Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GPT-4.1 mini
47
GPT-5.3 Codex
89
Pick GPT-5.3 Codex if you want the stronger benchmark profile. GPT-4.1 mini only becomes the better choice if you want the cheaper token bill or you need the larger 1M context window.
Coding
+39.5 difference
GPT-4.1 mini
GPT-5.3 Codex
$0.4 / $1.6
$1.75 / $14
80 t/s
79 t/s
0.76s
88.26s
1M
400K
Pick GPT-5.3 Codex if you want the stronger benchmark profile. GPT-4.1 mini only becomes the better choice if you want the cheaper token bill or you need the larger 1M context window.
GPT-5.3 Codex is clearly ahead on the provisional aggregate, 89 to 47. 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 63.1 against 23.6. The single biggest benchmark swing on the page is SWE-bench Verified, 23.6% to 85%.
GPT-5.3 Codex is also the more expensive model on tokens at $1.75 input / $14.00 output per 1M tokens, versus $0.40 input / $1.60 output per 1M tokens for GPT-4.1 mini. That is roughly 8.8x on output cost alone. GPT-5.3 Codex is the reasoning model in the pair, while GPT-4.1 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-4.1 mini gives you the larger context window at 1M, compared with 400K for GPT-5.3 Codex.
GPT-5.3 Codex is ahead on BenchLM's provisional leaderboard, 89 to 47. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 23.6% and 85%.
GPT-5.3 Codex has the edge for coding in this comparison, averaging 63.1 versus 23.6. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
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