Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
Llama 4 Scout is clearly ahead on the aggregate, 42 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 $0.00 input / $0.00 output per 1M tokens for Llama 4 Scout. That is roughly Infinityx on output cost alone. o1-pro 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-pro.
Pick Llama 4 Scout 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.
Llama 4 Scout
38.7
o1-pro
79
Llama 4 Scout
47.4
o1-pro
86
Llama 4 Scout is ahead overall, 42 to 33. The biggest single separator in this matchup is AIME 2024, where the scores are 49 and 86.
o1-pro has the edge for knowledge tasks in this comparison, averaging 79 versus 38.7. 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 47.4. Inside this category, AIME 2024 is the benchmark that creates the most daylight between them.
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