Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
Gemini 3.1 Pro is clearly ahead on the aggregate, 89 to 31. 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 knowledge, where it averages 86 against 71.2. The single biggest benchmark swing on the page is GPQA, 97 to 71.2.
Gemini 3.1 Pro is also the more expensive model on tokens at $1.25 input / $5.00 output per 1M tokens, versus $0.05 input / $0.40 output per 1M tokens for GPT-5 nano. That is roughly 12.5x on output cost alone. GPT-5 nano is the reasoning model in the pair, while Gemini 3.1 Pro 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. Gemini 3.1 Pro gives you the larger context window at 1M, compared with 400K for GPT-5 nano.
Pick Gemini 3.1 Pro if you want the stronger benchmark profile. GPT-5 nano only becomes the better choice if you want the cheaper token bill or you want the stronger reasoning-first profile.
Gemini 3.1 Pro
86
GPT-5 nano
71.2
Gemini 3.1 Pro
97.1
GPT-5 nano
85.2
Gemini 3.1 Pro is ahead overall, 89 to 31. The biggest single separator in this matchup is GPQA, where the scores are 97 and 71.2.
Gemini 3.1 Pro has the edge for knowledge tasks in this comparison, averaging 86 versus 71.2. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Gemini 3.1 Pro has the edge for math in this comparison, averaging 97.1 versus 85.2. Inside this category, AIME 2025 is the benchmark that creates the most daylight between them.
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