Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
DeepSeek LLM 2.0 is clearly ahead on the aggregate, 70 to 56. 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 coding, where it averages 52.7 against 49.3. The single biggest benchmark swing on the page is IFEval, 85 to 93.9. o3-mini does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
o3-mini 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. o3-mini gives you the larger context window at 200K, compared with 128K for DeepSeek LLM 2.0.
Pick DeepSeek LLM 2.0 if you want the stronger benchmark profile. o3-mini only becomes the better choice if knowledge is the priority or you need the larger 200K context window.
DeepSeek LLM 2.0
65.2
o3-mini
82.1
DeepSeek LLM 2.0
52.7
o3-mini
49.3
DeepSeek LLM 2.0
79.5
o3-mini
87.3
DeepSeek LLM 2.0
85
o3-mini
93.9
DeepSeek LLM 2.0 is ahead overall, 70 to 56. The biggest single separator in this matchup is IFEval, where the scores are 85 and 93.9.
o3-mini has the edge for knowledge tasks in this comparison, averaging 82.1 versus 65.2. 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 52.7 versus 49.3. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
o3-mini has the edge for math in this comparison, averaging 87.3 versus 79.5. Inside this category, AIME 2024 is the benchmark that creates the most daylight between them.
o3-mini has the edge for instruction following in this comparison, averaging 93.9 versus 85. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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