Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
GPT-4.1 finishes one point ahead overall, 43 to 42. That is enough to call, but not enough to treat as a blowout. This matchup comes down to a few meaningful edges rather than one model dominating the board.
GPT-4.1's sharpest advantage is in knowledge, where it averages 78.3 against 38.7. The single biggest benchmark swing on the page is MMLU, 90.2 to 47. Llama 4 Scout 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 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.
Pick GPT-4.1 if you want the stronger benchmark profile. Llama 4 Scout only becomes the better choice if mathematics is the priority or you want the cheaper token bill.
GPT-4.1
78.3
Llama 4 Scout
38.7
GPT-4.1
54.6
Llama 4 Scout
20.7
GPT-4.1
26.4
Llama 4 Scout
47.4
GPT-4.1
87.4
Llama 4 Scout
68
GPT-4.1 is ahead overall, 43 to 42. The biggest single separator in this matchup is MMLU, where the scores are 90.2 and 47.
GPT-4.1 has the edge for knowledge tasks in this comparison, averaging 78.3 versus 38.7. 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 54.6 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 26.4. Inside this category, AIME 2024 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.
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