Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
GPT-5.1-Codex-Max is clearly ahead on the aggregate, 87 to 43. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.1-Codex-Max's sharpest advantage is in multilingual, where it averages 89 against 87. The single biggest benchmark swing on the page is MMLU, 98 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.
GPT-5.1-Codex-Max is also the more expensive model on tokens at $2.00 input / $8.00 output per 1M tokens, versus $0.15 input / $0.60 output per 1M tokens for GPT-4o mini. That is roughly 13.3x on output cost alone. GPT-5.1-Codex-Max 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. GPT-5.1-Codex-Max gives you the larger context window at 400K, compared with 128K for GPT-4o mini.
Pick GPT-5.1-Codex-Max 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.
GPT-5.1-Codex-Max
81.5
GPT-4o mini
82
GPT-5.1-Codex-Max
78.7
GPT-4o mini
87.2
GPT-5.1-Codex-Max
89
GPT-4o mini
87
GPT-5.1-Codex-Max is ahead overall, 87 to 43. The biggest single separator in this matchup is MMLU, where the scores are 98 and 82.
GPT-4o mini has the edge for knowledge tasks in this comparison, averaging 82 versus 81.5. 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 78.7. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
GPT-5.1-Codex-Max has the edge for multilingual tasks in this comparison, averaging 89 versus 87. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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