Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
DeepSeek LLM 2.0 is clearly ahead on the aggregate, 70 to 31. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5 nano 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. GPT-5 nano gives you the larger context window at 400K, compared with 128K for DeepSeek LLM 2.0.
Pick DeepSeek LLM 2.0 if you want the stronger benchmark profile. GPT-5 nano only becomes the better choice if knowledge is the priority or you need the larger 400K context window.
DeepSeek LLM 2.0
65.2
GPT-5 nano
71.2
DeepSeek LLM 2.0
79.5
GPT-5 nano
85.2
DeepSeek LLM 2.0 is ahead overall, 70 to 31. The biggest single separator in this matchup is GPQA, where the scores are 78 and 71.2.
GPT-5 nano has the edge for knowledge tasks in this comparison, averaging 71.2 versus 65.2. Inside this category, GPQA is the benchmark that creates the most daylight between them.
GPT-5 nano has the edge for math in this comparison, averaging 85.2 versus 79.5. Inside this category, AIME 2025 is the benchmark that creates the most daylight between them.
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