Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
GPT-4o is clearly ahead on the aggregate, 60 to 51. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
o1 is also the more expensive model on tokens at $15.00 input / $60.00 output per 1M tokens, versus $2.50 input / $10.00 output per 1M tokens for GPT-4o. That is roughly 6.0x on output cost alone. o1 is the reasoning model in the pair, while GPT-4o 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. o1 gives you the larger context window at 200K, compared with 128K for GPT-4o.
Pick GPT-4o if you want the stronger benchmark profile. o1 only becomes the better choice if knowledge is the priority or you need the larger 200K context window.
GPT-4o
53.8
o1
83.8
GPT-4o
38.7
o1
41
GPT-4o
66.9
o1
74.3
GPT-4o
82
o1
92.2
GPT-4o is ahead overall, 60 to 51. The biggest single separator in this matchup is MMLU, where the scores are 66 and 91.8.
o1 has the edge for knowledge tasks in this comparison, averaging 83.8 versus 53.8. Inside this category, MMLU is the benchmark that creates the most daylight between them.
o1 has the edge for coding in this comparison, averaging 41 versus 38.7. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
o1 has the edge for math in this comparison, averaging 74.3 versus 66.9. 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 82. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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