GPT-5 vs Ministral 3 14B 2512

Head-to-head by our 12-test suite (scores shown are from our testing and referenced rankings):

• Structured output: GPT-5 5 vs Ministral 4. GPT-5 is tied for 1st (tied with 24 others) out of 54; Ministral ranks 26 of 54. For JSON/schema tasks GPT-5 is meaningfully more reliable.
• Strategic analysis: GPT-5 5 vs Ministral 4. GPT-5 tied for 1st out of 54; Ministral ranks 27 of 54. GPT-5 gives stronger nuanced tradeoff reasoning with numbers.
• Tool calling: GPT-5 5 vs Ministral 4. GPT-5 tied for 1st with 16 others (54 total); Ministral ranks 18 of 54. GPT-5 picks functions and arguments more accurately in our tests.
• Faithfulness: GPT-5 5 vs Ministral 4. GPT-5 tied for 1st of 55; Ministral ranks 34 of 55. GPT-5 better resists hallucination and sticks to sources in our benchmarks.
• Long context: GPT-5 5 vs Ministral 4. GPT-5 tied for 1st of 55; Ministral ranks 38 of 55. For retrieval or summarization at 30K+ tokens GPT-5 showed higher retrieval accuracy.
• Safety calibration: GPT-5 2 vs Ministral 1. GPT-5 ranks 12 of 55 vs Ministral 32 of 55 — both are not top-tier on safety calibration, but GPT-5 is safer by our measure.
• Agentic planning: GPT-5 5 vs Ministral 3. GPT-5 tied for 1st of 54; Ministral ranks 42 of 54. For goal decomposition and recovery GPT-5 scored much higher.
• Multilingual: GPT-5 5 vs Ministral 4. GPT-5 tied for 1st of 55; Ministral ranks 36 of 55 — GPT-5 gives higher non-English parity in our tests.
• Ties: constrained rewriting (both 4), creative problem solving (both 4), classification (both 4), persona consistency (both 5). On these tasks both models are comparable; constrained rewriting ranks 6 of 53 for both.
• External benchmarks (supplementary): GPT-5 scores 73.6% on SWE-bench Verified (Epoch AI), 98.1% on MATH Level 5 (Epoch AI), and 91.4% on AIME 2025 (Epoch AI). Ministral 3 14B 2512 has no external SWE/MATH/AIME scores in the payload to compare. These external results support GPT-5’s superiority on coding/math tasks in our comparison.
• Other operational differences from the payload: GPT-5 offers a 400,000 token context window and supports text+image+file->text; Ministral offers a 262,144 token window and text+image->text. That larger window and file handling help explain GPT-5’s edge on long-context and structured-output tasks.

Pricing (per mTok): GPT-5 input $1.25, output $10.00; Ministral 3 14B 2512 input $0.20, output $0.20. Assuming a 50/50 split of input/output tokens, effective cost per 1,000 tokens is $5.625 for GPT-5 vs $0.20 for Ministral. That implies monthly costs for total token usage of: 1M tokens — GPT-5 $5,625 vs Ministral $200; 10M — GPT-5 $56,250 vs Ministral $2,000; 100M — GPT-5 $562,500 vs Ministral $20,000. The output-cost ratio is 50x (10 / 0.2 = 50), matching the payload priceRatio. Teams building high-volume consumer chatbots, large-scale summarization, or services with predictable token budgets should prioritize Ministral to control costs; mission-critical apps that need GPT-5’s higher scores (tool calling, long-context, faithfulness) should budget for the large premium.

Choose GPT-5 if: you need top results on tool calling, long-context retrieval/summarization, faithfulness, strategic analysis, agentic planning, or superior math/coding external scores (MATH Level 5 98.1%, SWE-bench 73.6%). Budget for a large cost premium (output $10/mTok). Choose Ministral 3 14B 2512 if: you must run at high volume on a tight budget (output $0.2/mTok), need strong persona consistency or constrained rewriting at low cost, or want a capable, efficient model with a 262K context window when raw budget is the deciding factor.