Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
DeepSeek V3.1 (Reasoning) is clearly ahead on the aggregate, 41 to 30. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
DeepSeek V3.1 (Reasoning)'s sharpest advantage is in agentic, where it averages 44.2 against 25.7. The single biggest benchmark swing on the page is LongBench v2, 57 to 34. LFM2.5-1.2B-Instruct does hit back in instruction following, 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 LFM2.5-1.2B-Instruct 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 LFM2.5-1.2B-Instruct.
Pick DeepSeek V3.1 (Reasoning) if you want the stronger benchmark profile. LFM2.5-1.2B-Instruct only becomes the better choice if instruction following is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
DeepSeek V3.1 (Reasoning)
44.2
LFM2.5-1.2B-Instruct
25.7
DeepSeek V3.1 (Reasoning)
19.9
LFM2.5-1.2B-Instruct
7.2
DeepSeek V3.1 (Reasoning)
41.5
LFM2.5-1.2B-Instruct
32.4
DeepSeek V3.1 (Reasoning)
45.8
LFM2.5-1.2B-Instruct
32.1
DeepSeek V3.1 (Reasoning)
32.8
LFM2.5-1.2B-Instruct
26
DeepSeek V3.1 (Reasoning)
70
LFM2.5-1.2B-Instruct
80
DeepSeek V3.1 (Reasoning)
62.1
LFM2.5-1.2B-Instruct
60.7
DeepSeek V3.1 (Reasoning)
46.1
LFM2.5-1.2B-Instruct
37
DeepSeek V3.1 (Reasoning) is ahead overall, 41 to 30. The biggest single separator in this matchup is LongBench v2, where the scores are 57 and 34.
DeepSeek V3.1 (Reasoning) has the edge for knowledge tasks in this comparison, averaging 32.8 versus 26. Inside this category, HLE 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 7.2. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
DeepSeek V3.1 (Reasoning) has the edge for math in this comparison, averaging 46.1 versus 37. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
DeepSeek V3.1 (Reasoning) has the edge for reasoning in this comparison, averaging 45.8 versus 32.1. Inside this category, LongBench v2 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 25.7. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
DeepSeek V3.1 (Reasoning) has the edge for multimodal and grounded tasks in this comparison, averaging 41.5 versus 32.4. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
LFM2.5-1.2B-Instruct has the edge for instruction following in this comparison, averaging 80 versus 70. 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 60.7. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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