Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
DeepSeek V3.2 (Thinking) is clearly ahead on the aggregate, 70 to 30. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
DeepSeek V3.2 (Thinking)'s sharpest advantage is in reasoning, where it averages 80.6 against 32.1. The single biggest benchmark swing on the page is HumanEval, 79 to 14.
DeepSeek V3.2 (Thinking) 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 V3.2 (Thinking) gives you the larger context window at 128K, compared with 32K for LFM2.5-1.2B-Instruct.
Pick DeepSeek V3.2 (Thinking) if you want the stronger benchmark profile. LFM2.5-1.2B-Instruct only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
DeepSeek V3.2 (Thinking)
69.4
LFM2.5-1.2B-Instruct
25.7
DeepSeek V3.2 (Thinking)
51.2
LFM2.5-1.2B-Instruct
7.2
DeepSeek V3.2 (Thinking)
71
LFM2.5-1.2B-Instruct
32.4
DeepSeek V3.2 (Thinking)
80.6
LFM2.5-1.2B-Instruct
32.1
DeepSeek V3.2 (Thinking)
64.4
LFM2.5-1.2B-Instruct
26
DeepSeek V3.2 (Thinking)
85
LFM2.5-1.2B-Instruct
80
DeepSeek V3.2 (Thinking)
80.8
LFM2.5-1.2B-Instruct
60.7
DeepSeek V3.2 (Thinking)
85.1
LFM2.5-1.2B-Instruct
37
DeepSeek V3.2 (Thinking) is ahead overall, 70 to 30. The biggest single separator in this matchup is HumanEval, where the scores are 79 and 14.
DeepSeek V3.2 (Thinking) has the edge for knowledge tasks in this comparison, averaging 64.4 versus 26. Inside this category, MMLU is the benchmark that creates the most daylight between them.
DeepSeek V3.2 (Thinking) has the edge for coding in this comparison, averaging 51.2 versus 7.2. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
DeepSeek V3.2 (Thinking) has the edge for math in this comparison, averaging 85.1 versus 37. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
DeepSeek V3.2 (Thinking) has the edge for reasoning in this comparison, averaging 80.6 versus 32.1. Inside this category, SimpleQA is the benchmark that creates the most daylight between them.
DeepSeek V3.2 (Thinking) has the edge for agentic tasks in this comparison, averaging 69.4 versus 25.7. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
DeepSeek V3.2 (Thinking) has the edge for multimodal and grounded tasks in this comparison, averaging 71 versus 32.4. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
DeepSeek V3.2 (Thinking) has the edge for instruction following in this comparison, averaging 85 versus 80. Inside this category, IFEval is the benchmark that creates the most daylight between them.
DeepSeek V3.2 (Thinking) has the edge for multilingual tasks in this comparison, averaging 80.8 versus 60.7. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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