GPT-5 vs Mistral Small 3.2 24B

Summary of head-to-head results on our 12-test suite: GPT-5 wins 11 benchmarks, Mistral wins none, and they tie on constrained rewriting. Detailed walk-through: • Structured output: GPT-5 5 vs Mistral 4. GPT-5 is tied for 1st (tied with 24 others out of 54) — means stronger JSON/schema compliance for production integrations; Mistral ranks 26 of 54. • Strategic analysis: GPT-5 5 vs Mistral 2. GPT-5 tied for 1st (tied with 25 others of 54) — better at nuanced tradeoff reasoning and numeric tradeoffs. • Constrained rewriting: tie at 4 — both models handle compression/limits equally (rank 6 of 53 shared). • Creative problem solving: GPT-5 4 vs Mistral 2. GPT-5 ranks 9 of 54, Mistral ranks 47 of 54 — GPT-5 generates more feasible, non-obvious ideas. • Tool calling: GPT-5 5 vs Mistral 4. GPT-5 is tied for 1st (tied with 16 others of 54) — stronger function selection, arguments, and sequencing for agentic flows; Mistral ranks 18 of 54. • Faithfulness: GPT-5 5 vs Mistral 4. GPT-5 tied for 1st (tied with 32 others of 55) — better at sticking to source documents. • Classification: GPT-5 4 vs Mistral 3. GPT-5 tied for 1st (tied with 29 others of 53) — higher routing/label accuracy in our tests. • Long context: GPT-5 5 vs Mistral 4. GPT-5 tied for 1st (tied with 36 others of 55) — stronger retrieval accuracy at 30K+ tokens; Mistral ranks 38 of 55. • Safety calibration: GPT-5 2 vs Mistral 1. GPT-5 ranks 12 of 55 vs Mistral 32 of 55 — both score low in our safety calibration tests, but GPT-5 is measurably better. • Persona consistency: GPT-5 5 vs Mistral 3. GPT-5 tied for 1st (tied with 36 others of 53) — better at maintaining role/character. • Agentic planning: GPT-5 5 vs Mistral 4. GPT-5 tied for 1st (tied with 14 others of 54) — superior goal decomposition and failure recovery in our runs; Mistral ranks 16 of 54. • Multilingual: GPT-5 5 vs Mistral 4. GPT-5 tied for 1st (tied with 34 others of 55) — stronger non-English parity. External benchmarks (Epoch AI): GPT-5 scores 73.6% on SWE-bench Verified, 98.1% on MATH Level 5, and 91.4% on AIME 2025 (these are Epoch AI results, supplementary to our internal suite). No external benchmark scores for Mistral are provided in the payload. In short: GPT-5’s higher scores and top ranks indicate better reliability for coding/math-heavy, multi-step reasoning, long-context retrieval, and function-calling production use; Mistral is a lower-cost model that performs respectably on constrained rewriting but lags on most complex reasoning and multilingual tests in our comparisons.

Raw per-token pricing from the payload: GPT-5 input $1.25 per mTok and output $10 per mTok; Mistral Small 3.2 24B input $0.075 per mTok and output $0.20 per mTok. That translates to per-million-token rates of: GPT-5 input $1,250 / 1M, output $10,000 / 1M; Mistral input $75 / 1M, output $200 / 1M (the payload’s priceRatio is 50x). Example math for a realistic 50/50 input/output split: • 1M total tokens: GPT-5 ≈ $5,625 vs Mistral ≈ $137.50. • 10M total tokens: GPT-5 ≈ $56,250 vs Mistral ≈ $1,375. • 100M total tokens: GPT-5 ≈ $562,500 vs Mistral ≈ $13,750. Who should care: high-volume services, data pipelines, and consumer SaaS at tens of millions+ tokens/month will see major savings with Mistral; teams that need top-tier reasoning, tool-calling reliability, or top math/coding performance may justify GPT-5’s premium.

Choose GPT-5 if: • You need top-tier reasoning, math/coding performance, or robust tool-calling and long-context handling (GPT-5 wins 11 of 12 tests and ranks 1st on tool calling, faithfulness, long context, structured output, MATH Level 5). • You accept much higher runtime costs in exchange for fewer errors and stronger integration into agentic/tooled workflows. Choose Mistral Small 3.2 24B if: • Cost at scale is the primary constraint — Mistral’s output cost is $200 / 1M vs GPT-5’s $10,000 / 1M and a ~50x output price gap in the payload. • Your workloads are shorter, less agentic, or you can tolerate lower scores on creative problem solving, strategic analysis, and long-context tasks. Neither model 'wins' safety calibration in absolute terms here, but GPT-5 is measurably better.