Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
DeepSeek LLM 2.0 is clearly ahead on the aggregate, 62 to 33. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
DeepSeek LLM 2.0's sharpest advantage is in mathematics, where it averages 80.8 against 42.3. The single biggest benchmark swing on the page is HumanEval, 73 to 17.
LFM2.5-1.2B-Thinking is the reasoning model in the pair, while DeepSeek LLM 2.0 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 LLM 2.0 gives you the larger context window at 128K, compared with 32K for LFM2.5-1.2B-Thinking.
Pick DeepSeek LLM 2.0 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 LLM 2.0
57.9
LFM2.5-1.2B-Thinking
34.1
DeepSeek LLM 2.0
42.9
LFM2.5-1.2B-Thinking
8.2
DeepSeek LLM 2.0
64.5
LFM2.5-1.2B-Thinking
32.4
DeepSeek LLM 2.0
73.6
LFM2.5-1.2B-Thinking
38.4
DeepSeek LLM 2.0
57.5
LFM2.5-1.2B-Thinking
27
DeepSeek LLM 2.0
85
LFM2.5-1.2B-Thinking
72
DeepSeek LLM 2.0
78.8
LFM2.5-1.2B-Thinking
60.7
DeepSeek LLM 2.0
80.8
LFM2.5-1.2B-Thinking
42.3
DeepSeek LLM 2.0 is ahead overall, 62 to 33. The biggest single separator in this matchup is HumanEval, where the scores are 73 and 17.
DeepSeek LLM 2.0 has the edge for knowledge tasks in this comparison, averaging 57.5 versus 27. Inside this category, MMLU is the benchmark that creates the most daylight between them.
DeepSeek LLM 2.0 has the edge for coding in this comparison, averaging 42.9 versus 8.2. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
DeepSeek LLM 2.0 has the edge for math in this comparison, averaging 80.8 versus 42.3. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
DeepSeek LLM 2.0 has the edge for reasoning in this comparison, averaging 73.6 versus 38.4. Inside this category, SimpleQA is the benchmark that creates the most daylight between them.
DeepSeek LLM 2.0 has the edge for agentic tasks in this comparison, averaging 57.9 versus 34.1. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
DeepSeek LLM 2.0 has the edge for multimodal and grounded tasks in this comparison, averaging 64.5 versus 32.4. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
DeepSeek LLM 2.0 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 LLM 2.0 has the edge for multilingual tasks in this comparison, averaging 78.8 versus 60.7. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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