Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
DeepSeek Coder 2.0 is clearly ahead on the aggregate, 73 to 31. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
DeepSeek Coder 2.0 is also the more expensive model on tokens at $0.27 input / $1.10 output per 1M tokens, versus $0.05 input / $0.40 output per 1M tokens for GPT-5 nano. That is roughly 2.8x on output cost alone. GPT-5 nano is the reasoning model in the pair, while DeepSeek Coder 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 Coder 2.0.
Pick DeepSeek Coder 2.0 if you want the stronger benchmark profile. GPT-5 nano only becomes the better choice if mathematics is the priority or you want the cheaper token bill.
DeepSeek Coder 2.0
66.3
GPT-5 nano
71.2
DeepSeek Coder 2.0
80.1
GPT-5 nano
85.2
DeepSeek Coder 2.0 is ahead overall, 73 to 31. The biggest single separator in this matchup is GPQA, where the scores are 79 and 71.2.
GPT-5 nano has the edge for knowledge tasks in this comparison, averaging 71.2 versus 66.3. 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 80.1. Inside this category, AIME 2025 is the benchmark that creates the most daylight between them.
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