GPT-5.2 vs Ministral 3 8B 2512

Head-to-head by test (scores show our 1–5 internal scale unless noted):

• Strategic analysis: GPT-5.2 5 vs Ministral 3 8B 2512 3 — GPT-5.2 wins; it is tied for 1st (rank: tied for 1st with 25 others of 54) indicating top-tier nuanced tradeoff reasoning. This matters for pricing models, financial planning, or multi-step optimization.
• Creative problem solving: 5 vs 3 — GPT-5.2 wins and ranks tied for 1st (tied with 7 others of 54); expect more non-obvious feasible ideas from GPT-5.2.
• Faithfulness: 5 vs 4 — GPT-5.2 wins and is tied for 1st (with 32 others of 55); better at sticking to source material and avoiding hallucination.
• Long context: 5 vs 4 — GPT-5.2 wins and is tied for 1st (with 36 others of 55); combined with its 400,000-token window (vs Ministral’s 262,144), GPT-5.2 is clearly stronger for retrieval over 30K+ tokens.
• Safety calibration: 5 vs 1 — GPT-5.2 wins decisively and is tied for 1st (with 4 others of 55); means safer refusals/permits on harmful prompts.
• Agentic planning: 5 vs 3 — GPT-5.2 wins and is tied for 1st (with 14 others of 54); better goal decomposition and recovery.
• Multilingual: 5 vs 4 — GPT-5.2 wins and is tied for 1st (with 34 others of 55); stronger non-English parity.
• Constrained rewriting: 4 vs 5 — Ministral wins (tied for 1st with 4 others of 53); better at tight-character compression tasks.
• Structured output: tie 4 vs 4 — both rank 26 of 54 (27 models share this score); expect similar JSON/schema compliance.
• Tool calling: tie 4 vs 4 — both rank 18 of 54; comparable at selecting functions and arguments.
• Classification: tie 4 vs 4 — both tied for 1st with many models; similar routing/categorization accuracy.
• Persona consistency: tie 5 vs 5 — both tied for 1st with 36 others; both hold character and resist injection well. External benchmarks (attribution): GPT-5.2 scores 73.8% on SWE-bench Verified (Epoch AI) and 96.1% on AIME 2025 (Epoch AI), supporting its strength on coding/competition math. Ministral has no external SWE/AIME scores in this payload. Overall, GPT-5.2 wins 7 categories, Ministral wins 1, and 4 are ties — the wins cluster around higher-stakes reasoning, long context, safety and math.

Per-token rates (per thousand tokens): GPT-5.2 input $1.75, output $14.00; Ministral 3 8B 2512 input $0.15, output $0.15. Using a realistic 50/50 input/output split, cost per 1M total tokens: GPT-5.2 ≈ $7,875; Ministral ≈ $150. At scale: 10M tokens/month ≈ $78,750 (GPT-5.2) vs $1,500 (Ministral); 100M ≈ $787,500 vs $15,000. The payload’s priceRatio is 93.33 (GPT-5.2 output rate is ~93x Ministral’s). Who should care: startups and high-volume apps (chatbots, background inference) will see immediate savings with Ministral; enterprises or teams needing top-tier safety, long-context, and math/reasoning performance may justify GPT-5.2’s much higher spend.

Choose GPT-5.2 if you need best-in-class long-context retrieval (400K window), top safety calibration, multi-step reasoning, or peak math/coding performance (AIME 96.1%, SWE-bench 73.8%); accept steep costs (~$7,875 per 1M tokens at a 50/50 split). Choose Ministral 3 8B 2512 if your priority is low-cost, large-scale deployment or constrained rewriting tasks — it costs ≈$150 per 1M tokens (50/50 split) and wins constrained-rewriting while matching GPT-5.2 on structured output, tool calling, classification, and persona consistency.