GPT-5 Nano vs Mistral Medium 3.1

Overall wins and ties: Mistral Medium 3.1 wins 5 benchmarks (strategic analysis, constrained rewriting, classification, persona consistency, agentic planning), GPT-5 Nano wins 2 (structured output, safety calibration), and 5 tests tie (creative problem solving, tool calling, faithfulness, long context, multilingual). Detailed walk-through:

• Structured output: GPT-5 Nano scores 5 vs Mistral 4. GPT-5 Nano is tied for 1st with 24 other models out of 54 on JSON/format adherence; Mistral sits at rank 26 of 54. Practical impact: use GPT-5 Nano when strict schema compliance and exact format are required (e.g., machine-readable JSON outputs).

• Safety calibration: GPT-5 Nano 4 vs Mistral 2. GPT-5 Nano ranks 6 of 55 (tied with 3 others), Mistral ranks 12 of 55. In our testing GPT-5 Nano better refuses harmful prompts while permitting legitimate requests.

• Strategic analysis: Mistral 5 vs GPT-5 Nano 4. Mistral is tied for 1st (with 25 others) for nuanced tradeoff reasoning; GPT-5 Nano ranks 27 of 54. This matters for financial models, policy tradeoffs, and multi-step decision work.

• Constrained rewriting: Mistral 5 vs GPT-5 Nano 3. Mistral ties for 1st with 4 others (compression within hard limits); GPT-5 Nano ranks 31 of 53. Use Mistral when meeting strict character budgets is essential (notifications, SMS, microcopy).

• Classification: Mistral 4 vs GPT-5 Nano 3. Mistral is tied for 1st with 29 others; GPT-5 Nano rank 31 of 53. For routing, intent detection, and automated labeling Mistral performs better in our tests.

• Persona consistency & agentic planning: Mistral scores 5 on both vs GPT-5 Nano 4. Mistral is tied for 1st in persona consistency (with 36 others) and agentic planning (with 14 others); GPT-5 Nano ranks 38 and 16 respectively. This impacts multi-turn character-driven assistants and multi-step goal decomposition.

• Tool calling: tie at 4 each; both rank 18 of 54. In our tool-selection and argument-accuracy tests they behave similarly.

• Faithfulness: tie at 4 each; both rank 34 of 55. Both models are comparably good at sticking to source material in our suite.

• Long context & multilingual: both score 5 and are tied for 1st (long context tied with 36 others; multilingual tied with 34 others). Both handle 30K+ token retrieval and non-English output well in our testing.

• Creative problem solving: tie at 3 each. Neither model stood out on novel idea generation in our suite.

External math benchmarks (supplementary): GPT-5 Nano scores 95.2% on MATH Level 5 and 81.1% on AIME 2025 (Epoch AI). These external results support GPT-5 Nano's strong formal/math performance in our evaluation pipeline.

Pricing per 1,000 tokens (mtok): GPT-5 Nano input $0.05, output $0.40; Mistral Medium 3.1 input $0.40, output $2.00. Assuming a 50/50 input:output token split, per-token costs are $0.000225 for GPT-5 Nano and $0.00120 for Mistral (Mistral ≈5.33× more expensive). Monthly examples at that split: 1M tokens → GPT-5 Nano $225 vs Mistral $1,200; 10M → $2,250 vs $12,000; 100M → $22,500 vs $120,000. The payload's priceRatio (0.2) reflects that GPT-5 Nano runs roughly one-fifth the cost. Teams with millions of tokens/month (SaaS apps, high-traffic chatbots, large-scale pipelines) should care about this gap; for low-volume, high-skill tasks the higher cost of Mistral can be justified by its wins in strategic tasks.

Choose GPT-5 Nano if: you need the lowest cost per token at scale (input $0.05/mtok, output $0.40/mtok), strict structured outputs (5/5 structured output, tied for 1st), better safety calibration (4/5, rank 6), long-context and multilingual performance, or superior external math results (95.2% MATH Level 5, 81.1% AIME 2025). Choose Mistral Medium 3.1 if: you prioritize strategic analysis, agentic planning, classification, constrained rewriting, or persona consistency (it wins 5 of 12 benchmarks and ties for 1st on several), and you can absorb the higher operational cost (input $0.40/mtok, output $2.00/mtok).