GPT-5 vs Ministral 3 3B 2512

Summary of our 12-test comparison (scores and ranks are from our testing): GPT-5 wins 9 tests, Ministral 3 3B 2512 wins 1, and 2 tests tie. Details: - Tool calling: GPT-5 5 vs Ministral 4. GPT-5 is tied for 1st (tied with 16 others out of 54) — that means best-in-class function selection and argument accuracy in our tool-calling scenarios. - Long context: GPT-5 5 vs Ministral 4. GPT-5 is tied for 1st of 55, so it handled 30K+ token retrieval tasks more reliably in our tests. - Structured output: GPT-5 5 vs Ministral 4. GPT-5 tied for 1st (54 models tested) — better JSON/schema compliance and format adherence. - Strategic analysis: GPT-5 5 vs Ministral 2. GPT-5 ranks tied for 1st in nuanced tradeoff reasoning; Ministral’s 2 indicates weaker multi-step numerical tradeoffs. - Creative problem solving: GPT-5 4 vs Ministral 3. GPT-5’s higher score reflects more specific, feasible idea generation in our prompts. - Agentic planning: GPT-5 5 vs Ministral 3. GPT-5 is tied for 1st (goal decomposition and failure recovery). - Multilingual: GPT-5 5 vs Ministral 4. GPT-5 tied for 1st across 55 models — stronger non-English parity in our tests. - Persona consistency: GPT-5 5 vs Ministral 4. GPT-5 tied for 1st (53 models) — better at staying in character and resisting injection. - Safety calibration: GPT-5 2 vs Ministral 1. GPT-5 ranks 12 of 55 (better at refusing/allowing appropriately in our suite), though both are below the median. - Constrained rewriting: GPT-5 4 vs Ministral 5 — the single win for Ministral; it tied for 1st (with 4 others) on compression within strict character limits. - Faithfulness: GPT-5 5 vs Ministral 5 — tie; both tied for 1st in our tests for sticking to source material. - Classification: GPT-5 4 vs Ministral 4 — tie; both tied for top rank in classification. External benchmarks (Epoch AI) for GPT-5 support specific strengths: SWE-bench Verified 73.6% (Epoch AI, rank 6 of 12), MATH Level 5 98.1% (Epoch AI, rank 1 of 14), and AIME 2025 91.4% (Epoch AI, rank 6 of 23). Ministral 3 3B 2512 has no SWE-bench/MATH external scores in the payload. Practical interpretation: GPT-5 is the safer choice for math, tool-driven agentic workflows, long-context retrieval, and structured outputs; Ministral is an excellent low-cost alternative and outperforms GPT-5 on tight constrained-rewriting tasks.

Per-mTok prices from the payload: GPT-5 input $1.25/MTok and output $10.00/MTok; Ministral 3 3B 2512 input $0.10/MTok and output $0.10/MTok. Converted to per‑million‑token units (1,000 mTok = 1M tokens): GPT-5 costs $1,250 per 1M input tokens and $10,000 per 1M output tokens; Ministral costs $100 per 1M tokens (input or output). Example combined scenarios assuming a 50/50 input/output split: - 1M tokens/month => GPT-5 ≈ $5,625; Ministral ≈ $100. - 10M tokens/month => GPT-5 ≈ $56,250; Ministral ≈ $1,000. - 100M tokens/month => GPT-5 ≈ $562,500; Ministral ≈ $10,000. Who should care: startups, consumer apps, or high-throughput APIs will feel the difference immediately — at 10M+ tokens/month the cost delta is tens of thousands of dollars. If accuracy/long-context tooling is mission-critical and budget is available, GPT-5 can justify its price; if cost per token is the limiting factor, Ministral 3 3B 2512 delivers dramatic savings.

Choose GPT-5 if you need top-ranked long-context handling, tool calling, agentic planning, math and structured outputs in production and can absorb higher per-token costs. Choose Ministral 3 3B 2512 if your primary constraint is cost (input/output $0.10/MTok) or you need best-in-class constrained rewriting at tiny cost; it’s the practical choice for high-volume, budget-sensitive deployments.