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 59. 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 41.8. The single biggest benchmark swing on the page is SWE-bench Pro, 89 to 44.
GPT-5.2 Pro is the reasoning model in the pair, while Mistral Large 2 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 Mistral Large 2.
Pick GPT-5.2 Pro if you want the stronger benchmark profile. Mistral Large 2 only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
GPT-5.2 Pro
85.9
Mistral Large 2
52.2
GPT-5.2 Pro
84.8
Mistral Large 2
41.8
GPT-5.2 Pro
96
Mistral Large 2
61
GPT-5.2 Pro
95.2
Mistral Large 2
67.5
GPT-5.2 Pro
81.5
Mistral Large 2
54.6
GPT-5.2 Pro
95
Mistral Large 2
83
GPT-5.2 Pro
93.4
Mistral Large 2
79.1
GPT-5.2 Pro
98.2
Mistral Large 2
73.8
GPT-5.2 Pro is ahead overall, 90 to 59. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 89 and 44.
GPT-5.2 Pro has the edge for knowledge tasks in this comparison, averaging 81.5 versus 54.6. 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 41.8. 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 73.8. 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 67.5. Inside this category, SimpleQA 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 52.2. 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 61. 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 83. 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 79.1. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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