Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
DeepSeek V3.1 (Reasoning) has the cleaner overall profile here, landing at 41 versus 38. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
DeepSeek V3.1 (Reasoning)'s sharpest advantage is in agentic, where it averages 44.2 against 33.4. The single biggest benchmark swing on the page is HumanEval, 26 to 42. LFM2-24B-A2B does hit back in mathematics, so the answer changes if that is the part of the workload you care about most.
DeepSeek V3.1 (Reasoning) is the reasoning model in the pair, while LFM2-24B-A2B 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. DeepSeek V3.1 (Reasoning) gives you the larger context window at 128K, compared with 32K for LFM2-24B-A2B.
Pick DeepSeek V3.1 (Reasoning) if you want the stronger benchmark profile. LFM2-24B-A2B only becomes the better choice if mathematics is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
DeepSeek V3.1 (Reasoning)
44.2
LFM2-24B-A2B
33.4
DeepSeek V3.1 (Reasoning)
19.9
LFM2-24B-A2B
18
DeepSeek V3.1 (Reasoning)
41.5
LFM2-24B-A2B
41.7
DeepSeek V3.1 (Reasoning)
45.8
LFM2-24B-A2B
46.6
DeepSeek V3.1 (Reasoning)
32.8
LFM2-24B-A2B
35.6
DeepSeek V3.1 (Reasoning)
70
LFM2-24B-A2B
68
DeepSeek V3.1 (Reasoning)
62.1
LFM2-24B-A2B
61.4
DeepSeek V3.1 (Reasoning)
46.1
LFM2-24B-A2B
50.4
DeepSeek V3.1 (Reasoning) is ahead overall, 41 to 38. The biggest single separator in this matchup is HumanEval, where the scores are 26 and 42.
LFM2-24B-A2B has the edge for knowledge tasks in this comparison, averaging 35.6 versus 32.8. Inside this category, MMLU is the benchmark that creates the most daylight between them.
DeepSeek V3.1 (Reasoning) has the edge for coding in this comparison, averaging 19.9 versus 18. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
LFM2-24B-A2B has the edge for math in this comparison, averaging 50.4 versus 46.1. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
LFM2-24B-A2B has the edge for reasoning in this comparison, averaging 46.6 versus 45.8. Inside this category, SimpleQA is the benchmark that creates the most daylight between them.
DeepSeek V3.1 (Reasoning) has the edge for agentic tasks in this comparison, averaging 44.2 versus 33.4. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
LFM2-24B-A2B has the edge for multimodal and grounded tasks in this comparison, averaging 41.7 versus 41.5. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
DeepSeek V3.1 (Reasoning) has the edge for instruction following in this comparison, averaging 70 versus 68. Inside this category, IFEval is the benchmark that creates the most daylight between them.
DeepSeek V3.1 (Reasoning) has the edge for multilingual tasks in this comparison, averaging 62.1 versus 61.4. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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