Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Llama 4 Maverick is clearly ahead on the aggregate, 43 to 38. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Llama 4 Maverick's sharpest advantage is in multimodal & grounded, where it averages 56.8 against 41.7. The single biggest benchmark swing on the page is MMMU-Pro, 59 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 Maverick. That is roughly Infinityx on output cost alone. Llama 4 Maverick gives you the larger context window at 1M, compared with 32K for LFM2-24B-A2B.
Pick Llama 4 Maverick if you want the stronger benchmark profile. LFM2-24B-A2B only becomes the better choice if coding is the priority.
Llama 4 Maverick
40.9
LFM2-24B-A2B
33.4
Llama 4 Maverick
15.7
LFM2-24B-A2B
18
Llama 4 Maverick
56.8
LFM2-24B-A2B
41.7
Llama 4 Maverick
54
LFM2-24B-A2B
46.6
Llama 4 Maverick
36.5
LFM2-24B-A2B
35.6
Llama 4 Maverick
68
LFM2-24B-A2B
68
Llama 4 Maverick
59.8
LFM2-24B-A2B
61.4
Llama 4 Maverick
51.3
LFM2-24B-A2B
50.4
Llama 4 Maverick is ahead overall, 43 to 38. The biggest single separator in this matchup is MMMU-Pro, where the scores are 59 and 39.
Llama 4 Maverick has the edge for knowledge tasks in this comparison, averaging 36.5 versus 35.6. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
LFM2-24B-A2B has the edge for coding in this comparison, averaging 18 versus 15.7. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Llama 4 Maverick has the edge for math in this comparison, averaging 51.3 versus 50.4. Inside this category, MATH-500 is the benchmark that creates the most daylight between them.
Llama 4 Maverick has the edge for reasoning in this comparison, averaging 54 versus 46.6. Inside this category, MRCRv2 is the benchmark that creates the most daylight between them.
Llama 4 Maverick has the edge for agentic tasks in this comparison, averaging 40.9 versus 33.4. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
Llama 4 Maverick has the edge for multimodal and grounded tasks in this comparison, averaging 56.8 versus 41.7. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Llama 4 Maverick 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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