GPT-5.2 vs Mistral Small 3.2 24B

Across our 12-test suite GPT-5.2 wins 9 benchmarks, ties 3, and Mistral Small 3.2 24B wins none. Detailed comparisons (scores are our 1–5 internal ratings unless otherwise noted):

• Strategic analysis: GPT-5.2 5 vs Mistral 2 — GPT-5.2 tied for 1st of 54 (top-tier for nuanced tradeoffs). This matters for financial models, pricing engines, and optimization tasks.
• Creative problem solving: GPT-5.2 5 vs Mistral 2 — GPT-5.2 tied for 1st of 54 (better at non-obvious, feasible ideas).
• Faithfulness: GPT-5.2 5 vs Mistral 4 — GPT-5.2 tied for 1st of 55 (less hallucination risk in our tests). Important for summarization and citation-sensitive outputs.
• Classification: GPT-5.2 4 vs Mistral 3 — GPT-5.2 tied for 1st of 53 (more reliable routing and tagging).
• Long context: GPT-5.2 5 vs Mistral 4 — GPT-5.2 tied for 1st of 55; Mistral ranks 38 of 55. GPT-5.2 is markedly better on 30k+ token retrieval tasks.
• Safety calibration: GPT-5.2 5 vs Mistral 1 — GPT-5.2 tied for 1st of 55 (safer refusal/allow behavior in our tests).
• Persona consistency & agentic planning: GPT-5.2 scores 5/5 on both vs Mistral 3/4 — GPT-5.2 tied for 1st on persona and agentic planning (helpful for assistants and multi-step agents).
• Multilingual: GPT-5.2 5 vs Mistral 4 — GPT-5.2 tied for 1st of 55 (better non-English parity in our tests).
• Ties: structured output (both 4), constrained rewriting (both 4), tool calling (both 4) — for JSON schema compliance and basic function selection both models perform similarly in our suite. Supplementary external benchmarks: GPT-5.2 scores 73.8% on SWE-bench Verified and 96.1% on AIME 2025 according to Epoch AI; Mistral Small 3.2 24B has no external scores in the payload. These external results support GPT-5.2’s strength on coding and high-difficulty math in third-party measures. Overall, GPT-5.2 is the higher-performing model for complex reasoning, long context, and safety-sensitive tasks; Mistral is competent on many structured and tool workflows but scores lower on core reasoning benchmarks.

The payload prices are: GPT-5.2 input $1.75/mtok and output $14/mtok; Mistral Small 3.2 24B input $0.075/mtok and output $0.20/mtok. Using a 50/50 input/output token split as a baseline: per 1M total tokens GPT-5.2 costs about $7,875 (500k input = $875; 500k output = $7,000). Mistral costs about $137.50 per 1M total tokens (500k input = $37.50; 500k output = $100). At scale that gap grows linearly: ~ $78,750 vs $1,375 for 10M tokens, and ~ $787,500 vs $13,750 for 100M tokens. The payload also reports a priceRatio of 70 (GPT-5.2 ≈70× more expensive). Who should care: startups and products with sustained multi‑million token volumes will see immediate budget impact and should consider Mistral for cost control; teams needing top-tier reasoning, safety, and long-context performance may justify GPT-5.2’s much higher cost.

Choose GPT-5.2 if you need best-in-benchmark reasoning, safety, and long-context performance (examples: complex financial/medical analysis, multi-step agents, long-document summarization, competitive math/coding tasks). GPT-5.2 wins 9 of 12 benchmarks and posts strong external results (AIME 96.1%, SWE-bench 73.8% per Epoch AI). Choose Mistral Small 3.2 24B if you must minimize inference spend at scale and can accept lower reasoning headroom — it costs roughly $137.50 per 1M tokens (50/50 split) vs GPT-5.2’s ~$7,875 per 1M. Mistral is a practical choice for high-volume chat, lightweight instruction following, or when cost per token is the primary constraint.