Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
Gemini 3.1 Pro is clearly ahead on the aggregate, 89 to 43. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Gemini 3.1 Pro's sharpest advantage is in multilingual, where it averages 96 against 87. The single biggest benchmark swing on the page is MMLU, 99 to 82. GPT-4o mini does hit back in coding, so the answer changes if that is the part of the workload you care about most.
Gemini 3.1 Pro is also the more expensive model on tokens at $1.25 input / $5.00 output per 1M tokens, versus $0.15 input / $0.60 output per 1M tokens for GPT-4o mini. That is roughly 8.3x on output cost alone. Gemini 3.1 Pro gives you the larger context window at 1M, compared with 128K for GPT-4o mini.
Pick Gemini 3.1 Pro if you want the stronger benchmark profile. GPT-4o mini only becomes the better choice if coding is the priority or you want the cheaper token bill.
Gemini 3.1 Pro
86
GPT-4o mini
82
Gemini 3.1 Pro
79
GPT-4o mini
87.2
Gemini 3.1 Pro
96
GPT-4o mini
87
Gemini 3.1 Pro is ahead overall, 89 to 43. The biggest single separator in this matchup is MMLU, where the scores are 99 and 82.
Gemini 3.1 Pro has the edge for knowledge tasks in this comparison, averaging 86 versus 82. Inside this category, MMLU is the benchmark that creates the most daylight between them.
GPT-4o mini has the edge for coding in this comparison, averaging 87.2 versus 79. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
Gemini 3.1 Pro has the edge for multilingual tasks in this comparison, averaging 96 versus 87. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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