Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
DeepSeek V3.2 is clearly ahead on the aggregate, 64 to 33. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
DeepSeek V3.2's sharpest advantage is in mathematics, where it averages 82.1 against 42.3. The single biggest benchmark swing on the page is HumanEval, 76 to 17.
LFM2.5-1.2B-Thinking is the reasoning model in the pair, while DeepSeek V3.2 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 V3.2 gives you the larger context window at 128K, compared with 32K for LFM2.5-1.2B-Thinking.
Pick DeepSeek V3.2 if you want the stronger benchmark profile. LFM2.5-1.2B-Thinking only becomes the better choice if you want the stronger reasoning-first profile.
DeepSeek V3.2
58.8
LFM2.5-1.2B-Thinking
34.1
DeepSeek V3.2
43.2
LFM2.5-1.2B-Thinking
8.2
DeepSeek V3.2
66
LFM2.5-1.2B-Thinking
32.4
DeepSeek V3.2
75.3
LFM2.5-1.2B-Thinking
38.4
DeepSeek V3.2
60
LFM2.5-1.2B-Thinking
27
DeepSeek V3.2
85
LFM2.5-1.2B-Thinking
72
DeepSeek V3.2
82.1
LFM2.5-1.2B-Thinking
60.7
DeepSeek V3.2
82.1
LFM2.5-1.2B-Thinking
42.3
DeepSeek V3.2 is ahead overall, 64 to 33. The biggest single separator in this matchup is HumanEval, where the scores are 76 and 17.
DeepSeek V3.2 has the edge for knowledge tasks in this comparison, averaging 60 versus 27. Inside this category, MMLU is the benchmark that creates the most daylight between them.
DeepSeek V3.2 has the edge for coding in this comparison, averaging 43.2 versus 8.2. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
DeepSeek V3.2 has the edge for math in this comparison, averaging 82.1 versus 42.3. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
DeepSeek V3.2 has the edge for reasoning in this comparison, averaging 75.3 versus 38.4. Inside this category, SimpleQA is the benchmark that creates the most daylight between them.
DeepSeek V3.2 has the edge for agentic tasks in this comparison, averaging 58.8 versus 34.1. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
DeepSeek V3.2 has the edge for multimodal and grounded tasks in this comparison, averaging 66 versus 32.4. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
DeepSeek V3.2 has the edge for instruction following in this comparison, averaging 85 versus 72. Inside this category, IFEval is the benchmark that creates the most daylight between them.
DeepSeek V3.2 has the edge for multilingual tasks in this comparison, averaging 82.1 versus 60.7. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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