Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
GPT-5.2-Codex is clearly ahead on the aggregate, 88 to 43. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.2-Codex's sharpest advantage is in multilingual, where it averages 91 against 87. The single biggest benchmark swing on the page is MMLU, 99 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-5.2-Codex 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-5.2-Codex 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-5.2-Codex gives you the larger context window at 400K, compared with 128K for GPT-4o mini.
Pick GPT-5.2-Codex 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-5.2-Codex
81.7
GPT-4o mini
82
GPT-5.2-Codex
79
GPT-4o mini
87.2
GPT-5.2-Codex
91
GPT-4o mini
87
GPT-5.2-Codex is ahead overall, 88 to 43. The biggest single separator in this matchup is MMLU, where the scores are 99 and 82.
GPT-4o mini has the edge for knowledge tasks in this comparison, averaging 82 versus 81.7. 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 79. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
GPT-5.2-Codex has the edge for multilingual tasks in this comparison, averaging 91 versus 87. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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