Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
GPT-5.3 Codex is clearly ahead on the aggregate, 92 to 43. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.3 Codex's sharpest advantage is in multilingual, where it averages 96 against 87. The single biggest benchmark swing on the page is MMLU, 99 to 82.
GPT-5.3 Codex 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-5.3 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-5.3 Codex gives you the larger context window at 400K, compared with 128K for GPT-4o mini.
Pick GPT-5.3 Codex if you want the stronger benchmark profile. GPT-4o mini only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.
GPT-5.3 Codex
86.3
GPT-4o mini
82
GPT-5.3 Codex
88.3
GPT-4o mini
87.2
GPT-5.3 Codex
96
GPT-4o mini
87
GPT-5.3 Codex is ahead overall, 92 to 43. The biggest single separator in this matchup is MMLU, where the scores are 99 and 82.
GPT-5.3 Codex has the edge for knowledge tasks in this comparison, averaging 86.3 versus 82. Inside this category, MMLU is the benchmark that creates the most daylight between them.
GPT-5.3 Codex has the edge for coding in this comparison, averaging 88.3 versus 87.2. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
GPT-5.3 Codex has the edge for multilingual tasks in this comparison, averaging 96 versus 87. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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