GPT-5.2 vs Ministral 3 14B 2512

Across our 12-test suite GPT-5.2 wins 7 tests, Ministral wins 0, and 5 tests tie. Detail by test (payload scores):

• Strategic analysis: GPT-5.2 5 vs Ministral 4 — GPT-5.2 is tied for 1st (tied with 25 others) out of 54, so expect superior nuanced tradeoff reasoning for finance, planning, or policy tasks.
• Creative problem solving: 5 vs 4 — GPT-5.2 ranks tied for 1st (7 others); better at non-obvious, feasible ideas.
• Faithfulness: 5 vs 4 — GPT-5.2 tied for 1st (32 others); fewer hallucinations and stronger source adherence in our testing.
• Long context: 5 vs 4 — GPT-5.2 tied for 1st (36 others); better retrieval/consistency for 30K+ token contexts.
• Safety calibration: 5 vs 1 — GPT-5.2 tied for 1st (4 others); Ministral scores 1 and ranks 32 of 55 — this is a clear difference for safety-sensitive apps (moderation, content filtering).
• Agentic planning: 5 vs 3 — GPT-5.2 tied for 1st (14 others); better goal decomposition and failure recovery as tested.
• Multilingual: 5 vs 4 — GPT-5.2 tied for 1st (34 others); stronger non-English outputs in our evaluation. Ties (identical scores): structured output 4/4 (both rank 26 of 54), constrained rewriting 4/4 (both rank 6 of 53), tool calling 4/4 (both rank 18 of 54), classification 4/4 (both tied for 1st), persona consistency 5/5 (both tied for 1st). External benchmarks (Epoch AI): GPT-5.2 scores 73.8% on SWE‑bench Verified and 96.1% on AIME 2025 — cited from the payload — highlighting its strength on code/issue resolution and high-difficulty math. Ministral 3 14B 2512 has no external scores in the payload. Practical meaning: GPT-5.2 is measurably stronger where correctness, safety, long-context fidelity, strategic reasoning, and hard math matter. Ministral matches GPT-5.2 on structured outputs, tool selection, classification, and persona consistency in our tests — making it a compelling low-cost option for many product features that do not demand the highest-tier reasoning or safety calibration.

Per the payload, GPT-5.2 charges $1.75/input mtok and $14/output mtok; Ministral 3 14B 2512 charges $0.20/input mtok and $0.20/output mtok. Using the same mtok unit as the prices (1 mtok = 1,000 tokens):

• 1M tokens/month (1,000 mtok): GPT-5.2 = $1,750 input or $14,000 output; a 50/50 split ≈ $7,875. Ministral = $200 total (50/50 = $200).
• 10M tokens/month (10,000 mtok): GPT-5.2 = $17,500 input or $140,000 output; 50/50 ≈ $78,750. Ministral = $2,000.
• 100M tokens/month (100,000 mtok): GPT-5.2 = $175,000 input or $1,400,000 output; 50/50 ≈ $787,500. Ministral = $20,000. The payload lists a priceRatio of 70, reflecting the order-of-magnitude gap. Who should care: product teams running high-volume inference (>=10M tokens/month), multi-tenant SaaS, or chat apps — the difference moves budgets from low thousands to six- or seven-figure runs. Individual developers or low-volume use can favor GPT-5.2 for quality; cost-sensitive scale deployments should prefer Ministral 3 14B 2512.

Choose GPT-5.2 if you need: high safety calibration, best-in-class long-context handling, agentic planning, faithfulness, top strategic reasoning, or top AIME/SWE-bench performance (payload: AIME 96.1%, SWE-bench 73.8%). Expect to pay ~70x more per output mtok ($14 vs $0.20). Choose Ministral 3 14B 2512 if you need: a dramatically lower cost base for scale, parity on structured outputs, tool calling, classification, and persona consistency (ties in our tests), and can accept lower safety calibration (score 1) and weaker agentic planning and long-context performance. It’s the practical choice for cost-constrained scale or non-safety-critical features.