Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
o1 has the cleaner overall profile here, landing at 51 versus 49. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
o1's sharpest advantage is in knowledge, where it averages 83.8 against 42.7. The single biggest benchmark swing on the page is MMLU, 91.8 to 50.
o1 is also the more expensive model on tokens at $15.00 input / $60.00 output per 1M tokens, versus $0.15 input / $0.60 output per 1M tokens for Gemini 2.5 Flash. That is roughly 100.0x on output cost alone. o1 is the reasoning model in the pair, while Gemini 2.5 Flash 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 Flash gives you the larger context window at 1M, compared with 200K for o1.
Pick o1 if you want the stronger benchmark profile. Gemini 2.5 Flash only becomes the better choice if you want the cheaper token bill or you need the larger 1M context window.
o1
83.8
Gemini 2.5 Flash
42.7
o1
41
Gemini 2.5 Flash
27.7
o1
74.3
Gemini 2.5 Flash
51.9
o1
92.2
Gemini 2.5 Flash
79
o1 is ahead overall, 51 to 49. The biggest single separator in this matchup is MMLU, where the scores are 91.8 and 50.
o1 has the edge for knowledge tasks in this comparison, averaging 83.8 versus 42.7. 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 27.7. 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 51.9. 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 79. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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