQA is the benchmark that creates the most daylight between them.
o1 has the edge for math in this comparison, averaging 74.3 versus 9.8. Inside this category, AIME 2024 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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