GPT-5.4 vs Ministral 3 3B 2512

Summary of our 12-test suite (scores are from our testing): GPT-5.4 wins 8 benchmarks, Ministral 3 3B 2512 wins 2, and 2 are ties. Detailed walk-through:

• Structured output: GPT-5.4 5 vs Ministral 4. GPT-5.4 is tied for 1st (rank: tied for 1st with 24 others of 54), indicating superior JSON/schema compliance for integrations and data pipelines.

• Strategic analysis: GPT-5.4 5 vs Ministral 2. GPT-5.4 ranks tied for 1st (25 others) — this matters for nuanced tradeoff reasoning and financial/modeling tasks.

• Creative problem solving: GPT-5.4 4 vs Ministral 3. GPT-5.4 ranks 9th of 54 (tied) versus Ministral at rank 30 — GPT-5.4 produces more non-obvious, feasible ideas.

• Long context: GPT-5.4 5 vs Ministral 4. GPT-5.4 is tied for 1st (36 others of 55) and has a 1,050,000 token window versus 131,072 for Ministral — critical for summarizing, retrieval, and multi-file codebases.

• Safety calibration: GPT-5.4 5 vs Ministral 1. GPT-5.4 is tied for 1st (4 others) — it better refuses harmful prompts while allowing legitimate ones.

• Persona consistency & Multilingual: GPT-5.4 scores 5 vs Ministral 4 on both; GPT-5.4 ranks tied for 1st in persona consistency and multilingual tests, meaning more reliable role-playing and non-English parity.

• Agentic planning: GPT-5.4 5 vs Ministral 3. GPT-5.4 tied for 1st (with 14 others) vs Ministral ranked 42 — GPT-5.4 is stronger at goal decomposition and failure recovery for agents.

• Faithfulness: tie at 5 for both; both models top-rank (GPT-5.4 tied for 1st, Ministral also tied for 1st), signaling similar ability to stick to source material on our tests.

• Tool calling: tie at 4 for both, rank 18 of 54 — both are competent at selecting and sequencing function calls.

• Constrained rewriting: Ministral 5 vs GPT-5.4 4. Ministral is tied for 1st (with 4 others) — better at tight-character compressions and forced-length rewrites.

• Classification: Ministral 4 vs GPT-5.4 3. Ministral ties for 1st on classification (with 29 others) — preferable for routing and tagging tasks.

External/third-party benchmarks: GPT-5.4 scores 76.9% on SWE-bench Verified and 95.3% on AIME 2025 (both reported by Epoch AI); those external results corroborate its strength on coding and math benchmarks. The payload contains no external SWE-bench or AIME scores for Ministral 3 3B 2512.

Practical interpretation: GPT-5.4 is the clear choice for high-stakes, long-context, safety-sensitive, and complex reasoning tasks; Ministral 3 3B 2512 is stronger where tight compression and classification efficiency matter and is drastically cheaper per token.

Pricing per 1,000 tokens (mTok) is GPT-5.4 input $2.50 / mTok and output $15.00 / mTok; Ministral 3 3B 2512 is input $0.10 / mTok and output $0.10 / mTok. Assuming a 50/50 input/output split: for 1M tokens/month (1,000 mTok) GPT-5.4 costs $8,750 (500 mTok input × $2.50 = $1,250; 500 mTok output × $15 = $7,500). Ministral costs $100 (500 mTok × $0.1 × 2). At 10M tokens/month GPT-5.4 ≈ $87,500 vs Ministral ≈ $1,000. At 100M tokens/month GPT-5.4 ≈ $875,000 vs Ministral ≈ $10,000. The payload’s priceRatio is 150, reflecting GPT-5.4’s ~150× higher output cost per mTok. Who should care: product teams and startups with heavy inference volumes (10M+ tokens/month) will see material cost differences; teams needing top-tier safety, long-context, or advanced planning may accept GPT-5.4’s premium. Low-latency, cost-constrained deployments or experimentation pipelines should prefer Ministral 3 3B 2512.

Choose GPT-5.4 if you need: large-context summarization or retrieval (1,050,000 token window), top-tier safety calibration (5 vs 1), advanced agentic planning, strategic analysis, schema/structured output compliance, or strong multilingual and persona consistency — accept the higher token cost for these gains. Choose Ministral 3 3B 2512 if you need: a low-cost production model for classification, constrained rewriting, vision->text tasks, or large-volume, cost-sensitive inference (output $0.1/mTok); it’s the practical choice for apps where per-token price dominates and state-of-the-art safety/long-context are not required.