Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
MiniMax M2.5 is clearly ahead on the aggregate, 66 to 31. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
MiniMax M2.5 is also the more expensive model on tokens at $0.30 input / $1.20 output per 1M tokens, versus $0.05 input / $0.40 output per 1M tokens for GPT-5 nano. That is roughly 3.0x on output cost alone. GPT-5 nano 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. GPT-5 nano gives you the larger context window at 400K, compared with 128K for MiniMax M2.5.
Pick MiniMax M2.5 if you want the stronger benchmark profile. GPT-5 nano only becomes the better choice if mathematics is the priority or you want the cheaper token bill.
MiniMax M2.5
61
GPT-5 nano
71.2
MiniMax M2.5
73.1
GPT-5 nano
85.2
MiniMax M2.5 is ahead overall, 66 to 31. The biggest single separator in this matchup is AIME 2025, where the scores are 74 and 85.2.
GPT-5 nano has the edge for knowledge tasks in this comparison, averaging 71.2 versus 61. Inside this category, GPQA is the benchmark that creates the most daylight between them.
GPT-5 nano has the edge for math in this comparison, averaging 85.2 versus 73.1. Inside this category, AIME 2025 is the benchmark that creates the most daylight between them.
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