Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-4.1 is clearly ahead on the aggregate, 65 to 31. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-4.1's sharpest advantage is in reasoning, where it averages 80.9 against 35.3. The single biggest benchmark swing on the page is MMLU, 90.2 to 25. Ministral 3 3B (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 is also the more expensive model on tokens at $2.00 input / $8.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Ministral 3 3B (Reasoning). That is roughly Infinityx on output cost alone. Ministral 3 3B (Reasoning) is the reasoning model in the pair, while GPT-4.1 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 gives you the larger context window at 1M, compared with 128K for Ministral 3 3B (Reasoning).
Pick GPT-4.1 if you want the stronger benchmark profile. Ministral 3 3B (Reasoning) only becomes the better choice if mathematics is the priority or you want the cheaper token bill.
GPT-4.1
64.7
Ministral 3 3B (Reasoning)
34
GPT-4.1
51.7
Ministral 3 3B (Reasoning)
7.2
GPT-4.1
73.6
Ministral 3 3B (Reasoning)
30.4
GPT-4.1
80.9
Ministral 3 3B (Reasoning)
35.3
GPT-4.1
63.3
Ministral 3 3B (Reasoning)
25.2
GPT-4.1
87.4
Ministral 3 3B (Reasoning)
68
GPT-4.1
69
Ministral 3 3B (Reasoning)
59.7
GPT-4.1
26.4
Ministral 3 3B (Reasoning)
40.9
GPT-4.1 is ahead overall, 65 to 31. The biggest single separator in this matchup is MMLU, where the scores are 90.2 and 25.
GPT-4.1 has the edge for knowledge tasks in this comparison, averaging 63.3 versus 25.2. Inside this category, MMLU is the benchmark that creates the most daylight between them.
GPT-4.1 has the edge for coding in this comparison, averaging 51.7 versus 7.2. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Ministral 3 3B (Reasoning) has the edge for math in this comparison, averaging 40.9 versus 26.4. Inside this category, AIME 2024 is the benchmark that creates the most daylight between them.
GPT-4.1 has the edge for reasoning in this comparison, averaging 80.9 versus 35.3. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
GPT-4.1 has the edge for agentic tasks in this comparison, averaging 64.7 versus 34. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
GPT-4.1 has the edge for multimodal and grounded tasks in this comparison, averaging 73.6 versus 30.4. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
GPT-4.1 has the edge for instruction following in this comparison, averaging 87.4 versus 68. Inside this category, IFEval is the benchmark that creates the most daylight between them.
GPT-4.1 has the edge for multilingual tasks in this comparison, averaging 69 versus 59.7. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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