Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
Llama 4 Scout is clearly ahead on the aggregate, 42 to 35. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Llama 4 Scout's sharpest advantage is in mathematics, where it averages 47.4 against 23.1. The single biggest benchmark swing on the page is MMLU, 47 to 87.5. GPT-4.1 mini does hit back in knowledge, 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 Llama 4 Scout. That is roughly Infinityx on output cost alone. Llama 4 Scout gives you the larger context window at 10M, compared with 1M for GPT-4.1 mini.
Pick Llama 4 Scout if you want the stronger benchmark profile. GPT-4.1 mini only becomes the better choice if knowledge is the priority.
Llama 4 Scout
38.7
GPT-4.1 mini
75.9
Llama 4 Scout
20.7
GPT-4.1 mini
23.6
Llama 4 Scout
47.4
GPT-4.1 mini
23.1
Llama 4 Scout
68
GPT-4.1 mini
88.5
Llama 4 Scout is ahead overall, 42 to 35. The biggest single separator in this matchup is MMLU, where the scores are 47 and 87.5.
GPT-4.1 mini has the edge for knowledge tasks in this comparison, averaging 75.9 versus 38.7. 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 23.6 versus 20.7. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Llama 4 Scout has the edge for math in this comparison, averaging 47.4 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 instruction following in this comparison, averaging 88.5 versus 68. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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