Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
o3 is clearly ahead on the aggregate, 76 to 43. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
o3 is also the more expensive model on tokens at $10.00 input / $40.00 output per 1M tokens, versus $0.15 input / $0.60 output per 1M tokens for GPT-4o mini. That is roughly 66.7x on output cost alone. o3 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 gives you the larger context window at 200K, compared with 128K for GPT-4o mini.
Pick o3 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
73.3
GPT-4o mini
82
o3
56
GPT-4o mini
87.2
o3
83
GPT-4o mini
87
o3 is ahead overall, 76 to 43. The biggest single separator in this matchup is HumanEval, where the scores are 78 and 87.2.
GPT-4o mini has the edge for knowledge tasks in this comparison, averaging 82 versus 73.3. 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 56. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
GPT-4o mini has the edge for multilingual tasks in this comparison, averaging 87 versus 83. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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