GPT-5 Nano vs Ministral 3 14B 2512

Across our 12-test suite GPT-5 Nano wins 5 tests, Ministral 3 14B wins 4, and 3 are ties. Detailed walk-through: Wins for GPT-5 Nano - structured output: GPT-5 Nano scored 5 vs 4 for 14B; ranking shows GPT-5 Nano is tied for 1st with 24 others out of 54 — excellent for JSON/schema compliance and tool integration. - long context: 5 vs 4; GPT-5 Nano tied for 1st with 36 others out of 55 — strong for retrieval and documents >30K tokens. - safety calibration: 4 vs 1; GPT-5 Nano ranks 6 of 55 (tied with 3), while 14B ranks 32 of 55 — GPT-5 Nano is notably better at refusing harmful requests and permitting legitimate ones. - agentic planning: 4 vs 3; GPT-5 Nano ranks 16 of 54 — better at goal decomposition and failure recovery. - multilingual: 5 vs 4; GPT-5 Nano tied for 1st with 34 others out of 55 — stronger in non-English parity. Wins for Ministral 3 14B 2512 - constrained rewriting: 4 vs 3; 14B ranks 6 of 53 (25 tied) — better at tight-character compression and exact rewrites. - creative problem solving: 4 vs 3; 14B ranks 9 of 54 — favors non-obvious feasible ideas. - classification: 4 vs 3; 14B tied for 1st with 29 others out of 53 — superior at routing and categorization. - persona consistency: 5 vs 4; 14B tied for 1st with 36 others — better at maintaining character and resisting injection. Ties (both models score 4) - strategic analysis, tool calling, faithfulness: both models perform similarly on nuanced tradeoff reasoning, function selection/arguments, and sticking to source material. External math benchmarks: GPT-5 Nano scores 95.2% on MATH Level 5 and 81.1% on AIME 2025 (Epoch AI) — these external results support its stronger math/competition performance in our profile. Practical meaning: pick GPT-5 Nano when you need schema-accurate outputs, long-context retrieval, strong safety, multilingual parity, or advanced math reasoning. Pick Ministral 3 14B when you need cheaper long outputs, tight rewriting, classification, persona-driven assistants, or better creative ideation.

Costs in the payload are per mTok. GPT-5 Nano: input $0.05/mTok, output $0.40/mTok. Ministral 3 14B 2512: input $0.20/mTok, output $0.20/mTok. Assuming a 50/50 input/output split: - 1M tokens (1000 mTok total → 500 mTok input + 500 mTok output): GPT-5 Nano = 500*$0.05 + 500*$0.40 = $25 + $200 = $225. Ministral 3 14B = 500*$0.20 + 500*$0.20 = $100 + $100 = $200. - 10M tokens (10,000 mTok; 5,000/5,000): GPT-5 Nano = $2,250; Ministral = $2,000. - 100M tokens (100,000 mTok; 50,000/50,000): GPT-5 Nano = $22,500; Ministral = $20,000. Who should care: any output-heavy services (long responses, generated documents, summaries) will prefer Ministral 3 14B because GPT-5 Nano's output rate is twice as expensive ($0.40 vs $0.20). Conversely, workloads dominated by long inputs or retrieval contexts benefit from GPT-5 Nano's low input price ($0.05) and its strengths in long-context handling and structured output. If your token mix skews toward short prompts with long outputs, choose 14B; if you stream large contexts or need schema-compliant replies and safety, expect to pay more for GPT-5 Nano.

Choose GPT-5 Nano if you need: - Schema-compliant, production-grade structured outputs (score 5 structured output; tied for 1st). - Very large context handling (score 5 long context; tied for 1st). - Strong safety (score 4; rank 6/55) or multilingual parity (score 5). - High-stakes assistant behavior and math-heavy tasks (95.2% MATH Level 5; 81.1% AIME 2025, Epoch AI). Accept higher output cost ($0.40/mTok) for these benefits. Choose Ministral 3 14B 2512 if you need: - Lower output cost for long generated responses ($0.20/mTok) and want to minimize runtime spend. - Better constrained rewriting (score 4; rank 6/53), classification (score 4; tied for 1st) or persona-consistent chat (score 5; tied for 1st). - Strong creative problem solving (score 4; rank 9/54). Prefer 14B when budget for output tokens is the primary constraint.