Ministral 3 14B 2512 vs Mistral Small 4

Summary of head-to-head results (all scores are from our 12-test suite): Mistral Small 4 wins 4 benchmarks, Ministral 3 14B 2512 wins 2, and 6 benchmarks tie. Key wins and what they mean:

• Structured_output: Small 4 = 5 vs 14B 2512 = 4. In our testing Small 4 is tied for 1st (tied with 24 others out of 54) on JSON/schema compliance; 14B 2512 sits lower (rank 26 of 54). For apps that must produce exact JSON or strict formats (APIs, invoices), Small 4 reduces formatting fixes.
• Multilingual: Small 4 = 5 vs 14B 2512 = 4. Small 4 is tied for 1st (tied with 34 others out of 55) whereas 14B 2512 ranks 36 of 55. Expect Small 4 to produce more consistent non-English output in our tests.
• Safety_calibration: Small 4 = 2 vs 14B 2512 = 1. Small 4 ranks 12 of 55 vs 14B 2512 at 32 of 55; Small 4 refused/allowed appropriately more often in our safety tests, relevant for public-facing agents.
• Agentic_planning: Small 4 = 4 vs 14B 2512 = 3. Small 4 ranks 16 of 54 vs 14B 2512 at 42 of 54; this translates to better goal decomposition and recovery in multi-step automation in our testing.
• Classification: 14B 2512 = 4 vs Small 4 = 2. 14B 2512 is tied for 1st (with 29 others) while Small 4 ranks 51 of 53. For routing, tagging, or high-stakes classification, 14B 2512 gave more accurate labels in our tests.
• Constrained_rewriting: 14B 2512 = 4 vs Small 4 = 3. 14B 2512 ranks 6 of 53 (top tier) vs Small 4 at 31 of 53; when you must compress or fit copy into hard character limits, 14B 2512 handled constraints better in our suite. Ties (both models scored the same in our tests): strategic analysis (4), creative problem solving (4), tool calling (4), faithfulness (4), long context (4), persona consistency (5). Notably, both models tied for 1st on persona consistency, and both have the same long context score and ranking (rank 38 of 55), so for retrieval over 30K+ tokens they performed similarly in our evaluation. In short: Small 4 is the edge choice for format fidelity, multilingual quality, safety, and planning; 14B 2512 is cheaper and stronger for classification and tight-character rewriting.

Per the payload, Ministral 3 14B 2512 charges $0.20 per mTok for input and $0.20 per mTok for output (total $0.40 per mTok). Mistral Small 4 charges $0.15 per mTok input and $0.60 per mTok output (total $0.75 per mTok). Translating to common monthly volumes (1 mTok = 1k tokens):

• 1M tokens (1,000 mTok): 14B 2512 = $400; Small 4 = $750.
• 10M tokens (10,000 mTok): 14B 2512 = $4,000; Small 4 = $7,500.
• 100M tokens (100,000 mTok): 14B 2512 = $40,000; Small 4 = $75,000. The gap grows with output-heavy workloads because Small 4's $0.60 output rate is triple 14B 2512's $0.20. Teams running high-volume content generation, chat logs, or long-form outputs should care about the ~$0.35–$0.55 per mTok incremental cost. Cost-sensitive deployments (large-scale classification routing, high-volume inference) should favor Ministral 3 14B 2512 to cut monthly bills roughly in half at scale; teams that prioritize structured JSON, multilingual fidelity, or safer refusal behavior may accept the higher output price of Mistral Small 4.

Choose Ministral 3 14B 2512 if: you need a cheaper per-token LLM for high-volume inference, you prioritize classification accuracy or constrained rewriting (14B 2512 scored 4 vs Small 4's 2 on classification and 4 vs 3 on constrained rewriting in our tests), or you must maximize throughput on a budget. Choose Mistral Small 4 if: you need best-in-class structured outputs and multilingual fidelity (Small 4 scored 5 vs 4 on both structured output and multilingual), better safety calibration and agentic planning, and you can absorb higher output costs (Small 4's $0.60/mTok output).