Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
Mistral Large 3 is clearly ahead on the aggregate, 68 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 $2.00 input / $6.00 output per 1M tokens for Mistral Large 3. That is roughly 100.0x on output cost alone. o1-pro is the reasoning model in the pair, while Mistral Large 3 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. o1-pro gives you the larger context window at 200K, compared with 128K for Mistral Large 3.
Pick Mistral Large 3 if you want the stronger benchmark profile. o1-pro only becomes the better choice if knowledge is the priority or you need the larger 200K context window.
Mistral Large 3
63.5
o1-pro
79
Mistral Large 3
75.6
o1-pro
86
Mistral Large 3 is ahead overall, 68 to 33. The biggest single separator in this matchup is AIME 2024, where the scores are 78 and 86.
o1-pro has the edge for knowledge tasks in this comparison, averaging 79 versus 63.5. 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 75.6. Inside this category, AIME 2024 is the benchmark that creates the most daylight between them.
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