Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-4.1 nano is clearly ahead on the aggregate, 49 to 27. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-4.1 nano's sharpest advantage is in reasoning, where it averages 74.1 against 30.1. The single biggest benchmark swing on the page is MMLU, 80.1 to 24. Ministral 3 3B does hit back in mathematics, so the answer changes if that is the part of the workload you care about most.
GPT-4.1 nano is also the more expensive model on tokens at $0.10 input / $0.40 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Ministral 3 3B. That is roughly Infinityx on output cost alone. GPT-4.1 nano gives you the larger context window at 1M, compared with 128K for Ministral 3 3B.
Pick GPT-4.1 nano if you want the stronger benchmark profile. Ministral 3 3B only becomes the better choice if mathematics is the priority or you want the cheaper token bill.
GPT-4.1 nano
47.4
Ministral 3 3B
22.9
GPT-4.1 nano
18
Ministral 3 3B
6.2
GPT-4.1 nano
59.3
Ministral 3 3B
30.4
GPT-4.1 nano
74.1
Ministral 3 3B
30.1
GPT-4.1 nano
50.7
Ministral 3 3B
24.5
GPT-4.1 nano
83.2
Ministral 3 3B
67
GPT-4.1 nano
59
Ministral 3 3B
59.7
GPT-4.1 nano
9.8
Ministral 3 3B
36
GPT-4.1 nano is ahead overall, 49 to 27. The biggest single separator in this matchup is MMLU, where the scores are 80.1 and 24.
GPT-4.1 nano has the edge for knowledge tasks in this comparison, averaging 50.7 versus 24.5. Inside this category, MMLU is the benchmark that creates the most daylight between them.
GPT-4.1 nano has the edge for coding in this comparison, averaging 18 versus 6.2. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
Ministral 3 3B has the edge for math in this comparison, averaging 36 versus 9.8. Inside this category, AIME 2024 is the benchmark that creates the most daylight between them.
GPT-4.1 nano has the edge for reasoning in this comparison, averaging 74.1 versus 30.1. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
GPT-4.1 nano has the edge for agentic tasks in this comparison, averaging 47.4 versus 22.9. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
GPT-4.1 nano has the edge for multimodal and grounded tasks in this comparison, averaging 59.3 versus 30.4. Inside this category, OfficeQA Pro is the benchmark that creates the most daylight between them.
GPT-4.1 nano has the edge for instruction following in this comparison, averaging 83.2 versus 67. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Ministral 3 3B has the edge for multilingual tasks in this comparison, averaging 59.7 versus 59. GPT-4.1 nano stays close enough that the answer can still flip depending on your workload.
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