Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
o1 is clearly ahead on the aggregate, 51 to 43. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
o1's sharpest advantage is in knowledge, where it averages 83.8 against 82. The single biggest benchmark swing on the page is MMLU, 91.8 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.
o1 is also the more expensive model on tokens at $15.00 input / $60.00 output per 1M tokens, versus $0.15 input / $0.60 output per 1M tokens for GPT-4o mini. That is roughly 100.0x on output cost alone. o1 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. o1 gives you the larger context window at 200K, compared with 128K for GPT-4o mini.
Pick o1 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.
o1
83.8
GPT-4o mini
82
o1
41
GPT-4o mini
87.2
o1 is ahead overall, 51 to 43. The biggest single separator in this matchup is MMLU, where the scores are 91.8 and 82.
o1 has the edge for knowledge tasks in this comparison, averaging 83.8 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 41. o1 stays close enough that the answer can still flip depending on your workload.
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