GPT-5 Mini vs Ministral 3 3B 2512

Head-to-head on our 12-test suite (scores are our 1–5 proxies and external math/coding percentages where provided):

• Structured output: GPT-5 Mini 5 vs Ministral 4 — GPT-5 Mini tied for 1st (tied with 24 others) for JSON/schema compliance; expect fewer format errors with GPT-5 Mini.
• Strategic analysis: GPT-5 Mini 5 vs Ministral 2 — GPT-5 Mini tied for 1st on nuanced tradeoff reasoning, while Ministral ranks 44 of 54; GPT-5 Mini is far better at multi-step numerical tradeoffs.
• Creative problem solving: 4 vs 3 — GPT-5 Mini wins; better at producing specific, non-obvious feasible ideas.
• Long context: 5 vs 4 — GPT-5 Mini tied for 1st for retrieval at 30K+ tokens; better for long transcripts and documents.
• Safety calibration: 3 vs 1 — GPT-5 Mini ranks 10 of 55 vs Ministral 32 of 55; GPT-5 Mini more reliably refuses harmful prompts and permits legitimate ones.
• Persona consistency: 5 vs 4 — GPT-5 Mini tied for 1st; better at maintaining character and resisting injection.
• Agentic planning: 4 vs 3 — GPT-5 Mini ranks 16 of 54 vs Ministral 42 of 54; stronger at goal decomposition and recovery.
• Multilingual: 5 vs 4 — GPT-5 Mini tied for 1st; better non-English parity.
• Constrained rewriting: 4 vs 5 — Ministral 3 3B 2512 wins (tied for 1st); superior when compressing text into hard character limits.
• Tool calling: 3 vs 4 — Ministral wins; its rank (18 of 54) vs GPT-5 Mini (47 of 54) suggests better function selection and argument sequencing.
• Faithfulness and Classification: ties — both score 5 (faithfulness) and 4 (classification); neither has a clear edge.
• External benchmarks (supplementary): GPT-5 Mini scores 64.7% on SWE-bench Verified (Epoch AI), 97.8% on MATH Level 5 (Epoch AI), and 86.7% on AIME 2025 (Epoch AI), supporting its strong coding/math performance. Ministral 3 3B 2512 has no external percentages in the payload. Overall: GPT-5 Mini wins 8 tests, Ministral 3 3B 2512 wins 2, and 2 tie — the difference is largest in strategic analysis, long-context, and safety calibration, while Ministral shines at constrained rewriting and tool calling.

Pricing (per payload): GPT-5 Mini charges $0.25 input and $2.00 output per mTok; Ministral 3 3B 2512 charges $0.10 input and $0.10 output per mTok. Interpreting mTok as 1,000 tokens, per-million-token costs are: GPT-5 Mini — $250 per 1M input tokens and $2,000 per 1M output tokens; Ministral 3 3B 2512 — $100 per 1M input and $100 per 1M output. Example combined scenarios (50/50 input/output): for 1M tokens GPT-5 Mini ≈ $1,125 vs Ministral ≈ $100; for 10M tokens GPT-5 Mini ≈ $11,250 vs Ministral ≈ $1,000; for 100M tokens GPT-5 Mini ≈ $112,500 vs Ministral ≈ $10,000. Who should care: any application at ≥10M tokens/month (chatbots, high-volume API services, large ingestion pipelines) will see a material ROI impact from choosing the cheaper Ministral 3 3B 2512; single-user or low-volume/high-quality workloads may justify GPT-5 Mini's premium.

Choose GPT-5 Mini if you need high-quality structured outputs, reliable safety calibration, long-context handling, strong strategic math/reasoning (e.g., financial modeling, multi-step analysis), or you value higher persona and faithfulness scores despite a much higher token cost. Choose Ministral 3 3B 2512 if you need a low-cost production model for tool-heavy flows or aggressive token budgets (high-volume chat, microservices, or constrained-rewrite pipelines) where constrained rewriting and tool-calling quality plus a cheap $0.10 per 1k output token rate outweighs GPT-5 Mini's reasoning and long-context advantages.