GPT-4.1 Nano vs Mistral Small 3.2 24B

Across our 12-test suite, GPT-4.1 Nano wins 4 benchmarks, Mistral Small 3.2 24B wins 0, and the rest are ties. Detailed walk-through (all scores are from our testing unless otherwise noted):

• Structured output: GPT-4.1 Nano scores 5 vs Mistral’s 4; GPT-4.1 Nano is tied for 1st on this test ("tied for 1st with 24 other models out of 54 tested"). This matters when you need strict JSON/schema compliance for APIs, tool integration, or machine-readable results.

• Faithfulness: GPT-4.1 Nano scores 5 vs Mistral 4; GPT-4.1 Nano is tied for 1st on faithfulness ("tied for 1st with 32 other models out of 55 tested"). Expect fewer hallucinations and tighter adherence to source material in our trials.

• Safety calibration: GPT-4.1 Nano 2 vs Mistral 1; GPT-4.1 Nano ranks 12 of 55 ("rank 12 of 55 (20 models share this score)") while Mistral ranks 32 of 55. Both scores are low overall, but Nano was clearer in refusing harmful prompts in our tests.

• Persona consistency: GPT-4.1 Nano 4 vs Mistral 3; Nano ranks 38 of 53 vs Mistral’s 45 of 53, so Nano maintains character and resists injection better in our evaluations.

Ties (both models scored the same in our tests):

• Tool calling: both 4 ("rank 18 of 54 (29 models share this score)") — sequencing and argument selection look comparable in practice.
• Constrained rewriting: both 4 ("rank 6 of 53 (25 models share this score)") — both handle tight character/format compression similarly.
• Creative problem solving: both 2 (low relative performance).
• Classification: both 3 ("rank 31 of 53 (20 models share this score)") — similar routing/labeling accuracy.
• Long context: both 4 ("rank 38 of 55 (17 models share this score)") — both handle 30K+ token retrieval comparably.
• Agentic planning: both 4 ("rank 16 of 54 (26 models share this score)") — goal decomposition/failover comparable.
• Strategic analysis, multilingual: both score 2 and 4 respectively (ties).

External math benchmarks (Epoch AI): GPT-4.1 Nano scores 70% on MATH Level 5 and 28.9% on AIME 2025 (according to Epoch AI). Mistral Small 3.2 24B has no external math scores in the payload. These external results indicate GPT-4.1 Nano has measurable capability on high-difficulty math tests in addition to our internal scores.

Bottom line of the tests: GPT-4.1 Nano’s wins are concentrated on structured outputs, faithfulness, safety calibration, and persona consistency — the precise areas that matter for production integrations and trustworthy responses. Mistral is competitive on many other core capabilities at a lower price.

Per the payload, GPT-4.1 Nano charges $0.10 per M input tokens and $0.40 per M output tokens; Mistral Small 3.2 24B charges $0.075 per M input and $0.20 per M output. Using a simple 50/50 input/output split, GPT-4.1 Nano costs $0.25 per 1M tokens, vs $0.1375 per 1M for Mistral — a $0.1125 difference per 1M. At common volumes that amounts to: 1M tokens/mo → $0.25 (GPT) vs $0.1375 (Mistral); 10M → $2.50 vs $1.375; 100M → $25.00 vs $13.75 (an $11.25 monthly gap). The payload’s priceRatio is 2.0, reflecting that GPT-4.1 Nano is about twice the per-token cost in typical usage. Teams operating at >10M–100M tokens/month (chat apps, large-scale assistants, content farms) will feel this gap; small-scale prototypes or expensive-per-request use cases should weigh GPT-4.1 Nano’s quality wins against the doubled cost.

Choose GPT-4.1 Nano if you need: strict schema/JSON outputs, higher faithfulness (fewer hallucinations), better safety calibration, or stronger persona consistency for production-grade assistants and API integrations. Its internal wins (structured output 5/tied for 1st, faithfulness 5/tied for 1st, safety calibration 2 vs 1) justify the premium when correctness and format are critical.

Choose Mistral Small 3.2 24B if you need: a cost-efficient model that matches GPT-4.1 Nano on tool calling, long-context retrieval, classification, constrained rewriting, and agentic planning (ties in our tests). Mistral is the practical pick for high-volume, budget-sensitive deployments where those tied capabilities dominate and absolute best-in-class schema/faithfulness is not required.