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 82. The single biggest benchmark swing on the page is MMLU, 86.9 to 82. GPT-4o mini does hit back in coding, so the answer changes if that is the part of the workload you care about most.
o3-mini is also the more expensive model on tokens at $1.10 input / $4.40 output per 1M tokens, versus $0.15 input / $0.60 output per 1M tokens for GPT-4o mini. That is roughly 7.3x on output cost alone. o3-mini 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. o3-mini gives you the larger context window at 200K, compared with 128K for GPT-4o mini.
Pick o3-mini if you want the stronger benchmark profile. GPT-4o mini only becomes the better choice if coding is the priority or you want the cheaper token bill.
o3-mini
82.1
GPT-4o mini
82
o3-mini
49.3
GPT-4o mini
87.2
o3-mini is ahead overall, 56 to 43. The biggest single separator in this matchup is MMLU, where the scores are 86.9 and 82.
o3-mini has the edge for knowledge tasks in this comparison, averaging 82.1 versus 82. 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 49.3. o3-mini stays close enough that the answer can still flip depending on your workload.
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