Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
DeepSeek-R1 is clearly ahead on the aggregate, 43 to 39. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
DeepSeek-R1 is also the more expensive model on tokens at $0.55 input / $2.19 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Phi-4. That is roughly Infinityx on output cost alone. DeepSeek-R1 is the reasoning model in the pair, while Phi-4 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. DeepSeek-R1 gives you the larger context window at 128K, compared with 16K for Phi-4.
Pick DeepSeek-R1 if you want the stronger benchmark profile. Phi-4 only becomes the better choice if coding is the priority or you want the cheaper token bill.
DeepSeek-R1
38.8
Phi-4
70.5
DeepSeek-R1
24
Phi-4
82.6
DeepSeek-R1
61
Phi-4
80.6
DeepSeek-R1 is ahead overall, 43 to 39. The biggest single separator in this matchup is HumanEval, where the scores are 36 and 82.6.
Phi-4 has the edge for knowledge tasks in this comparison, averaging 70.5 versus 38.8. Inside this category, MMLU is the benchmark that creates the most daylight between them.
Phi-4 has the edge for coding in this comparison, averaging 82.6 versus 24. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
Phi-4 has the edge for multilingual tasks in this comparison, averaging 80.6 versus 61. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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