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 61. 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. The single biggest benchmark swing on the page is SWE-bench Pro, 89 to 42.
GPT-5.2 Pro is also the more expensive model on tokens at $25.00 input / $150.00 output per 1M tokens, versus $2.00 input / $6.00 output per 1M tokens for Mistral Large 3. That is roughly 25.0x on output cost alone. GPT-5.2 Pro is the reasoning model in the pair, while Mistral Large 3 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 3.
Pick GPT-5.2 Pro if you want the stronger benchmark profile. Mistral Large 3 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
Mistral Large 3
52.5
GPT-5.2 Pro
84.8
Mistral Large 3
41
GPT-5.2 Pro
96
Mistral Large 3
75.5
GPT-5.2 Pro
95.2
Mistral Large 3
70.6
GPT-5.2 Pro
81.5
Mistral Large 3
57.1
GPT-5.2 Pro
95
Mistral Large 3
83
GPT-5.2 Pro
93.4
Mistral Large 3
78.8
GPT-5.2 Pro
98.2
Mistral Large 3
77.3
GPT-5.2 Pro is ahead overall, 90 to 61. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 89 and 42.
GPT-5.2 Pro has the edge for knowledge tasks in this comparison, averaging 81.5 versus 57.1. 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. 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 77.3. 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 70.6. 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 52.5. 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 75.5. 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 78.8. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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