Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
Sibling matchup inside the GPT-4.1 family.
GPT-4.1 and GPT-4.1 nano sit in the same GPT-4.1 family. This page is less about two unrelated model lineages and more about how the siblings trade off on benchmark shape, token costs, and practical limits like context window.
GPT-4.1 is clearly ahead on the aggregate, 43 to 23. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-4.1's sharpest advantage is in mathematics, where it averages 26.4 against 9.8. The single biggest benchmark swing on the page is AIME 2024, 26.4 to 9.8.
GPT-4.1 is also the more expensive model on tokens at $2.00 input / $8.00 output per 1M tokens, versus $0.10 input / $0.40 output per 1M tokens for GPT-4.1 nano. That is roughly 20.0x on output cost alone.
GPT-4.1 makes more sense if mathematics is the priority, while GPT-4.1 nano is the cleaner fit if you want the cheaper token bill.
GPT-4.1
78.3
GPT-4.1 nano
65.2
GPT-4.1
26.4
GPT-4.1 nano
9.8
GPT-4.1
87.4
GPT-4.1 nano
83.2
GPT-4.1 and GPT-4.1 nano are sibling variants in the GPT-4.1 family, so the right pick depends on whether you value the better benchmark line, cheaper tokens, or the larger context window. GPT-4.1 is ahead overall 43 to 23.
GPT-4.1 has the edge for knowledge tasks in this comparison, averaging 78.3 versus 65.2. Inside this category, GPQA is the benchmark that creates the most daylight between them.
GPT-4.1 has the edge for math in this comparison, averaging 26.4 versus 9.8. Inside this category, AIME 2024 is the benchmark that creates the most daylight between them.
GPT-4.1 has the edge for instruction following in this comparison, averaging 87.4 versus 83.2. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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