Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
DeepSeek Coder 2.0 is clearly ahead on the aggregate, 73 to 23. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
DeepSeek Coder 2.0's sharpest advantage is in mathematics, where it averages 80.1 against 9.8. The single biggest benchmark swing on the page is AIME 2024, 83 to 9.8.
DeepSeek Coder 2.0 is also the more expensive model on tokens at $0.27 input / $1.10 output per 1M tokens, versus $0.10 input / $0.40 output per 1M tokens for GPT-4.1 nano. That is roughly 2.8x on output cost alone. GPT-4.1 nano gives you the larger context window at 1M, compared with 128K for DeepSeek Coder 2.0.
Pick DeepSeek Coder 2.0 if you want the stronger benchmark profile. GPT-4.1 nano only becomes the better choice if you want the cheaper token bill or you need the larger 1M context window.
DeepSeek Coder 2.0
66.3
GPT-4.1 nano
65.2
DeepSeek Coder 2.0
80.1
GPT-4.1 nano
9.8
DeepSeek Coder 2.0
86
GPT-4.1 nano
83.2
DeepSeek Coder 2.0 is ahead overall, 73 to 23. The biggest single separator in this matchup is AIME 2024, where the scores are 83 and 9.8.
DeepSeek Coder 2.0 has the edge for knowledge tasks in this comparison, averaging 66.3 versus 65.2. Inside this category, GPQA is the benchmark that creates the most daylight between them.
DeepSeek Coder 2.0 has the edge for math in this comparison, averaging 80.1 versus 9.8. Inside this category, AIME 2024 is the benchmark that creates the most daylight between them.
DeepSeek Coder 2.0 has the edge for instruction following in this comparison, averaging 86 versus 83.2. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Get notified when new models drop, benchmark scores change, or the leaderboard shifts. One email per week.
Free. No spam. Unsubscribe anytime. We only store derived location metadata for consent routing.