Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
GPT-5.3 Codex is clearly ahead on the aggregate, 92 to 39. 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 knowledge, where it averages 86.3 against 70.5. The single biggest benchmark swing on the page is GPQA, 97 to 56.1.
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 Phi-4. That is roughly Infinityx on output cost alone. GPT-5.3 Codex is the reasoning model in the pair, while Phi-4 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 16K for Phi-4.
Pick GPT-5.3 Codex if you want the stronger benchmark profile. Phi-4 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
Phi-4
70.5
GPT-5.3 Codex
88.3
Phi-4
82.6
GPT-5.3 Codex
96
Phi-4
80.6
GPT-5.3 Codex is ahead overall, 92 to 39. The biggest single separator in this matchup is GPQA, where the scores are 97 and 56.1.
GPT-5.3 Codex has the edge for knowledge tasks in this comparison, averaging 86.3 versus 70.5. Inside this category, GPQA 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 82.6. 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 80.6. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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