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 55. 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 33. The single biggest benchmark swing on the page is SWE-bench Pro, 89 to 34.
GPT-5.2 Pro is also the more expensive model on tokens at $25.00 input / $150.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Ministral 3 14B. That is roughly Infinityx on output cost alone. GPT-5.2 Pro is the reasoning model in the pair, while Ministral 3 14B 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 Ministral 3 14B.
Pick GPT-5.2 Pro if you want the stronger benchmark profile. Ministral 3 14B 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
Ministral 3 14B
48.4
GPT-5.2 Pro
84.8
Ministral 3 14B
33
GPT-5.2 Pro
96
Ministral 3 14B
70.5
GPT-5.2 Pro
95.2
Ministral 3 14B
63.6
GPT-5.2 Pro
81.5
Ministral 3 14B
50.1
GPT-5.2 Pro
95
Ministral 3 14B
80
GPT-5.2 Pro
93.4
Ministral 3 14B
76.8
GPT-5.2 Pro
98.2
Ministral 3 14B
69.7
GPT-5.2 Pro is ahead overall, 90 to 55. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 89 and 34.
GPT-5.2 Pro has the edge for knowledge tasks in this comparison, averaging 81.5 versus 50.1. 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 33. 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 69.7. 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 63.6. 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 48.4. 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 70.5. 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 80. 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 76.8. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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