Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
MiniMax M2.5 is clearly ahead on the aggregate, 66 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 $0.30 input / $1.20 output per 1M tokens for MiniMax M2.5. That is roughly 500.0x on output cost alone. o1-pro is the reasoning model in the pair, while MiniMax M2.5 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 MiniMax M2.5.
Pick MiniMax M2.5 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.
MiniMax M2.5
61
o1-pro
79
MiniMax M2.5
73.1
o1-pro
86
MiniMax M2.5 is ahead overall, 66 to 33. The biggest single separator in this matchup is AIME 2024, where the scores are 75 and 86.
o1-pro has the edge for knowledge tasks in this comparison, averaging 79 versus 61. 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 73.1. Inside this category, AIME 2024 is the benchmark that creates the most daylight between them.
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