Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
DeepSeek-R1 and GPT-4.1 finish on the same overall score, so this is less about a single winner and more about where the edge shows up. The headline says tie; the benchmark table is where the real choice happens.
GPT-4.1 is also the more expensive model on tokens at $2.00 input / $8.00 output per 1M tokens, versus $0.55 input / $2.19 output per 1M tokens for DeepSeek-R1. That is roughly 3.7x on output cost alone. DeepSeek-R1 is the reasoning model in the pair, while GPT-4.1 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 gives you the larger context window at 1M, compared with 128K for DeepSeek-R1.
Treat this as a split decision. DeepSeek-R1 makes more sense if mathematics is the priority or you want the cheaper token bill; GPT-4.1 is the better fit if knowledge is the priority or you need the larger 1M context window.
DeepSeek-R1
38.8
GPT-4.1
78.3
DeepSeek-R1
24
GPT-4.1
54.6
DeepSeek-R1
45.6
GPT-4.1
26.4
DeepSeek-R1
69
GPT-4.1
87.4
DeepSeek-R1 and GPT-4.1 are tied on overall score, so the right pick depends on which category matters most for your use case.
GPT-4.1 has the edge for knowledge tasks in this comparison, averaging 78.3 versus 38.8. Inside this category, MMLU is the benchmark that creates the most daylight between them.
GPT-4.1 has the edge for coding in this comparison, averaging 54.6 versus 24. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
DeepSeek-R1 has the edge for math in this comparison, averaging 45.6 versus 26.4. 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 69. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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