Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Ministral 3 14B (Reasoning) is clearly ahead on the aggregate, 60 to 45. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Ministral 3 14B (Reasoning)'s sharpest advantage is in multilingual, where it averages 77.8 against 48. The single biggest benchmark swing on the page is MMLU-ProX, 76 to 48. GPT-5 nano does hit back in knowledge, 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 14B (Reasoning). That is roughly Infinityx on output cost alone. GPT-5 nano gives you the larger context window at 400K, compared with 128K for Ministral 3 14B (Reasoning).
Pick Ministral 3 14B (Reasoning) if you want the stronger benchmark profile. GPT-5 nano only becomes the better choice if knowledge is the priority or you need the larger 400K context window.
Ministral 3 14B (Reasoning)
58.5
GPT-5 nano
37.7
Ministral 3 14B (Reasoning)
35
GPT-5 nano
22
Ministral 3 14B (Reasoning)
71.5
GPT-5 nano
56.7
Ministral 3 14B (Reasoning)
69.2
GPT-5 nano
58.8
Ministral 3 14B (Reasoning)
52.1
GPT-5 nano
63.7
Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.
Ministral 3 14B (Reasoning)
77.8
GPT-5 nano
48
Ministral 3 14B (Reasoning)
75.2
GPT-5 nano
85.2
Ministral 3 14B (Reasoning) is ahead overall, 60 to 45. The biggest single separator in this matchup is MMLU-ProX, where the scores are 76 and 48.
GPT-5 nano has the edge for knowledge tasks in this comparison, averaging 63.7 versus 52.1. Inside this category, FrontierScience is the benchmark that creates the most daylight between them.
Ministral 3 14B (Reasoning) has the edge for coding in this comparison, averaging 35 versus 22. 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 75.2. Inside this category, AIME 2025 is the benchmark that creates the most daylight between them.
Ministral 3 14B (Reasoning) has the edge for reasoning in this comparison, averaging 69.2 versus 58.8. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
Ministral 3 14B (Reasoning) has the edge for agentic tasks in this comparison, averaging 58.5 versus 37.7. Inside this category, OSWorld-Verified is the benchmark that creates the most daylight between them.
Ministral 3 14B (Reasoning) has the edge for multimodal and grounded tasks in this comparison, averaging 71.5 versus 56.7. Inside this category, OfficeQA Pro is the benchmark that creates the most daylight between them.
Ministral 3 14B (Reasoning) has the edge for multilingual tasks in this comparison, averaging 77.8 versus 48. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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