Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-4.1 mini is clearly ahead on the aggregate, 58 to 36. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-4.1 mini's sharpest advantage is in reasoning, where it averages 80.9 against 42.1. The single biggest benchmark swing on the page is MMLU, 87.5 to 30. Ministral 3 8B (Reasoning) 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 8B (Reasoning). That is roughly Infinityx on output cost alone. Ministral 3 8B (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 8B (Reasoning).
Pick GPT-4.1 mini if you want the stronger benchmark profile. Ministral 3 8B (Reasoning) 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 8B (Reasoning)
38.5
GPT-4.1 mini
28.8
Ministral 3 8B (Reasoning)
15.2
GPT-4.1 mini
69.6
Ministral 3 8B (Reasoning)
33.4
GPT-4.1 mini
80.9
Ministral 3 8B (Reasoning)
42.1
GPT-4.1 mini
62.4
Ministral 3 8B (Reasoning)
30
GPT-4.1 mini
88.5
Ministral 3 8B (Reasoning)
70
GPT-4.1 mini
72
Ministral 3 8B (Reasoning)
61.7
GPT-4.1 mini
23.1
Ministral 3 8B (Reasoning)
47.8
GPT-4.1 mini is ahead overall, 58 to 36. The biggest single separator in this matchup is MMLU, where the scores are 87.5 and 30.
GPT-4.1 mini has the edge for knowledge tasks in this comparison, averaging 62.4 versus 30. Inside this category, MMLU is the benchmark that creates the most daylight between them.
GPT-4.1 mini has the edge for coding in this comparison, averaging 28.8 versus 15.2. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
Ministral 3 8B (Reasoning) has the edge for math in this comparison, averaging 47.8 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 42.1. Inside this category, LongBench v2 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 38.5. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
GPT-4.1 mini has the edge for multimodal and grounded tasks in this comparison, averaging 69.6 versus 33.4. 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 70. Inside this category, IFEval is the benchmark that creates the most daylight between them.
GPT-4.1 mini has the edge for multilingual tasks in this comparison, averaging 72 versus 61.7. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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