Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Sibling matchup inside the GPT-4.1 family.
GPT-4.1 mini
47
GPT-4.1 nano
28
GPT-4.1 mini makes more sense if knowledge is the priority, while GPT-4.1 nano is the cleaner fit if you want the cheaper token bill.
Knowledge
+13.9 difference
Inst. Following
+5.3 difference
GPT-4.1 mini
GPT-4.1 nano
$0.4 / $1.6
$0.1 / $0.4
80 t/s
181 t/s
0.76s
0.63s
1M
1M
GPT-4.1 mini makes more sense if knowledge is the priority, while GPT-4.1 nano is the cleaner fit if you want the cheaper token bill.
GPT-4.1 mini and GPT-4.1 nano sit in the same GPT-4.1 family. This page is less about two unrelated model lineages and more about how the siblings trade off on benchmark shape, token costs, and practical limits like context window.
GPT-4.1 mini is clearly ahead on the provisional aggregate, 47 to 28. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-4.1 mini's sharpest advantage is in knowledge, where it averages 64.2 against 50.3. The single biggest benchmark swing on the page is GPQA, 64.2% to 50.3%.
GPT-4.1 mini is also the more expensive model on tokens at $0.40 input / $1.60 output per 1M tokens, versus $0.10 input / $0.40 output per 1M tokens for GPT-4.1 nano. That is roughly 4.0x on output cost alone.
GPT-4.1 mini and GPT-4.1 nano are sibling variants in the GPT-4.1 family, so the right pick depends on whether you value the better benchmark line, cheaper tokens, or the larger context window. GPT-4.1 mini is ahead on BenchLM's provisional leaderboard 47 to 28.
GPT-4.1 mini has the edge for knowledge tasks in this comparison, averaging 64.2 versus 50.3. Inside this category, GPQA is the benchmark that creates the most daylight between them.
GPT-4.1 mini has the edge for instruction following in this comparison, averaging 88.5 versus 83.2. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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