Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
GPT-5.3 Codex is clearly ahead on the aggregate, 92 to 32. 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 coding, where it averages 88.3 against 70.1. The single biggest benchmark swing on the page is MMLU, 99 to 73.7.
GPT-5.3 Codex is also the more expensive model on tokens at $2.50 input / $10.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for DBRX Instruct. That is roughly Infinityx on output cost alone. GPT-5.3 Codex is the reasoning model in the pair, while DBRX Instruct 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 32K for DBRX Instruct.
Pick GPT-5.3 Codex if you want the stronger benchmark profile. DBRX Instruct 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
DBRX Instruct
73.7
GPT-5.3 Codex
88.3
DBRX Instruct
70.1
GPT-5.3 Codex is ahead overall, 92 to 32. The biggest single separator in this matchup is MMLU, where the scores are 99 and 73.7.
GPT-5.3 Codex has the edge for knowledge tasks in this comparison, averaging 86.3 versus 73.7. 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 70.1. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
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