Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
DeepSeek-R1 is clearly ahead on the aggregate, 43 to 33. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
DeepSeek-R1's sharpest advantage is in multimodal & grounded, where it averages 47.5 against 32.4. The single biggest benchmark swing on the page is HumanEval, 36 to 17. 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.
DeepSeek-R1 is also the more expensive model on tokens at $0.55 input / $2.19 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for LFM2.5-1.2B-Thinking. That is roughly Infinityx on output cost alone. DeepSeek-R1 gives you the larger context window at 128K, compared with 32K for LFM2.5-1.2B-Thinking.
Pick DeepSeek-R1 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 cheaper token bill.
DeepSeek-R1
44.5
LFM2.5-1.2B-Thinking
34.1
DeepSeek-R1
21.5
LFM2.5-1.2B-Thinking
8.2
DeepSeek-R1
47.5
LFM2.5-1.2B-Thinking
32.4
DeepSeek-R1
50.9
LFM2.5-1.2B-Thinking
38.4
DeepSeek-R1
37.9
LFM2.5-1.2B-Thinking
27
DeepSeek-R1
69
LFM2.5-1.2B-Thinking
72
DeepSeek-R1
60.4
LFM2.5-1.2B-Thinking
60.7
DeepSeek-R1
52.5
LFM2.5-1.2B-Thinking
42.3
DeepSeek-R1 is ahead overall, 43 to 33. The biggest single separator in this matchup is HumanEval, where the scores are 36 and 17.
DeepSeek-R1 has the edge for knowledge tasks in this comparison, averaging 37.9 versus 27. Inside this category, MMLU is the benchmark that creates the most daylight between them.
DeepSeek-R1 has the edge for coding in this comparison, averaging 21.5 versus 8.2. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
DeepSeek-R1 has the edge for math in this comparison, averaging 52.5 versus 42.3. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
DeepSeek-R1 has the edge for reasoning in this comparison, averaging 50.9 versus 38.4. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
DeepSeek-R1 has the edge for agentic tasks in this comparison, averaging 44.5 versus 34.1. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
DeepSeek-R1 has the edge for multimodal and grounded tasks in this comparison, averaging 47.5 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 69. 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 60.4. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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