Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
DeepSeekMath V2 is clearly ahead on the aggregate, 71 to 23. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
DeepSeekMath V2's sharpest advantage is in mathematics, where it averages 80.4 against 9.8. The single biggest benchmark swing on the page is AIME 2024, 82 to 9.8. GPT-4.1 nano does hit back in instruction following, so the answer changes if that is the part of the workload you care about most.
DeepSeekMath V2 is the reasoning model in the pair, while GPT-4.1 nano 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-4.1 nano gives you the larger context window at 1M, compared with 128K for DeepSeekMath V2.
Pick DeepSeekMath V2 if you want the stronger benchmark profile. GPT-4.1 nano only becomes the better choice if instruction following is the priority or you need the larger 1M context window.
DeepSeekMath V2
67.2
GPT-4.1 nano
65.2
DeepSeekMath V2
80.4
GPT-4.1 nano
9.8
DeepSeekMath V2
83
GPT-4.1 nano
83.2
DeepSeekMath V2 is ahead overall, 71 to 23. The biggest single separator in this matchup is AIME 2024, where the scores are 82 and 9.8.
DeepSeekMath V2 has the edge for knowledge tasks in this comparison, averaging 67.2 versus 65.2. Inside this category, GPQA is the benchmark that creates the most daylight between them.
DeepSeekMath V2 has the edge for math in this comparison, averaging 80.4 versus 9.8. Inside this category, AIME 2024 is the benchmark that creates the most daylight between them.
GPT-4.1 nano has the edge for instruction following in this comparison, averaging 83.2 versus 83. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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