GPT-5.2 vs Mistral Medium 3.1

Across our 12-test suite, GPT-5.2 wins 3 benchmarks, Mistral Medium 3.1 wins 1, and 8 are ties. Detailed walk-through (scores are our 1–5 internal ratings unless otherwise noted):

• Safety calibration: GPT-5.2 5 vs Mistral 2 — GPT-5.2 wins in our testing (rank: tied for 1st out of 55, per rankingsA). This matters for content-moderation and compliance workflows where refusal/allow behavior must be reliable.
• Faithfulness: GPT-5.2 5 vs Mistral 4 — GPT-5.2 wins (ranked tied for 1st of 55). Higher faithfulness means fewer hallucinations when sticking to source material.
• Creative problem solving: GPT-5.2 5 vs Mistral 3 — GPT-5.2 wins (tied for 1st of 54). Expect more non-obvious, practical ideas and solutions in brainstorming and product-design tasks.
• Constrained rewriting: GPT-5.2 4 vs Mistral 5 — Mistral wins here (Mistral tied for 1st of 53). Mistral is better at tight-character compression and strict-format rewriting.
• Structured output: tie 4 vs 4 — both models are competent at JSON/schema compliance (GPT-5.2 rank 26/54; Mistral rank 26/54).
• Strategic analysis: tie 5 vs 5 — both score top marks for nuanced tradeoff reasoning (GPT-5.2 tied for 1st; Mistral tied for 1st).
• Tool calling: tie 4 vs 4 — both are capable at function selection and sequencing (GPT-5.2 rank 18/54; Mistral rank 18/54).
• Classification: tie 4 vs 4 — both rank tied for 1st on routing/categorization.
• Long context: tie 5 vs 5 — both excel at retrieval across 30k+ tokens (GPT-5.2 tied for 1st of 55; Mistral tied for 1st of 55). Note GPT-5.2 provides a 400k context window vs Mistral’s 131k, which affects absolute usable history.
• Persona consistency and agentic planning: ties at 5 — both models maintain persona and decompose goals well (both tied for 1st on these dimensions).
• Multilingual: ties at 5 — parity on non-English output quality (both tied for 1st).

External (Epoch AI) benchmarks where available: on SWE-bench Verified (Epoch AI) GPT-5.2 scores 73.8% (rank 5 of 12 in the payload), and on AIME 2025 (Epoch AI) GPT-5.2 scores 96.1% (rank 1 of 23, sole holder). These external results support GPT-5.2’s strength on coding/problem-solving and high-school/competition math tasks. Mistral Medium 3.1 has no external scores in the payload to cite. Overall, GPT-5.2’s wins are concentrated in safety, faithfulness and creative problem solving; Mistral’s clear advantage is constrained rewriting plus a large cost advantage.

List prices from the payload: GPT-5.2 input $1.75 /Mtoken and output $14 /Mtoken; Mistral Medium 3.1 input $0.40 /Mtoken and output $2 /Mtoken. Using a 50/50 input:output traffic assumption, cost per 1M combined tokens is $7.875 for GPT-5.2 and $1.20 for Mistral Medium 3.1. At scale: 1M tokens/month → GPT-5.2 $7.88 vs Mistral $1.20; 10M → GPT-5.2 $78.75 vs Mistral $12.00; 100M → GPT-5.2 $787.50 vs Mistral $120.00. The payload also reports a priceRatio of 7, reflecting GPT-5.2’s substantially higher per-token rates. Teams with high-volume inference (millions of tokens/month), tight margins, or large multi-tenant deployments should care most about this cost gap; organizations that need best-in-class safety, faithfulness, or math/benchmark-winning performance may justify GPT-5.2’s higher price.

Choose GPT-5.2 if you need top-tier safety calibration, faithfulness, creative problem solving, or benchmark-leading math/coding performance (see AIME 2025 96.1% and SWE-bench 73.8% from Epoch AI), and you can absorb higher per-token costs and want a 400k context window. Choose Mistral Medium 3.1 if you are cost-sensitive at scale (≈ $1.20 per 1M tokens at a 50/50 in/out split vs GPT-5.2’s $7.88), need best-in-class constrained rewriting, or want similar long-context, agentic planning and multilingual quality at a fraction of the price.