Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-5 nano is clearly ahead on the aggregate, 45 to 27. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5 nano's sharpest advantage is in mathematics, where it averages 85.2 against 36. The single biggest benchmark swing on the page is AIME 2025, 85.2 to 24. Ministral 3 3B does hit back in multilingual, so the answer changes if that is the part of the workload you care about most.
GPT-5 nano is also the more expensive model on tokens at $0.05 input / $0.40 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Ministral 3 3B. That is roughly Infinityx on output cost alone. GPT-5 nano is the reasoning model in the pair, while Ministral 3 3B 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 nano gives you the larger context window at 400K, compared with 128K for Ministral 3 3B.
Pick GPT-5 nano if you want the stronger benchmark profile. Ministral 3 3B only becomes the better choice if multilingual is the priority or you want the cheaper token bill.
GPT-5 nano
37.7
Ministral 3 3B
22.9
GPT-5 nano
22
Ministral 3 3B
6.2
GPT-5 nano
56.7
Ministral 3 3B
30.4
GPT-5 nano
58.8
Ministral 3 3B
30.1
GPT-5 nano
63.7
Ministral 3 3B
24.5
Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.
GPT-5 nano
48
Ministral 3 3B
59.7
GPT-5 nano
85.2
Ministral 3 3B
36
GPT-5 nano is ahead overall, 45 to 27. The biggest single separator in this matchup is AIME 2025, where the scores are 85.2 and 24.
GPT-5 nano has the edge for knowledge tasks in this comparison, averaging 63.7 versus 24.5. Inside this category, GPQA is the benchmark that creates the most daylight between them.
GPT-5 nano has the edge for coding in this comparison, averaging 22 versus 6.2. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
GPT-5 nano has the edge for math in this comparison, averaging 85.2 versus 36. Inside this category, AIME 2025 is the benchmark that creates the most daylight between them.
GPT-5 nano has the edge for reasoning in this comparison, averaging 58.8 versus 30.1. Inside this category, MRCRv2 is the benchmark that creates the most daylight between them.
GPT-5 nano has the edge for agentic tasks in this comparison, averaging 37.7 versus 22.9. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
GPT-5 nano has the edge for multimodal and grounded tasks in this comparison, averaging 56.7 versus 30.4. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Ministral 3 3B has the edge for multilingual tasks in this comparison, averaging 59.7 versus 48. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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