GPT-4o-mini vs Ministral 3 8B 2512

Overview: across our 12-test suite Ministral 3 8B 2512 wins five categories, GPT-4o-mini wins one, and six categories tie. Details (score and contextual rank where available):

• Safety calibration: GPT-4o-mini 4 vs Ministral 1. GPT-4o-mini ranks 6 of 55 (tied with 3 others) — the clear safety advantage for moderation-sensitive systems; Ministral ranks 32 of 55.
• Constrained rewriting: GPT-4o-mini 3 vs Ministral 5. Ministral is tied for 1st with 4 other models — best choice for hard character/byte-limited rewriting and compression tasks.
• Persona consistency: GPT-4o-mini 4 vs Ministral 5. Ministral ties for 1st with 36 others — stronger at maintaining role and resisting injection in chat-style experiences.
• Creative problem solving: GPT-4o-mini 2 vs Ministral 3. Ministral ranks 30 of 54 whereas GPT-4o-mini ranks 47 of 54 — better for non-obvious, specific idea generation.
• Faithfulness: GPT-4o-mini 3 vs Ministral 4. Ministral’s advantage (rank 34 vs GPT-4o-mini rank 52) indicates fewer hallucinations when sticking to source material.
• Strategic analysis: GPT-4o-mini 2 vs Ministral 3. Ministral ranks higher (36 vs GPT-4o-mini 44), so it handles nuanced tradeoffs with numbers better in our tests. Ties (no clear winner): structured output 4/4 (both rank 26/54), tool calling 4/4 (both rank 18/54), classification 4/4 (both tied for 1st among 53), long context 4/4 (both rank 38/55), agentic planning 3/3 (both rank 42/54), multilingual 4/4 (both rank 36/55). Practical implications: tool selection, schema-compliant JSON, classification, and very long-context retrieval behave similarly between the two models in our testing. External math benchmarks (supplementary): GPT-4o-mini scores 52.6% on MATH Level 5 and 6.9% on AIME 2025 (Epoch AI) — these are additional data points from external benchmarks and are attributed to Epoch AI. Ministral 3 8B 2512 has no external math scores in the payload. Net: Ministral dominates the creative, persona, faithfulness and constrained-rewrite axes; GPT-4o-mini holds the safety edge and comparable performance on tool-calling, classification, structured output and long-context tasks.

Prices from the payload: GPT-4o-mini charges $0.15 input and $0.60 output per mTok; Ministral 3 8B 2512 charges $0.15 input and $0.15 output per mTok. Interpreting mTok as the payload unit, that implies per 1M input tokens = $0.15 × 1000 = $150 and per 1M output tokens = cost × 1000. Example totals when input and output volumes are equal (1M input + 1M output): GPT-4o-mini ≈ $750 ( $150 input + $600 output ) vs Ministral ≈ $300 ( $150 + $150 ) — a $450 gap per 1M/1M. For 10M/10M that gap is $4,500 (GPT-4o-mini $7,500 vs Ministral $3,000). For 100M/100M the gap is $45,000 (GPT-4o-mini $75,000 vs Ministral $30,000). If you only count output tokens, GPT-4o-mini is $600 per 1M output vs Ministral $150 per 1M output. Who should care: high-volume consumer apps, API-heavy SaaS, and startups with tight margins will see large dollar differences; teams prioritizing safety calibration or specific OpenAI features may accept GPT-4o-mini’s premium.

Choose GPT-4o-mini if: you need stronger safety calibration (rank 6/55 in our tests), require OpenAI’s documented parameters like file->text and web_search_options, or your product demands the OpenAI ecosystem despite paying ~4× more per output token. Choose Ministral 3 8B 2512 if: you need cost-efficient generation at scale (output cost $0.15 vs $0.60 per mTok), better constrained rewriting, higher faithfulness, better persona consistency, or stronger creative/problem-solving and strategic-analysis performance in our tests. If you are high-volume and cost-sensitive, pick Ministral; if safety calibration is the decisive factor, pick GPT-4o-mini.