GPT-5 Mini vs Ministral 3 14B 2512

Head-to-head (our 12-test suite): GPT-5 Mini wins 7 tests, Ministral 3 14B 2512 wins 1, and 4 tests tie. Detailed results (our scores):

• Structured_output: GPT-5 Mini 5 vs Ministral 4 — GPT-5 Mini wins and is tied for 1st (tied with 24 others), which indicates stronger JSON/schema compliance for API-style responses. In practice: fewer format errors and easier downstream parsing.
• Strategic_analysis: GPT-5 Mini 5 vs Ministral 4 — GPT-5 Mini wins and is tied for 1st, meaning better nuanced tradeoff reasoning and numeric analysis in our tests.
• Faithfulness: GPT-5 Mini 5 vs Ministral 4 — GPT-5 Mini wins (tied for 1st), so it more reliably sticks to source material in our evaluation.
• Long_context: GPT-5 Mini 5 vs Ministral 4 — GPT-5 Mini wins (tied for 1st), so retrieval and accuracy over 30K+ tokens performed better in our runs.
• Safety_calibration: GPT-5 Mini 3 vs Ministral 1 — GPT-5 Mini wins (rank 10/55 vs 32/55), showing GPT-5 Mini refused harmful inputs more appropriately while permitting legitimate ones in our tests.
• Agentic_planning: GPT-5 Mini 4 vs Ministral 3 — GPT-5 Mini wins, giving better goal decomposition and recovery in our scenarios.
• Multilingual: GPT-5 Mini 5 vs Ministral 4 — GPT-5 Mini wins (tied for 1st), producing higher-quality non-English outputs in our suite.
• Tool_calling: GPT-5 Mini 3 vs Ministral 4 — Ministral 3 14B 2512 wins (GPT-5 Mini rank 47/54, Ministral rank 18/54). That means Ministral performed better at function selection, argument accuracy, and sequencing in our tests — a practical advantage for systems relying on tool/agent orchestration.
• Constrained_rewriting, Creative_problem_solving, Classification, Persona_consistency: ties (scores equal). For constrained rewriting both rank 6/53; for creative problem solving both rank 9/54; classification and persona consistency are tied-for-1st for both. External benchmarks (supplementary): GPT-5 Mini scores 64.7% on SWE-bench Verified, 97.8% on MATH Level 5, and 86.7% on AIME 2025 (Epoch AI). These external measures corroborate GPT-5 Mini's strength on challenging math and some coding tasks, but are supplemental — Ministral has no external scores in the payload. Overall, GPT-5 Mini's wins map to better structured outputs, long-context handling, faithfulness and safety in our testing; Ministral's clear advantage is tool calling and much lower per-token cost.

Per the payload, GPT-5 Mini charges $0.25 per mTok input and $2.00 per mTok output (combined $2.25/mTok). Ministral 3 14B 2512 charges $0.20 per mTok input and $0.20 per mTok output (combined $0.40/mTok). At 1M tokens/month (1,000 mToks) output-only costs: GPT-5 Mini = $2,000; Ministral = $200. Combined input+output: GPT-5 Mini = $2,250; Ministral = $400. At 10M tokens/month (10,000 mToks) output-only: GPT-5 Mini = $20,000; Ministral = $2,000. Combined: GPT-5 Mini = $22,500; Ministral = $4,000. At 100M tokens/month (100,000 mToks) output-only: GPT-5 Mini = $200,000; Ministral = $20,000. Combined: GPT-5 Mini = $225,000; Ministral = $40,000. Who should care: startups and high-volume API customers will see a 10x+ gap in output cost (payload priceRatio = 10), so choose Ministral 3 14B 2512 when per-token spend dominates. Teams that need top-tier long-context, structured output guarantees, and stronger safety/faithfulness (and can accept the price) should consider GPT-5 Mini despite the higher per-token bill.

Choose GPT-5 Mini if you need: reliable JSON/schema output, best-in-class long-context retrieval (30K+), stronger faithfulness and safety calibration, and advanced strategic analysis — and you can absorb higher per-token costs ($2.00 output/mTok). Choose Ministral 3 14B 2512 if you need: a low-cost model for high-volume usage (output $0.20/mTok), better tool/function-calling behavior in our tests, or you're building cost-sensitive production pipelines where per-token spend dominates.