GPT-5.4 vs Mistral Medium 3.1

Across our 12-test suite, GPT-5.4 wins 4 tests, Mistral Medium 3.1 wins 2, and 6 tests tie. Detailed comparisons (scores are our 1–5 internal ratings unless otherwise noted): - Structured output: GPT-5.4 5 vs Mistral 4 — GPT-5.4 is tied for 1st (ranked tied for 1st with 24 others out of 54), meaning it better adheres to JSON/schema constraints for pipelines. - Creative problem solving: GPT-5.4 4 vs Mistral 3 — GPT-5.4 is stronger for non-obvious, feasible idea generation (rank 9 of 54). - Faithfulness: GPT-5.4 5 vs Mistral 4 — GPT-5.4 ranks tied for 1st (with 32 others out of 55), reducing hallucination risk for summarization and retrieval tasks. - Safety calibration: GPT-5.4 5 vs Mistral 2 — a large gap; GPT-5.4 is tied for 1st (with 4 others out of 55) and will refuse harmful requests more reliably per our tests. - Constrained rewriting: GPT-5.4 4 vs Mistral 5 — Mistral wins here (tied for 1st), so it’s better when you must compress or strictly fit character-limited outputs. - Classification: GPT-5.4 3 vs Mistral 4 — Mistral ranks tied for 1st in classification (with 29 others), making it preferable for routing/categorization tasks. - Ties (both models): strategic analysis 5, tool calling 4, long context 5, persona consistency 5, agentic planning 5, multilingual 5 — both models match on nuanced reasoning, tool selection sequencing, 30K+ retrieval accuracy, persona stability, decomposition and failure recovery, and non-English quality in our tests. External benchmarks: GPT-5.4 scores 76.9% on SWE-bench Verified and 95.3% on AIME 2025 (both from Epoch AI), where it ranks 2nd of 12 on SWE-bench Verified and 3rd of 23 on AIME 2025; Mistral Medium 3.1 has no external SWE-bench/AIME scores in the payload. Practical implication: pick GPT-5.4 when safety, faithfulness, and strict schema adherence matter; pick Mistral for constrained-rewrite and classification workloads or when cost is the dominant constraint.

Prices (per 1,000 tokens): GPT-5.4 input $2.50, output $15.00; Mistral Medium 3.1 input $0.40, output $2.00. Price ratio in the payload is 7.5×. Assuming a 50/50 split between input and output tokens: - 1M total tokens/month (500k input + 500k output): GPT-5.4 ≈ $8,750; Mistral ≈ $1,200. - 10M tokens/month: GPT-5.4 ≈ $87,500; Mistral ≈ $12,000. - 100M tokens/month: GPT-5.4 ≈ $875,000; Mistral ≈ $120,000. Who should care: teams with heavy throughput (10M+ tokens/month), SaaS products, or consumer-scale services must evaluate the ~7.5× operational cost gap — Mistral materially reduces spend. Enterprises that need the highest safety and faithfulness may justify GPT-5.4’s cost; startups and high-volume applications will often prefer Mistral Medium 3.1 for cost control.

Choose GPT-5.4 if: - You need top-tier safety calibration and faithfulness (GPT-5.4 scores 5 vs Mistral’s 2 and 4 respectively). - Your application requires strict structured output/JSON compliance (GPT-5.4 5 vs 4) or benefits from GPT’s SWE-bench Verified 76.9% and AIME 95.3% results (Epoch AI). - You can absorb much higher inference costs (input $2.50 / output $15 per 1,000 tokens). Choose Mistral Medium 3.1 if: - You must minimize operating cost (input $0.40 / output $2 per 1,000 tokens; ~7.5× cheaper). - Your primary needs are classification or constrained rewriting (Mistral wins those tests: classification 4 vs 3, constrained-rewriting 5 vs 4). - You need a strong multilingual or long-context model at a much lower price and can accept lower safety calibration and slightly lower faithfulness.