GPT-4.1 Mini vs Ministral 3 3B 2512

Summary from our 12-test suite: GPT-4.1 Mini wins 6 tests, Ministral 3 3B 2512 wins 3, and 3 are ties. Detailed breakdown: - Strategic analysis: GPT-4.1 Mini 4 vs Ministral 2 — GPT-4.1 Mini ranks 27 of 54 (score 4) vs Ministral rank 44 of 54 (score 2); this means GPT-4.1 Mini handles nuanced tradeoff reasoning (real-number reasoning) noticeably better in our tests. - Long context: GPT-4.1 Mini 5 vs Ministral 4 — GPT-4.1 Mini is tied for 1st of 55 models (score 5) while Ministral ranks 38/55 (score 4); expect stronger retrieval and coherence past 30K tokens on GPT-4.1 Mini. - Safety calibration: GPT-4.1 Mini 2 vs Ministral 1 — GPT-4.1 Mini ranks 12/55 vs Ministral 32/55; GPT-4.1 Mini is more likely to follow safety guardrails in our calibration tests. - Persona consistency: GPT-4.1 Mini 5 vs Ministral 4 — GPT-4.1 Mini tied for 1st (36 others) vs Ministral rank 38/53; better at maintaining character and resisting injection. - Agentic planning: GPT-4.1 Mini 4 vs Ministral 3 — GPT-4.1 Mini rank 16/54 vs Ministral 42/54; better goal decomposition and recovery behavior in our agentic tests. - Multilingual: GPT-4.1 Mini 5 vs Ministral 4 — GPT-4.1 Mini tied for 1st of 55 vs Ministral rank 36/55; stronger non-English parity. - Wins for Ministral: Constrained rewriting 5 vs GPT-4.1 Mini 4 — Ministral tied for 1st (4 others), so it’s excellent at strict compression/formatting tasks. Faithfulness 5 vs 4 — Ministral tied for 1st (32 others) vs GPT-4.1 Mini rank 34/55; expect fewer hallucinations when source fidelity is critical. Classification 4 vs 3 — Ministral tied for 1st (29 others) vs GPT-4.1 Mini rank 31/53; better at routing/categorization workloads in our tests. - Ties: Structured output 4/4 (both rank 26/54), Creative problem solving 3/3 (both rank 30/54), Tool calling 4/4 (both rank 18/54) — in these areas you can expect similar behavior. - External math benchmarks (supplementary): According to Epoch AI, GPT-4.1 Mini scores 87.3% on MATH Level 5 and 44.7% on AIME 2025; Ministral 3 3B 2512 has no reported scores for those tests in the payload. Overall, GPT-4.1 Mini wins where context length, multilingual output, persona, planning, and safety matter; Ministral 3 3B 2512 wins where low-cost, faithfulness, constrained rewriting, and classification are priorities.

Per-mTok pricing from the payload: GPT-4.1 Mini charges $0.40 input / $1.60 output per mTok; Ministral 3 3B 2512 charges $0.10 input / $0.10 output per mTok. For parity (1M input + 1M output tokens = 1,000 mTok each): GPT-4.1 Mini = $0.40×1000 + $1.60×1000 = $2,000; Ministral 3 3B 2512 = $0.10×1000 + $0.10×1000 = $200. At 10M in+out tokens/month: $20,000 vs $2,000. At 100M in+out tokens/month: $200,000 vs $20,000. The payload also reports a priceRatio of 16 (GPT-4.1 Mini output $1.60 / Ministral output $0.10). Teams with high-volume inference (classification routing, chat fleets, data labeling) should care deeply about the cost gap; small teams or projects that require GPT-4.1 Mini’s long-context and multilingual strengths may justify the higher spend.

Choose GPT-4.1 Mini if you need: - Excellent long-context handling (5/5, tied for 1st) for document retrieval, multi-file reasoning, or 1M-token workflows. - Best-in-class multilingual and persona consistency (5/5 each, tied for top ranks). - Strong agentic planning and safer refusals in production. Accept the higher spend (output $1.60/mTok) when those capabilities reduce downstream engineering or error costs. Choose Ministral 3 3B 2512 if you need: - A highly cost-efficient model for high-volume classification, routing, or constrained-rewrite tasks (output $0.10/mTok). - Top-tier constrained rewriting (5/5, tied for 1st) or faithfulness (5/5, tied for 1st) with much lower inference cost. - A budget-first deployment where 16× lower output cost materially changes feasibility.