GPT-4.1 Mini vs Ministral 3 8B 2512

Summary of head-to-head results (our 12-test suite): GPT-4.1 Mini wins 5 benchmarks, Ministral 3 8B 2512 wins 2, and 5 tests tie. Details by test:

• Long-context: GPT-4.1 Mini 5 vs Ministral 4. GPT-4.1 Mini ties for 1st in our ranking ("tied for 1st with 36 other models out of 55 tested") and provides a 1,047,576-token context window vs Ministral's 262,144 — this matters for retrieval, summarizing large documents, and multimodal file workflows.
• Multilingual: GPT-4.1 Mini 5 vs Ministral 4. GPT-4.1 Mini is tied for 1st (with 34 others) — pick it when non‑English fidelity matters.
• Safety calibration: GPT-4.1 Mini 2 vs Ministral 1. GPT-4.1 Mini ranks 12 of 55 vs Ministral 32 of 55 — GPT-4.1 Mini is better at refusing harmful requests while permitting legitimate ones in our tests.
• Agentic planning: GPT-4.1 Mini 4 vs Ministral 3. GPT-4.1 Mini ranks 16 of 54 vs Ministral 42 of 54 — better goal decomposition and recovery for multi-step agents.
• Strategic analysis: GPT-4.1 Mini 4 vs Ministral 3. GPT-4.1 Mini ranks 27 of 54 vs Ministral 36 of 54 — stronger nuanced tradeoff reasoning in our tests.
• Constrained rewriting: GPT-4.1 Mini 4 vs Ministral 5 — Ministral ties for 1st (tied with 4 others) and wins this test, useful for strict character limits and compression tasks.
• Classification: GPT-4.1 Mini 3 vs Ministral 4 — Ministral ties for 1st with 29 others (ranked top in our classification benchmark), so it’s the better router/tagger in our suite.
• Structured output, creative problem solving, tool calling, faithfulness, persona consistency: ties (both score equal). Structured output ranks are mid-table (rank 26 of 54). Tool calling scored 4/5 for both (rank 18 of 54), meaning both select and sequence functions competently in our test scenarios.
• External math benchmarks (supplementary, Epoch AI): GPT-4.1 Mini scores 87.3% on MATH Level 5 and 44.7% on AIME 2025 (Epoch AI); Ministral 3 8B 2512 has no MATH/AIME scores in the payload. These external results support GPT-4.1 Mini's relative strength on higher-difficulty math in our supplementary data.

Pricing in the payload: GPT-4.1 Mini charges $0.40 input + $1.60 output per mTok; Ministral 3 8B 2512 charges $0.15 input + $0.15 output per mTok. Assuming a 1:1 split of input:output tokens (common for chat), per-mTok totals are $2.00 for GPT-4.1 Mini vs $0.30 for Ministral 3 8B 2512 (a 6.67x total-cost gap). Concrete monthly examples (1 mTok = 1,000 tokens):

• 1M tokens (1,000 mTok): GPT-4.1 Mini = $2,000; Ministral = $300.
• 10M tokens (10,000 mTok): GPT-4.1 Mini = $20,000; Ministral = $3,000.
• 100M tokens (100,000 mTok): GPT-4.1 Mini = $200,000; Ministral = $30,000. Note: the payload also exposes an output-cost ratio (1.6 / 0.15 = 10.6667), labeled priceRatio in the data — output tokens alone are ~10.67x more expensive on GPT-4.1 Mini. Who should care: startups, high-volume SaaS, and any product with millions of output tokens/month must weigh these multi-thousand-dollar differences; teams prioritizing long-context, multilingual quality, or safety calibration may accept the higher bill for GPT-4.1 Mini.

Choose GPT-4.1 Mini if: you need best-in-class long-context handling (1,047,576-token window), stronger multilingual output, better safety calibration, agentic planning, or higher math performance (MATH Level 5 87.3%, AIME 2025 44.7% per Epoch AI in the payload). Choose Ministral 3 8B 2512 if: per-token cost is a primary constraint (payload pricing totals $0.30/mTok vs $2.00/mTok assumed 1:1 I/O), you need top-tier constrained rewriting or classification (Ministral wins both), or you must keep operating costs low at scale.