Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
DeepSeek-R1 and GPT-4o mini 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.
DeepSeek-R1 is also the more expensive model on tokens at $0.55 input / $2.19 output per 1M tokens, versus $0.15 input / $0.60 output per 1M tokens for GPT-4o mini. That is roughly 3.6x on output cost alone. DeepSeek-R1 is the reasoning model in the pair, while GPT-4o mini 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.
Treat this as a split decision. DeepSeek-R1 makes more sense if you want the stronger reasoning-first profile; GPT-4o mini is the better fit if coding is the priority or you want the cheaper token bill.
DeepSeek-R1
38.8
GPT-4o mini
82
DeepSeek-R1
24
GPT-4o mini
87.2
DeepSeek-R1
61
GPT-4o mini
87
DeepSeek-R1 and GPT-4o mini are tied on overall score, so the right pick depends on which category matters most for your use case.
GPT-4o mini has the edge for knowledge tasks in this comparison, averaging 82 versus 38.8. 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 24. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
GPT-4o mini has the edge for multilingual tasks in this comparison, averaging 87 versus 61. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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