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
60
GPT-4.1 nano
28
GPT-4.1 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
+16.0 difference
Inst. Following
+4.2 difference
GPT-4.1
GPT-4.1 nano
$2 / $8
$0.1 / $0.4
108 t/s
181 t/s
1.02s
0.63s
1M
1M
GPT-4.1 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 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 is clearly ahead on the provisional aggregate, 60 to 28. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-4.1's sharpest advantage is in knowledge, where it averages 66.3 against 50.3. The single biggest benchmark swing on the page is GPQA, 66.3% to 50.3%.
GPT-4.1 is also the more expensive model on tokens at $2.00 input / $8.00 output per 1M tokens, versus $0.10 input / $0.40 output per 1M tokens for GPT-4.1 nano. That is roughly 20.0x on output cost alone.
GPT-4.1 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 is ahead on BenchLM's provisional leaderboard 60 to 28.
GPT-4.1 has the edge for knowledge tasks in this comparison, averaging 66.3 versus 50.3. Inside this category, GPQA is the benchmark that creates the most daylight between them.
GPT-4.1 has the edge for instruction following in this comparison, averaging 87.4 versus 83.2. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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