Ministral 3 14B 2512 vs Mistral Medium 3.1

Overview: across our 12-test suite, Mistral Medium 3.1 wins 6 tests, Ministral 3 14B 2512 wins 1, and 5 tests tie. Detailed walk-through (scores shown as our 1–5 ratings). 1) Long_context: Medium 3.1 scores 5 vs Ministral 4 — Medium 3.1 is tied for 1st in our ranking ("tied for 1st with 36 other models out of 55"), meaning it handles 30K+ retrieval tasks more reliably; Ministral's 4 ranks 38 of 55. 2) Agentic_planning: Medium 3.1 5 vs Ministral 3 — Medium 3.1 ranks tied for 1st (stronger goal decomposition and failure recovery in our tests), while Ministral ranks 42 of 54. 3) Strategic_analysis: Medium 3.1 5 vs Ministral 4 — Medium 3.1 is tied for 1st (nuanced tradeoffs with numbers), Ministral sits midpack (rank 27 of 54). 4) Constrained_rewriting: Medium 3.1 5 vs Ministral 4 — Medium 3.1 tied for 1st (best at compression within hard character limits), Ministral ranks 6 of 53. 5) Multilingual: Medium 3.1 5 vs Ministral 4 — Medium 3.1 tied for 1st (equivalent quality non‑English output), Ministral is lower in the distribution. 6) Safety_calibration: Medium 3.1 2 vs Ministral 1 — Medium 3.1 ranks 12 of 55 vs Ministral 32 of 55, so Medium 3.1 more reliably refuses harmful prompts in our tests. 7) Creative_problem_solving: Ministral 4 beats Medium 3.1 3 — Ministral ranks 9 of 54 vs Medium 30 of 54, so Ministral generates more non‑obvious, feasible ideas in our creative tests. 8–12) Ties: structured output (4/4), tool calling (4/4), faithfulness (4/4), classification (4/4), persona consistency (5/5) — both models perform equally in JSON/schema adherence, function selection/sequencing, sticking to source material, routing, and maintaining persona (persona consistency is a tied-for-1st score). Practical implications: choose Medium 3.1 when you need reliable long-context retrieval, planning/agentic workflows, multilingual parity, and safer responses; choose Ministral 3 14B 2512 when budget and creative ideation matter and you can tolerate weaker planning and safety calibration.

Pricing (per mTok): Ministral 3 14B 2512 = $0.20 input / $0.20 output. Mistral Medium 3.1 = $0.40 input / $2.00 output. Assuming a 50/50 split between input and output tokens: for 1M total tokens/month (500k input + 500k output) costs are $200 for Ministral 3 14B 2512 and $1,200 for Mistral Medium 3.1. At 10M tokens/month that becomes $2,000 vs $12,000; at 100M tokens/month $20,000 vs $120,000. Who should care: startups and high-volume inference customers should note Mistral Medium 3.1 can be ~6x more expensive at these usage profiles; teams with strict budget constraints should prefer Ministral 3 14B 2512, while teams that need the extra long-context, safety, and planning capabilities may justify the higher spend on Medium 3.1.

Choose Ministral 3 14B 2512 if you need lower operational cost and stronger creative idea generation (it scores 4 on creative problem solving) — good for experimental apps, cost‑sensitive consumer products, and ideation workflows. Choose Mistral Medium 3.1 if your priority is long-context retrieval, multilingual parity, safer refusal behavior, and agentic planning (it scores 5 on long context, agentic planning, strategic analysis, constrained rewriting, and multilingual) — ideal for enterprise retrieval, multi‑language customer support, and agentic automation where the higher per‑token cost is justified.