Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
o3-mini is clearly ahead on the aggregate, 56 to 35. 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 29.7. The single biggest benchmark swing on the page is MMLU, 86.9 to 33.
o3-mini is the reasoning model in the pair, while DeepSeek V3.1 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. o3-mini gives you the larger context window at 200K, compared with 128K for DeepSeek V3.1.
Pick o3-mini if you want the stronger benchmark profile. DeepSeek V3.1 only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
o3-mini
82.1
DeepSeek V3.1
29.7
o3-mini
49.3
DeepSeek V3.1
17.7
o3-mini
87.3
DeepSeek V3.1
35.4
o3-mini
93.9
DeepSeek V3.1
67
o3-mini is ahead overall, 56 to 35. The biggest single separator in this matchup is MMLU, where the scores are 86.9 and 33.
o3-mini has the edge for knowledge tasks in this comparison, averaging 82.1 versus 29.7. 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 17.7. 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 35.4. 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 67. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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