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 47.
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 Scout. That is roughly Infinityx on output cost alone. o1 is the reasoning model in the pair, while Llama 4 Scout 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 Scout gives you the larger context window at 10M, compared with 200K for o1.
Pick o1 if you want the stronger benchmark profile. Llama 4 Scout only becomes the better choice if you want the cheaper token bill or you need the larger 10M context window.
o1
83.8
Llama 4 Scout
38.7
o1
41
Llama 4 Scout
20.7
o1
74.3
Llama 4 Scout
47.4
o1
92.2
Llama 4 Scout
68
o1 is ahead overall, 51 to 42. The biggest single separator in this matchup is MMLU, where the scores are 91.8 and 47.
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 20.7. 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 47.4. 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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