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 60. 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 38.9. The single biggest benchmark swing on the page is SWE-bench Pro, 89 to 40.
GPT-5.2 Pro is also the more expensive model on tokens at $25.00 input / $150.00 output per 1M tokens, versus $0.50 input / $2.80 output per 1M tokens for Kimi K2.5. That is roughly 53.6x on output cost alone. GPT-5.2 Pro is the reasoning model in the pair, while Kimi K2.5 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 128K for Kimi K2.5.
Pick GPT-5.2 Pro if you want the stronger benchmark profile. Kimi K2.5 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
Kimi K2.5
52.3
GPT-5.2 Pro
84.8
Kimi K2.5
38.9
GPT-5.2 Pro
96
Kimi K2.5
64.6
GPT-5.2 Pro
95.2
Kimi K2.5
71.7
GPT-5.2 Pro
81.5
Kimi K2.5
57.2
GPT-5.2 Pro
95
Kimi K2.5
85
GPT-5.2 Pro
93.4
Kimi K2.5
79.8
GPT-5.2 Pro
98.2
Kimi K2.5
78.7
GPT-5.2 Pro is ahead overall, 90 to 60. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 89 and 40.
GPT-5.2 Pro has the edge for knowledge tasks in this comparison, averaging 81.5 versus 57.2. 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 38.9. 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 78.7. 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 71.7. Inside this category, LongBench v2 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 52.3. 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 64.6. 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 85. 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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