Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
o1 is clearly ahead on the aggregate, 51 to 42. 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 38.7. The single biggest benchmark swing on the page is MMLU, 91.8 to 46.
o1 is also the more expensive model on tokens at $15.00 input / $60.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Llama 4 Maverick. That is roughly Infinityx on output cost alone. o1 is the reasoning model in the pair, while Llama 4 Maverick 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. Llama 4 Maverick gives you the larger context window at 1M, compared with 200K for o1.
Pick o1 if you want the stronger benchmark profile. Llama 4 Maverick only becomes the better choice if you want the cheaper token bill or you need the larger 1M context window.
o1
83.8
Llama 4 Maverick
38.7
o1
41
Llama 4 Maverick
22
o1
74.3
Llama 4 Maverick
46.8
o1
92.2
Llama 4 Maverick
68
o1 is ahead overall, 51 to 42. The biggest single separator in this matchup is MMLU, where the scores are 91.8 and 46.
o1 has the edge for knowledge tasks in this comparison, averaging 83.8 versus 38.7. 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 22. 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 46.8. 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 68. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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