Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-5.2 is clearly ahead on the aggregate, 88 to 60. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.2's sharpest advantage is in coding, where it averages 81.8 against 35. The single biggest benchmark swing on the page is SWE-bench Pro, 85 to 36.
GPT-5.2 is also the more expensive model on tokens at $2.00 input / $8.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Ministral 3 14B (Reasoning). That is roughly Infinityx on output cost alone. GPT-5.2 gives you the larger context window at 400K, compared with 128K for Ministral 3 14B (Reasoning).
Pick GPT-5.2 if you want the stronger benchmark profile. Ministral 3 14B (Reasoning) only becomes the better choice if you want the cheaper token bill.
GPT-5.2
85.4
Ministral 3 14B (Reasoning)
58.5
GPT-5.2
81.8
Ministral 3 14B (Reasoning)
35
GPT-5.2
95
Ministral 3 14B (Reasoning)
71.5
GPT-5.2
93.2
Ministral 3 14B (Reasoning)
69.2
GPT-5.2
79.5
Ministral 3 14B (Reasoning)
52.1
GPT-5.2
94
Ministral 3 14B (Reasoning)
81
GPT-5.2
92.4
Ministral 3 14B (Reasoning)
77.8
GPT-5.2
97.2
Ministral 3 14B (Reasoning)
75.2
GPT-5.2 is ahead overall, 88 to 60. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 85 and 36.
GPT-5.2 has the edge for knowledge tasks in this comparison, averaging 79.5 versus 52.1. Inside this category, HLE is the benchmark that creates the most daylight between them.
GPT-5.2 has the edge for coding in this comparison, averaging 81.8 versus 35. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
GPT-5.2 has the edge for math in this comparison, averaging 97.2 versus 75.2. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
GPT-5.2 has the edge for reasoning in this comparison, averaging 93.2 versus 69.2. Inside this category, MRCRv2 is the benchmark that creates the most daylight between them.
GPT-5.2 has the edge for agentic tasks in this comparison, averaging 85.4 versus 58.5. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
GPT-5.2 has the edge for multimodal and grounded tasks in this comparison, averaging 95 versus 71.5. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
GPT-5.2 has the edge for instruction following in this comparison, averaging 94 versus 81. Inside this category, IFEval is the benchmark that creates the most daylight between them.
GPT-5.2 has the edge for multilingual tasks in this comparison, averaging 92.4 versus 77.8. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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