GPT-4.1 Mini vs Ministral 3 14B 2512

Across our 12-test suite, GPT-4.1 Mini wins 4 tests, Ministral 3 14B 2512 wins 2, and 6 are ties (our win/loss/tie breakdown). Test-by-test in our testing:

• Long context: GPT-4.1 Mini 5 vs Ministral 4 — GPT-4.1 Mini ties for 1st on long context and includes a 1,047,576-token window (rank: tied for 1st of 55), which matters for retrieval and summarization over million-token documents. Ministral's 262,144 window is smaller and its long context rank is 38 of 55.
• Multilingual: GPT-4.1 Mini 5 vs Ministral 4 — GPT-4.1 Mini ties for 1st on multilingual (tied with 34 others), so it produces higher-quality non-English output in our tests.
• Agentic planning: GPT-4.1 Mini 4 vs Ministral 3 — GPT-4.1 Mini ranks 16 of 54 (tied) for agentic planning; Ministral ranks 42, so GPT-4.1 Mini better decomposes goals and recovery in our tests.
• Safety calibration: GPT-4.1 Mini 2 vs Ministral 1 — GPT-4.1 Mini ranked 12 of 55 while Ministral ranked 32, indicating GPT-4.1 Mini better distinguishes harmful vs allowed requests in our suite.
• Creative problem solving: GPT-4.1 Mini 3 vs Ministral 4 — Ministral ranked 9 of 54 on creative problem solving; it produced more non-obvious, specific ideas in our tests.
• Classification: GPT-4.1 Mini 3 vs Ministral 4 — Ministral ties for 1st (tied with 29 others) on classification, so for routing and categorization workloads it performed better.
• Ties (structured output, strategic analysis, constrained rewriting, tool calling, faithfulness, persona consistency): both models scored equally on these tasks in our tests (both scored 4 on structured output, 4 on strategic analysis, 4 on constrained rewriting, 4 on tool calling, 4 on faithfulness; both tied for 1st on persona consistency). Supplementary external math data for GPT-4.1 Mini: it scores 87.3% on MATH Level 5 and 44.7% on AIME 2025 (Epoch AI), which indicates strong math performance on those benchmarks in addition to our internal tests. In short: choose GPT-4.1 Mini when long-context, multilingual demands, or safer refusal behavior matter; choose Ministral 3 14B 2512 when creative problem solving or classification accuracy matters and cost is a priority.

GPT-4.1 Mini input costs $0.40/mTok and output $1.60/mTok; Ministral 3 14B 2512 charges $0.20/mTok input and $0.20/mTok output. The output unit price alone is 8x higher for GPT-4.1 Mini (1.6 / 0.2 = 8). Assuming a 50/50 split of input vs output tokens (explicitly stated assumption):

• 1M tokens/month (500k input + 500k output): GPT-4.1 Mini ≈ $1,000/month; Ministral 3 14B 2512 ≈ $200/month (difference $800).
• 10M tokens/month: GPT-4.1 Mini ≈ $10,000; Ministral ≈ $2,000 (difference $8,000).
• 100M tokens/month: GPT-4.1 Mini ≈ $100,000; Ministral ≈ $20,000 (difference $80,000). Who should care: startups and high-volume SaaS teams will feel the difference immediately — at scale, Ministral 3 14B 2512 reduces cloud costs dramatically, while GPT-4.1 Mini is justifiable when its long-context or multilingual strengths materially improve product outcomes.

Choose GPT-4.1 Mini if you need:

• Very large context (1,047,576-token window) for retrieval, summarization, or indexing of massive documents;
• Strong multilingual quality (5/5 in our testing);
• Better agentic planning and safer refusal behavior in our suite — accept higher cost. Choose Ministral 3 14B 2512 if you need:
• A cost-efficient model (input/output $0.20/mTok) for high-volume production;
• Better creative problem solving (4 vs 3) or top-tier classification (ties for 1st in our tests);
• A competitive, lower-cost option when your app does not require million-token context.