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 33. 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 32.4. The single biggest benchmark swing on the page is MMMU-Pro, 60 to 27. LFM2.5-1.2B-Thinking does hit back in instruction following, so the answer changes if that is the part of the workload you care about most.
LFM2.5-1.2B-Thinking is the reasoning model in the pair, while Llama 4 Scout 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. Llama 4 Scout gives you the larger context window at 10M, compared with 32K for LFM2.5-1.2B-Thinking.
Pick Llama 4 Scout if you want the stronger benchmark profile. LFM2.5-1.2B-Thinking only becomes the better choice if instruction following is the priority or you want the stronger reasoning-first profile.
Llama 4 Scout
40.6
LFM2.5-1.2B-Thinking
34.1
Llama 4 Scout
12.9
LFM2.5-1.2B-Thinking
8.2
Llama 4 Scout
57.8
LFM2.5-1.2B-Thinking
32.4
Llama 4 Scout
55
LFM2.5-1.2B-Thinking
38.4
Llama 4 Scout
35.6
LFM2.5-1.2B-Thinking
27
Llama 4 Scout
68
LFM2.5-1.2B-Thinking
72
Llama 4 Scout
59.8
LFM2.5-1.2B-Thinking
60.7
Llama 4 Scout
51
LFM2.5-1.2B-Thinking
42.3
Llama 4 Scout is ahead overall, 42 to 33. The biggest single separator in this matchup is MMMU-Pro, where the scores are 60 and 27.
Llama 4 Scout has the edge for knowledge tasks in this comparison, averaging 35.6 versus 27. Inside this category, MMLU is the benchmark that creates the most daylight between them.
Llama 4 Scout has the edge for coding in this comparison, averaging 12.9 versus 8.2. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
Llama 4 Scout has the edge for math in this comparison, averaging 51 versus 42.3. 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 38.4. Inside this category, LongBench v2 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 34.1. 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 32.4. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
LFM2.5-1.2B-Thinking has the edge for instruction following in this comparison, averaging 72 versus 68. Inside this category, IFEval is the benchmark that creates the most daylight between them.
LFM2.5-1.2B-Thinking has the edge for multilingual tasks in this comparison, averaging 60.7 versus 59.8. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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