Head-to-head comparison across 3benchmark 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 mini
47
GPT-4.1 makes more sense if coding is the priority, while GPT-4.1 mini is the cleaner fit if instruction following is the priority or you want the cheaper token bill.
Coding
+31.0 difference
Knowledge
+2.1 difference
Inst. Following
+1.1 difference
GPT-4.1
GPT-4.1 mini
$2 / $8
$0.4 / $1.6
108 t/s
80 t/s
1.02s
0.76s
1M
1M
GPT-4.1 makes more sense if coding is the priority, while GPT-4.1 mini is the cleaner fit if instruction following is the priority or you want the cheaper token bill.
GPT-4.1 and GPT-4.1 mini 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 47. 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 coding, where it averages 54.6 against 23.6. The single biggest benchmark swing on the page is SWE-bench Verified, 54.6% to 23.6%. GPT-4.1 mini does hit back in instruction following, so the answer changes if that is the part of the workload you care about most.
GPT-4.1 is also the more expensive model on tokens at $2.00 input / $8.00 output per 1M tokens, versus $0.40 input / $1.60 output per 1M tokens for GPT-4.1 mini. That is roughly 5.0x on output cost alone.
GPT-4.1 and GPT-4.1 mini 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 47.
GPT-4.1 has the edge for knowledge tasks in this comparison, averaging 66.3 versus 64.2. Inside this category, MMLU is the benchmark that creates the most daylight between them.
GPT-4.1 has the edge for coding in this comparison, averaging 54.6 versus 23.6. Inside this category, SWE-bench Verified 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 87.4. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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