GPT-5 Nano vs Mistral Small 3.2 24B

Summary of head-to-heads (scores from our 12-test suite): GPT-5 Nano wins on structured output (5 vs 4), strategic analysis (4 vs 2), creative problem solving (3 vs 2), long context (5 vs 4), safety calibration (4 vs 1), persona consistency (4 vs 3), and multilingual (5 vs 4) — seven wins. Mistral Small 3.2 24B wins constrained rewriting (4 vs 3) — one win. They tie on tool calling (4/4), faithfulness (4/4), classification (3/3), and agentic planning (4/4). What this means for real tasks: • Structured output (JSON schema compliance): GPT-5 Nano scores 5 and is tied for 1st ("tied for 1st with 24 other models out of 54 tested"), so expect more reliable JSON/format adherence in production APIs. • Long context: Nano scores 5 and is tied for 1st ("tied for 1st with 36 other models out of 55 tested"), so it better preserves accuracy over 30K+ token contexts. • Safety: Nano’s 4 vs Mistral’s 1 (rank 6 vs rank 32) indicates Nano refuses harmful requests more reliably in our tests. • Strategic analysis & creative problem solving: Nano’s 4 vs Mistral’s 2 shows noticeably better nuanced reasoning and idea generation in our suite. • Constrained rewriting: Mistral’s 4 (rank 6 of 53) beats Nano’s 3 — Mistral is preferable when you must compress text into tight character limits. • Tool calling and faithfulness: both models tie at 4 — expect similar function selection and sticking-to-source behavior in our tests. Additional external context: GPT-5 Nano also posts strong external math scores — 95.2% on MATH Level 5 and 81.1% on AIME 2025 (Epoch AI) — which corroborates its strength on complex, formal reasoning tasks (these external scores are from Epoch AI).

Pricing (from payload): GPT-5 Nano input $0.05/mtok, output $0.40/mtok; Mistral Small 3.2 input $0.075/mtok, output $0.20/mtok. The clearest gap: GPT-5 Nano’s output is 2× Mistral’s (priceRatio = 2.0). Example monthly costs assuming a 50/50 split of input/output tokens: • 1M tokens: GPT-5 Nano ≈ $0.225/month, Mistral ≈ $0.1375/month. • 10M tokens: GPT-5 Nano ≈ $2.25/month, Mistral ≈ $1.375/month. • 100M tokens: GPT-5 Nano ≈ $22.50/month, Mistral ≈ $13.75/month. If you instead bill purely by output tokens, 1M output tokens cost $0.40 (Nano) vs $0.20 (Mistral). Who should care: startups and high-volume apps generating large amounts of output (10M–100M+ tokens/month) will save materially with Mistral on output-heavy workloads; teams that need superior structured outputs, long-context handling, or safety may accept Nano’s higher output cost for better task fit.

Choose GPT-5 Nano if you need: • Reliable structured outputs (JSON/schema-heavy APIs), long-context accuracy (30K+ tokens), stronger safety calibration, multilingual parity, or higher-quality strategic analysis — you’ll pay higher output costs ($0.40/mtok). Choose Mistral Small 3.2 24B if you need: • Lower output cost ($0.20/mtok) for high-volume generative workloads, superior constrained rewriting (compression into hard limits), or the best price/throughput tradeoff when tool calling and faithfulness parity are sufficient.