Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
MiniMax M2.5 is clearly ahead on the aggregate, 66 to 56. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
o3-mini is also the more expensive model on tokens at $1.10 input / $4.40 output per 1M tokens, versus $0.30 input / $1.20 output per 1M tokens for MiniMax M2.5. That is roughly 3.7x on output cost alone. o3-mini 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. o3-mini 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. o3-mini only becomes the better choice if knowledge is the priority or you need the larger 200K context window.
MiniMax M2.5
61
o3-mini
82.1
MiniMax M2.5
48.3
o3-mini
49.3
MiniMax M2.5
73.1
o3-mini
87.3
MiniMax M2.5
85
o3-mini
93.9
MiniMax M2.5 is ahead overall, 66 to 56. The biggest single separator in this matchup is MMLU, where the scores are 73 and 86.9.
o3-mini has the edge for knowledge tasks in this comparison, averaging 82.1 versus 61. Inside this category, MMLU is the benchmark that creates the most daylight between them.
o3-mini has the edge for coding in this comparison, averaging 49.3 versus 48.3. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
o3-mini has the edge for math in this comparison, averaging 87.3 versus 73.1. Inside this category, AIME 2024 is the benchmark that creates the most daylight between them.
o3-mini has the edge for instruction following in this comparison, averaging 93.9 versus 85. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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