Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-5.2-Codex is clearly ahead on the aggregate, 85 to 32. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.2-Codex's sharpest advantage is in coding, where it averages 76 against 14.2. The single biggest benchmark swing on the page is SWE-bench Pro, 86 to 13.
GPT-5.2-Codex 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 8B. That is roughly Infinityx on output cost alone. GPT-5.2-Codex is the reasoning model in the pair, while Ministral 3 8B 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-Codex gives you the larger context window at 400K, compared with 128K for Ministral 3 8B.
Pick GPT-5.2-Codex if you want the stronger benchmark profile. Ministral 3 8B 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-Codex
87
Ministral 3 8B
28.9
GPT-5.2-Codex
76
Ministral 3 8B
14.2
GPT-5.2-Codex
87.6
Ministral 3 8B
32.4
GPT-5.2-Codex
92
Ministral 3 8B
36.1
GPT-5.2-Codex
72.5
Ministral 3 8B
28
GPT-5.2-Codex
92
Ministral 3 8B
69
GPT-5.2-Codex
88.4
Ministral 3 8B
61.7
GPT-5.2-Codex
95.4
Ministral 3 8B
43.3
GPT-5.2-Codex is ahead overall, 85 to 32. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 86 and 13.
GPT-5.2-Codex has the edge for knowledge tasks in this comparison, averaging 72.5 versus 28. Inside this category, MMLU is the benchmark that creates the most daylight between them.
GPT-5.2-Codex has the edge for coding in this comparison, averaging 76 versus 14.2. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
GPT-5.2-Codex has the edge for math in this comparison, averaging 95.4 versus 43.3. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
GPT-5.2-Codex has the edge for reasoning in this comparison, averaging 92 versus 36.1. Inside this category, SimpleQA is the benchmark that creates the most daylight between them.
GPT-5.2-Codex has the edge for agentic tasks in this comparison, averaging 87 versus 28.9. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
GPT-5.2-Codex has the edge for multimodal and grounded tasks in this comparison, averaging 87.6 versus 32.4. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
GPT-5.2-Codex has the edge for instruction following in this comparison, averaging 92 versus 69. Inside this category, IFEval is the benchmark that creates the most daylight between them.
GPT-5.2-Codex has the edge for multilingual tasks in this comparison, averaging 88.4 versus 61.7. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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