Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
Sibling matchup inside the o1 family.
o1 and o1-pro sit in the same o1 family. This page is less about two unrelated model lineages and more about how the siblings trade off on benchmark shape, token costs, and practical limits like context window.
o1 is clearly ahead on the aggregate, 51 to 33. 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 83.8 against 79. The single biggest benchmark swing on the page is AIME 2024, 74.3 to 86. o1-pro does hit back in mathematics, so the answer changes if that is the part of the workload you care about most.
o1-pro is also the more expensive model on tokens at $150.00 input / $600.00 output per 1M tokens, versus $15.00 input / $60.00 output per 1M tokens for o1. That is roughly 10.0x on output cost alone.
o1 makes more sense if knowledge is the priority or you want the cheaper token bill, while o1-pro is the cleaner fit if mathematics is the priority.
o1
83.8
o1-pro
79
o1
74.3
o1-pro
86
o1 and o1-pro are sibling variants in the o1 family, so the right pick depends on whether you value the better benchmark line, cheaper tokens, or the larger context window. o1 is ahead overall 51 to 33.
o1 has the edge for knowledge tasks in this comparison, averaging 83.8 versus 79. Inside this category, GPQA is the benchmark that creates the most daylight between them.
o1-pro has the edge for math in this comparison, averaging 86 versus 74.3. Inside this category, AIME 2024 is the benchmark that creates the most daylight between them.
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