Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Ministral 3 14B (Reasoning) has the cleaner overall profile here, landing at 60 versus 58. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
Ministral 3 14B (Reasoning)'s sharpest advantage is in mathematics, where it averages 75.2 against 23.1. The single biggest benchmark swing on the page is AIME 2024, 75 to 23.1. GPT-4.1 mini does hit back in reasoning, 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 (Reasoning). That is roughly Infinityx on output cost alone. Ministral 3 14B (Reasoning) is the reasoning model in the pair, while GPT-4.1 mini is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. GPT-4.1 mini gives you the larger context window at 1M, compared with 128K for Ministral 3 14B (Reasoning).
Pick Ministral 3 14B (Reasoning) if you want the stronger benchmark profile. GPT-4.1 mini only becomes the better choice if reasoning is the priority or you need the larger 1M context window.
Ministral 3 14B (Reasoning)
58.5
GPT-4.1 mini
56.5
Ministral 3 14B (Reasoning)
35
GPT-4.1 mini
28.8
Ministral 3 14B (Reasoning)
71.5
GPT-4.1 mini
69.6
Ministral 3 14B (Reasoning)
69.2
GPT-4.1 mini
80.9
Ministral 3 14B (Reasoning)
52.1
GPT-4.1 mini
62.4
Ministral 3 14B (Reasoning)
81
GPT-4.1 mini
88.5
Ministral 3 14B (Reasoning)
77.8
GPT-4.1 mini
72
Ministral 3 14B (Reasoning)
75.2
GPT-4.1 mini
23.1
Ministral 3 14B (Reasoning) is ahead overall, 60 to 58. The biggest single separator in this matchup is AIME 2024, where the scores are 75 and 23.1.
GPT-4.1 mini has the edge for knowledge tasks in this comparison, averaging 62.4 versus 52.1. Inside this category, MMLU is the benchmark that creates the most daylight between them.
Ministral 3 14B (Reasoning) has the edge for coding in this comparison, averaging 35 versus 28.8. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Ministral 3 14B (Reasoning) has the edge for math in this comparison, averaging 75.2 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 69.2. Inside this category, MRCRv2 is the benchmark that creates the most daylight between them.
Ministral 3 14B (Reasoning) has the edge for agentic tasks in this comparison, averaging 58.5 versus 56.5. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
Ministral 3 14B (Reasoning) has the edge for multimodal and grounded tasks in this comparison, averaging 71.5 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 81. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Ministral 3 14B (Reasoning) has the edge for multilingual tasks in this comparison, averaging 77.8 versus 72. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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