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 32. 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 13.3. The single biggest benchmark swing on the page is SWE-bench Pro, 89 to 14.
GPT-5.2 Pro is the reasoning model in the pair, while Mistral 8x7B v0.2 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 32K for Mistral 8x7B v0.2.
Pick GPT-5.2 Pro if you want the stronger benchmark profile. Mistral 8x7B v0.2 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 8x7B v0.2
27.9
GPT-5.2 Pro
84.8
Mistral 8x7B v0.2
13.3
GPT-5.2 Pro
96
Mistral 8x7B v0.2
32.3
GPT-5.2 Pro
95.2
Mistral 8x7B v0.2
35.2
GPT-5.2 Pro
81.5
Mistral 8x7B v0.2
28.9
GPT-5.2 Pro
95
Mistral 8x7B v0.2
67
GPT-5.2 Pro
93.4
Mistral 8x7B v0.2
58.8
GPT-5.2 Pro
98.2
Mistral 8x7B v0.2
42
GPT-5.2 Pro is ahead overall, 90 to 32. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 89 and 14.
GPT-5.2 Pro has the edge for knowledge tasks in this comparison, averaging 81.5 versus 28.9. Inside this category, GPQA 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 13.3. 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 42. 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 35.2. 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 27.9. 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 32.3. 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 67. 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 58.8. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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