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 76. 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 coding, where it averages 84.8 against 57.5. The single biggest benchmark swing on the page is SWE-bench Pro, 89 to 60.
GPT-5.2 Pro is also the more expensive model on tokens at $25.00 input / $150.00 output per 1M tokens, versus $3.00 input / $15.00 output per 1M tokens for Claude Sonnet 4.5. That is roughly 10.0x on output cost alone. GPT-5.2 Pro is the reasoning model in the pair, while Claude Sonnet 4.5 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 200K for Claude Sonnet 4.5.
Pick GPT-5.2 Pro if you want the stronger benchmark profile. Claude Sonnet 4.5 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
Claude Sonnet 4.5
70.3
GPT-5.2 Pro
84.8
Claude Sonnet 4.5
57.5
GPT-5.2 Pro
96
Claude Sonnet 4.5
91.4
GPT-5.2 Pro
95.2
Claude Sonnet 4.5
86
GPT-5.2 Pro
81.5
Claude Sonnet 4.5
70.7
GPT-5.2 Pro
95
Claude Sonnet 4.5
90
GPT-5.2 Pro
93.4
Claude Sonnet 4.5
88.4
GPT-5.2 Pro
98.2
Claude Sonnet 4.5
92.4
GPT-5.2 Pro is ahead overall, 90 to 76. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 89 and 60.
GPT-5.2 Pro has the edge for knowledge tasks in this comparison, averaging 81.5 versus 70.7. 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 57.5. Inside this category, SWE-bench Pro 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 92.4. Inside this category, MATH-500 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 86. 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 70.3. Inside this category, Terminal-Bench 2.0 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 91.4. Inside this category, OfficeQA 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 90. 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 88.4. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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