GPT-4o-mini vs Ministral 3 14B 2512

Summary of our 12-test suite comparisons (scores are from our testing; rankings show position among ~52–55 models):

• Wins for GPT-4o-mini: safety calibration 4 vs 1. GPT-4o-mini ranks 6 of 55 (tied with 3 others) on safety calibration, meaning it better refuses harmful requests while permitting legitimate ones in our tests; Ministral ranks 32 of 55. This is the clearest advantage for GPT-4o-mini.
• Wins for Ministral 3 14B 2512 (5 wins):
  • creative problem solving 4 vs 2 (Ministral rank 9 of 54; GPT rank 47 of 54). For idea-generation tasks, Ministral produced more feasible, specific concepts in our tests.
  • constrained rewriting 4 vs 3 (Ministral rank 6 of 53; GPT rank 31 of 53). Ministral handles tight character/format compression better.
  • faithfulness 4 vs 3 (Ministral rank 34 of 55; GPT rank 52 of 55). Ministral sticks to source material more reliably in our runs.
  • persona consistency 5 vs 4 (Ministral tied for 1st with 36 others; GPT rank 38 of 53). Ministral maintained character and resisted prompt injection more consistently.
  • strategic analysis 4 vs 2 (Ministral rank 27 of 54; GPT rank 44 of 54). Ministral produced better nuanced tradeoff reasoning with numbers.
• Ties (same score in our tests): structured output 4/4 (both rank 26 of 54), tool calling 4/4 (both rank 18 of 54), classification 4/4 (both tied for 1st among 53), long context 4/4 (both rank 38 of 55), agentic planning 3/3 (both rank 42 of 54), multilingual 4/4 (both rank 36 of 55). For these tasks, neither model showed a decisive advantage in our suites.
• External math benchmarks (Epoch AI): GPT-4o-mini posts MATH Level 5 52.6% (rank 13 of 14) and AIME 2025 6.9% (rank 21 of 23) in our payload; Ministral has no MATH/AIME scores included. Those low percentages indicate GPT-4o-mini underperformed on our math-oriented external tests compared with the pool. Context: many important developer-facing signals are tied (tool calling, classification, long-context). Where you need safe refusals and file->text handling, GPT-4o-mini leads; where creativity, persona, faithfulness and strategic reasoning matter, Ministral leads by clear margins in our testing.

Per-mtok prices in the payload: GPT-4o-mini input $0.15, output $0.60; Ministral 3 14B 2512 input $0.20, output $0.20. That makes GPT-4o-mini 3x more expensive on output tokens (0.60/0.20 = 3.0) while its input is slightly cheaper. Example totals using mtok = 1,000 tokens and simple scenarios: 1M tokens = 1,000 mtoks; 10M = 10,000 mtoks; 100M = 100,000 mtoks. Input-only (all tokens as input): GPT-4o-mini $150 / $1,500 / $15,000 (1M/10M/100M); Ministral $200 / $2,000 / $20,000. Output-only (all tokens as output): GPT-4o-mini $600 / $6,000 / $60,000; Ministral $200 / $2,000 / $20,000. A balanced 50/50 input-output split: GPT-4o-mini $375 / $3,750 / $37,500; Ministral $200 / $2,000 / $20,000. Who should care: high-output services (long responses, summaries, code generation) will see the biggest savings with Ministral; low-output or input-heavy pipelines see smaller gaps. If you expect tens of millions of output tokens monthly, GPT-4o-mini’s $0.60/mtok output rate will significantly increase your bill versus Ministral’s $0.20/mtok.

Choose Ministral 3 14B 2512 if you need: creative problem solving, strong persona consistency, faithful source-grounded outputs, constrained rewriting, and much lower output costs (output $0.20/mtok). It’s the best value for general-purpose assistants, content generation, and cost-sensitive high-output deployments. Choose GPT-4o-mini if you need: stronger safety calibration (score 4 vs 1), OpenAI’s file->text modality and a 128k context window with robust refusal behavior — accept higher output costs ($0.60/mtok) for those safety and integration tradeoffs.