Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
o3-mini is clearly ahead on the aggregate, 56 to 43. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
o3-mini's sharpest advantage is in knowledge, where it averages 82.1 against 38.8. The single biggest benchmark swing on the page is MMLU, 86.9 to 44.
o3-mini is also the more expensive model on tokens at $1.10 input / $4.40 output per 1M tokens, versus $0.55 input / $2.19 output per 1M tokens for DeepSeek-R1. That is roughly 2.0x on output cost alone. o3-mini gives you the larger context window at 200K, compared with 128K for DeepSeek-R1.
Pick o3-mini if you want the stronger benchmark profile. DeepSeek-R1 only becomes the better choice if you want the cheaper token bill.
o3-mini
82.1
DeepSeek-R1
38.8
o3-mini
49.3
DeepSeek-R1
24
o3-mini
87.3
DeepSeek-R1
45.6
o3-mini
93.9
DeepSeek-R1
69
o3-mini is ahead overall, 56 to 43. The biggest single separator in this matchup is MMLU, where the scores are 86.9 and 44.
o3-mini has the edge for knowledge tasks in this comparison, averaging 82.1 versus 38.8. Inside this category, MMLU is the benchmark that creates the most daylight between them.
o3-mini has the edge for coding in this comparison, averaging 49.3 versus 24. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
o3-mini has the edge for math in this comparison, averaging 87.3 versus 45.6. Inside this category, AIME 2024 is the benchmark that creates the most daylight between them.
o3-mini has the edge for instruction following in this comparison, averaging 93.9 versus 69. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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