Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
Sibling matchup inside the GPT-4o family.
GPT-4o and GPT-4o mini sit in the same GPT-4o family. This page is less about two unrelated model lineages and more about how the siblings trade off on benchmark shape, token costs, and practical limits like context window.
GPT-4o is clearly ahead on the aggregate, 60 to 43. 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 $0.15 input / $0.60 output per 1M tokens for GPT-4o mini. That is roughly 16.7x on output cost alone.
GPT-4o makes more sense if you want this variant’s specific profile, while GPT-4o mini is the cleaner fit if coding is the priority or you want the cheaper token bill.
GPT-4o
53.8
GPT-4o mini
82
GPT-4o
38.7
GPT-4o mini
87.2
GPT-4o
82
GPT-4o mini
87
GPT-4o and GPT-4o mini are sibling variants in the GPT-4o family, so the right pick depends on whether you value the better benchmark line, cheaper tokens, or the larger context window. GPT-4o is ahead overall 60 to 43.
GPT-4o mini has the edge for knowledge tasks in this comparison, averaging 82 versus 53.8. 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 38.7. 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 82. Inside this category, MGSM is the benchmark that creates the most daylight between them.
Get notified when new models drop, benchmark scores change, or the leaderboard shifts. One email per week.
Free. No spam. Unsubscribe anytime. We only store derived location metadata for consent routing.