Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Ministral 3 14B is clearly ahead on the aggregate, 55 to 49. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Ministral 3 14B's sharpest advantage is in mathematics, where it averages 69.7 against 9.8. The single biggest benchmark swing on the page is AIME 2024, 70 to 9.8. GPT-4.1 nano does hit back in reasoning, 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 14B. 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 14B.
Pick Ministral 3 14B if you want the stronger benchmark profile. GPT-4.1 nano only becomes the better choice if reasoning is the priority or you need the larger 1M context window.
Ministral 3 14B
48.4
GPT-4.1 nano
47.4
Ministral 3 14B
33
GPT-4.1 nano
18
Ministral 3 14B
70.5
GPT-4.1 nano
59.3
Ministral 3 14B
63.6
GPT-4.1 nano
74.1
Ministral 3 14B
50.1
GPT-4.1 nano
50.7
Ministral 3 14B
80
GPT-4.1 nano
83.2
Ministral 3 14B
76.8
GPT-4.1 nano
59
Ministral 3 14B
69.7
GPT-4.1 nano
9.8
Ministral 3 14B is ahead overall, 55 to 49. The biggest single separator in this matchup is AIME 2024, where the scores are 70 and 9.8.
GPT-4.1 nano has the edge for knowledge tasks in this comparison, averaging 50.7 versus 50.1. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Ministral 3 14B has the edge for coding in this comparison, averaging 33 versus 18. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
Ministral 3 14B has the edge for math in this comparison, averaging 69.7 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 63.6. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
Ministral 3 14B has the edge for agentic tasks in this comparison, averaging 48.4 versus 47.4. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
Ministral 3 14B has the edge for multimodal and grounded tasks in this comparison, averaging 70.5 versus 59.3. Inside this category, MMMU-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 80. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Ministral 3 14B has the edge for multilingual tasks in this comparison, averaging 76.8 versus 59. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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