Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
GPT-4o is clearly ahead on the aggregate, 60 to 56. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-4o is also the more expensive model on tokens at $2.50 input / $10.00 output per 1M tokens, versus $1.10 input / $4.40 output per 1M tokens for o3-mini. That is roughly 2.3x on output cost alone. o3-mini is the reasoning model in the pair, while GPT-4o 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 GPT-4o.
Pick GPT-4o if you want the stronger benchmark profile. o3-mini only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
GPT-4o
53.8
o3-mini
82.1
GPT-4o
38.7
o3-mini
49.3
GPT-4o
66.9
o3-mini
87.3
GPT-4o
82
o3-mini
93.9
GPT-4o is ahead overall, 60 to 56. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 20 and 49.3.
o3-mini has the edge for knowledge tasks in this comparison, averaging 82.1 versus 53.8. 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 38.7. 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 66.9. 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 82. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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