Ministral 3 14B 2512 vs Mistral Small 3.2 24B

Across our 12-test suite (scores 1–5), Ministral 3 14B 2512 wins 4 tests, Mistral Small 3.2 24B wins 1, and 7 tests tie. Detailed comparison (our scores):

• Strategic analysis: Ministral 3 14B 2512 4 vs Mistral Small 3.2 24B 2 — in our testing Ministral provides stronger nuanced tradeoff reasoning (ranks 27 of 54). Expect better numeric tradeoff answers and stepwise reasoning.
• Creative problem solving: Ministral 4 vs Mistral Small 2 — Ministral ranks 9 of 54 on this task in our testing, so it produces more specific, feasible ideas for brainstorming and product concepts.
• Classification: Ministral 4 vs Mistral Small 3 — Ministral ties for 1st on classification in our testing (tied with 29 others), so it’s better at routing and label accuracy.
• Persona consistency: Ministral 5 vs Mistral Small 3 — Ministral is tied for 1st on persona consistency in our testing (36 others), useful when maintaining character or resisting prompt injection.
• Agentic planning: Ministral 3 vs Mistral Small 4 — Mistral Small 3.2 24B wins here and ranks 16 of 54 on agentic planning in our testing, so it’s stronger at goal decomposition and recovery strategies.
• Ties (structured output 4/4, constrained rewriting 4/4, tool calling 4/4, faithfulness 4/4, long context 4/4, safety calibration 1/1, multilingual 4/4): both models perform equivalently on JSON/schema adherence, function selection, faithfulness, 30k+ context retrieval, and multilingual output in our testing. Tool calling ranks the same (rank 18 of 54 for both) indicating both handle function selection and argument accuracy similarly. Safety calibration is low (1/5) for both in our testing — expect conservative refusal behavior issues to be similar. Overall interpretation: Ministral 3 14B 2512 is the better choice for classification-heavy, creative, and persona-dependent applications; Mistral Small 3.2 24B has a measurable edge for agentic planning and is cheaper on input tokens.

Costs per 1k tokens: Ministral 3 14B 2512 charges $0.20 input / $0.20 output; Mistral Small 3.2 24B charges $0.075 input / $0.20 output. Assuming a 50/50 input/output token split: 1M total tokens => Ministral 3 14B 2512 = $200.00, Mistral Small 3.2 24B = $137.50 (savings $62.50 per 1M). At 10M tokens (50/50) Ministral = $2,000, Mistral Small = $1,375 (savings $625). At 100M tokens (50/50) Ministral = $20,000, Mistral Small = $13,750 (savings $6,250). Who should care: teams running millions+ tokens/month (chat operators, high-volume APIs, indexing pipelines) — input-cost differences scale linearly and produce meaningful savings at high volume. For low-volume or mostly output-heavy workloads the difference narrows because both models share the same $0.20/mk output price.

Choose Ministral 3 14B 2512 if you need stronger classification, creative idea generation, or strict persona consistency (it wins those tests in our suite). Choose Mistral Small 3.2 24B if agentic planning is key or you run high-volume, input-heavy workloads — it wins agentic planning and has a $0.075/mk input price that materially reduces cost at scale.