Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GPT-4.1 mini
47
GPT-5.4
88
Verified leaderboard positions: GPT-4.1 mini unranked · GPT-5.4 #13
Pick GPT-5.4 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 would rather avoid the extra latency and token burn of a reasoning model.
Coding
+34.1 difference
Knowledge
+1.9 difference
GPT-4.1 mini
GPT-5.4
$0.4 / $1.6
$2.5 / $15
80 t/s
74 t/s
0.76s
151.79s
1M
1.05M
Pick GPT-5.4 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 would rather avoid the extra latency and token burn of a reasoning model.
GPT-5.4 is clearly ahead on the provisional aggregate, 88 to 47. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.4's sharpest advantage is in coding, where it averages 57.7 against 23.6. The single biggest benchmark swing on the page is GPQA, 64.2% to 92.8%.
GPT-5.4 is also the more expensive model on tokens at $2.50 input / $15.00 output per 1M tokens, versus $0.40 input / $1.60 output per 1M tokens for GPT-4.1 mini. That is roughly 9.4x on output cost alone. GPT-5.4 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-5.4 gives you the larger context window at 1.05M, compared with 1M for GPT-4.1 mini.
GPT-5.4 is ahead on BenchLM's provisional leaderboard, 88 to 47. The biggest single separator in this matchup is GPQA, where the scores are 64.2% and 92.8%.
GPT-5.4 has the edge for knowledge tasks in this comparison, averaging 66.1 versus 64.2. Inside this category, GPQA is the benchmark that creates the most daylight between them.
GPT-5.4 has the edge for coding in this comparison, averaging 57.7 versus 23.6. GPT-4.1 mini stays close enough that the answer can still flip depending on your workload.
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