Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-5.2 Pro is clearly ahead on the aggregate, 90 to 66. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.2 Pro's sharpest advantage is in multimodal & grounded, where it averages 96 against 58.6. The single biggest benchmark swing on the page is MMMU-Pro, 96 to 50.
GPT-5.2 Pro is also the more expensive model on tokens at $25.00 input / $150.00 output per 1M tokens, versus $0.27 input / $1.10 output per 1M tokens for DeepSeek Coder 2.0. That is roughly 136.4x on output cost alone. GPT-5.2 Pro 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-5.2 Pro gives you the larger context window at 400K, compared with 128K for DeepSeek Coder 2.0.
Pick GPT-5.2 Pro if you want the stronger benchmark profile. DeepSeek Coder 2.0 only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.
GPT-5.2 Pro
85.9
DeepSeek Coder 2.0
67.5
GPT-5.2 Pro
84.8
DeepSeek Coder 2.0
52.8
GPT-5.2 Pro
96
DeepSeek Coder 2.0
58.6
GPT-5.2 Pro
95.2
DeepSeek Coder 2.0
75.5
GPT-5.2 Pro
81.5
DeepSeek Coder 2.0
59.6
GPT-5.2 Pro
95
DeepSeek Coder 2.0
86
GPT-5.2 Pro
93.4
DeepSeek Coder 2.0
79.8
GPT-5.2 Pro
98.2
DeepSeek Coder 2.0
80.5
GPT-5.2 Pro is ahead overall, 90 to 66. The biggest single separator in this matchup is MMMU-Pro, where the scores are 96 and 50.
GPT-5.2 Pro has the edge for knowledge tasks in this comparison, averaging 81.5 versus 59.6. Inside this category, HLE is the benchmark that creates the most daylight between them.
GPT-5.2 Pro has the edge for coding in this comparison, averaging 84.8 versus 52.8. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
GPT-5.2 Pro has the edge for math in this comparison, averaging 98.2 versus 80.5. Inside this category, HMMT Feb 2023 is the benchmark that creates the most daylight between them.
GPT-5.2 Pro has the edge for reasoning in this comparison, averaging 95.2 versus 75.5. Inside this category, MRCRv2 is the benchmark that creates the most daylight between them.
GPT-5.2 Pro has the edge for agentic tasks in this comparison, averaging 85.9 versus 67.5. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
GPT-5.2 Pro has the edge for multimodal and grounded tasks in this comparison, averaging 96 versus 58.6. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
GPT-5.2 Pro has the edge for instruction following in this comparison, averaging 95 versus 86. Inside this category, IFEval is the benchmark that creates the most daylight between them.
GPT-5.2 Pro has the edge for multilingual tasks in this comparison, averaging 93.4 versus 79.8. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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