GPT-5.4 Mini vs Ministral 3 3B 2512

Across our 12-test suite, GPT-5.4 Mini (modelA) wins 8 benchmarks, Ministral 3 3B 2512 (modelB) wins 1, and 3 tests tie. Detailed walk-through (scores shown are from our testing):

• Structured output: GPT-5.4 Mini 5 vs Ministral 4 — GPT-5.4 Mini tied for 1st (tied with 24 others of 54). This means GPT-5.4 Mini is more reliable for JSON/schema compliance and precise format adherence in APIs.
• Strategic analysis: 5 vs 2 — GPT-5.4 Mini ranks tied for 1st; it produces better nuanced tradeoff reasoning with numbers in our tests, useful for pricing, analysis, or financial decision tasks.
• Creative problem solving: 4 vs 3 — GPT-5.4 Mini scored higher; expect more specific, feasible ideas in brainstorming tasks.
• Long context: 5 vs 4 — GPT-5.4 Mini tied for 1st, so it handles retrieval/accuracy over 30K+ tokens better in our runs.
• Safety calibration: 2 vs 1 — GPT-5.4 Mini is safer in our testing (rank 12/55 vs Ministral rank 32/55), meaning fewer risky false acceptances or unsafe outputs.
• Persona consistency: 5 vs 4 — GPT-5.4 Mini maintained character better in roleplay and resisted injection more reliably.
• Agentic planning: 4 vs 3 — GPT-5.4 Mini decomposes goals and recovery steps more robustly in our tests.
• Multilingual: 5 vs 4 — GPT-5.4 Mini produced higher-quality non-English outputs in our testing.
• Constrained rewriting: 4 vs 5 — Ministral 3 3B 2512 wins and is tied for 1st (with 4 others); it compresses content into hard character limits more effectively in our trials.
• Tool calling: 4 vs 4 (tie) — both models scored identically for function selection and argument accuracy (rank 18/54 each).
• Faithfulness: 5 vs 5 (tie) — both tied for 1st (tied with 32 others) and resisted hallucination equally in our checks.
• Classification: 4 vs 4 (tie) — both tied for 1st for routing/categorization tasks. In practice: choose GPT-5.4 Mini when you need robust schema outputs, long-context retrieval, multilingual fidelity, and stronger strategic reasoning. Choose Ministral 3 3B 2512 when you need best-in-class constrained-rewrite performance or an extremely low price-per-token.

Using the payload per-mTok rates and assuming 1 mTok = 1,000 tokens, GPT-5.4 Mini charges $0.75 input and $4.50 output per mTok; Ministral 3 3B 2512 charges $0.10 input and $0.10 output per mTok. Under a 50/50 input/output split: for 1M tokens/month (1,000 mTok) GPT-5.4 Mini costs ~$2,625 (0.75500 + 4.5500 = $2,625) vs Ministral ~$100 (0.1*1000 = $100). At 10M tokens: GPT-5.4 Mini ~$26,250 vs Ministral ~$1,000. At 100M tokens: GPT-5.4 Mini ~$262,500 vs Ministral ~$10,000. If your usage is heavily output-dominated (more generated tokens), GPT-5.4 Mini’s output price ($4.50/mTok) amplifies the gap; if usage is mostly short prompts, the gap shrinks but remains large. Teams running high-throughput services, inference-heavy apps, or tight-cost deployments should care about the difference; small-scale experimentation or extremely cost-sensitive production is where Ministral 3 3B 2512 shines.

Choose GPT-5.4 Mini if you need production-grade structured outputs, long-context retrieval, stronger strategic analysis, multilingual fidelity, and better safety calibration — and you can afford higher per-token costs. Choose Ministral 3 3B 2512 if your top priorities are minimizing inference cost at scale or best-in-class constrained-rewriting under tight character limits; it’s the sensible pick for cost-sensitive, high-volume deployments.