Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-4.1 mini has the cleaner overall profile here, landing at 58 versus 55. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
GPT-4.1 mini's sharpest advantage is in reasoning, where it averages 80.9 against 63.6. The single biggest benchmark swing on the page is AIME 2024, 23.1 to 70. Ministral 3 14B does hit back in mathematics, so the answer changes if that is the part of the workload you care about most.
GPT-4.1 mini is also the more expensive model on tokens at $0.40 input / $1.60 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 mini gives you the larger context window at 1M, compared with 128K for Ministral 3 14B.
Pick GPT-4.1 mini if you want the stronger benchmark profile. Ministral 3 14B only becomes the better choice if mathematics is the priority or you want the cheaper token bill.
GPT-4.1 mini
56.5
Ministral 3 14B
48.4
GPT-4.1 mini
28.8
Ministral 3 14B
33
GPT-4.1 mini
69.6
Ministral 3 14B
70.5
GPT-4.1 mini
80.9
Ministral 3 14B
63.6
GPT-4.1 mini
62.4
Ministral 3 14B
50.1
GPT-4.1 mini
88.5
Ministral 3 14B
80
GPT-4.1 mini
72
Ministral 3 14B
76.8
GPT-4.1 mini
23.1
Ministral 3 14B
69.7
GPT-4.1 mini is ahead overall, 58 to 55. The biggest single separator in this matchup is AIME 2024, where the scores are 23.1 and 70.
GPT-4.1 mini has the edge for knowledge tasks in this comparison, averaging 62.4 versus 50.1. Inside this category, MMLU is the benchmark that creates the most daylight between them.
Ministral 3 14B has the edge for coding in this comparison, averaging 33 versus 28.8. Inside this category, SWE-bench Verified 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 23.1. Inside this category, AIME 2024 is the benchmark that creates the most daylight between them.
GPT-4.1 mini has the edge for reasoning in this comparison, averaging 80.9 versus 63.6. Inside this category, MRCRv2 is the benchmark that creates the most daylight between them.
GPT-4.1 mini has the edge for agentic tasks in this comparison, averaging 56.5 versus 48.4. 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 69.6. Inside this category, MMMU-Pro 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 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 72. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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