Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
Gemini 2.5 Pro is clearly ahead on the aggregate, 72 to 33. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
o1-pro is also the more expensive model on tokens at $150.00 input / $600.00 output per 1M tokens, versus $1.25 input / $5.00 output per 1M tokens for Gemini 2.5 Pro. That is roughly 120.0x on output cost alone. o1-pro is the reasoning model in the pair, while Gemini 2.5 Pro 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. Gemini 2.5 Pro gives you the larger context window at 1M, compared with 200K for o1-pro.
Pick Gemini 2.5 Pro if you want the stronger benchmark profile. o1-pro only becomes the better choice if knowledge is the priority or you want the stronger reasoning-first profile.
Gemini 2.5 Pro
67.5
o1-pro
79
Gemini 2.5 Pro
83.1
o1-pro
86
Gemini 2.5 Pro is ahead overall, 72 to 33. The biggest single separator in this matchup is GPQA, where the scores are 83 and 79.
o1-pro has the edge for knowledge tasks in this comparison, averaging 79 versus 67.5. 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 83.1. Gemini 2.5 Pro stays close enough that the answer can still flip depending on your workload.
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