Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Llama 4 Scout is clearly ahead on the aggregate, 42 to 38. 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 multimodal & grounded, where it averages 57.8 against 41.7. The single biggest benchmark swing on the page is MMMU-Pro, 60 to 39. LFM2-24B-A2B does hit back in coding, so the answer changes if that is the part of the workload you care about most.
LFM2-24B-A2B is also the more expensive model on tokens at $0.03 input / $0.12 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 32K for LFM2-24B-A2B.
Pick Llama 4 Scout if you want the stronger benchmark profile. LFM2-24B-A2B only becomes the better choice if coding is the priority.
Llama 4 Scout
40.6
LFM2-24B-A2B
33.4
Llama 4 Scout
12.9
LFM2-24B-A2B
18
Llama 4 Scout
57.8
LFM2-24B-A2B
41.7
Llama 4 Scout
55
LFM2-24B-A2B
46.6
Llama 4 Scout
35.6
LFM2-24B-A2B
35.6
Llama 4 Scout
68
LFM2-24B-A2B
68
Llama 4 Scout
59.8
LFM2-24B-A2B
61.4
Llama 4 Scout
51
LFM2-24B-A2B
50.4
Llama 4 Scout is ahead overall, 42 to 38. The biggest single separator in this matchup is MMMU-Pro, where the scores are 60 and 39.
Llama 4 Scout and LFM2-24B-A2B are effectively tied for knowledge tasks here, both landing at 35.6 on average.
LFM2-24B-A2B has the edge for coding in this comparison, averaging 18 versus 12.9. 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 51 versus 50.4. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
Llama 4 Scout has the edge for reasoning in this comparison, averaging 55 versus 46.6. Inside this category, MRCRv2 is the benchmark that creates the most daylight between them.
Llama 4 Scout has the edge for agentic tasks in this comparison, averaging 40.6 versus 33.4. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
Llama 4 Scout has the edge for multimodal and grounded tasks in this comparison, averaging 57.8 versus 41.7. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Llama 4 Scout and LFM2-24B-A2B are effectively tied for instruction following here, both landing at 68 on average.
LFM2-24B-A2B has the edge for multilingual tasks in this comparison, averaging 61.4 versus 59.8. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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