GPT-4o vs Mistral Small 3.2 24B

Overview: our 12-test suite shows GPT-4o wins 3 tasks (creative problem solving 3 vs 2, classification 4 vs 3, persona consistency 5 vs 3), Mistral wins 1 (constrained rewriting 4 vs 3), and 8 ties. Details: - creative problem solving: GPT-4o 3 vs Mistral 2 — GPT-4o ranks 30 of 54 on this test, so expect better non-obvious idea generation in our runs. - classification: GPT-4o 4 vs Mistral 3 — GPT-4o is tied for 1st (tied with 29 others) out of 53 on classification, indicating stronger routing/labeling in our tests. - persona consistency: GPT-4o 5 vs Mistral 3 — GPT-4o ties for 1st (with 36 others) on maintaining character and resisting injection in our benchmarks. - constrained rewriting: Mistral 4 vs GPT-4o 3 — Mistral ranks 6 of 53 (tie) on tight compression/limits, so it’s the better choice for strict character-limit rewriting. - Ties (structured output 4/4, strategic analysis 2/2, tool calling 4/4, faithfulness 4/4, long context 4/4, safety calibration 1/1, agentic planning 4/4, multilingual 4/4): both models match on these tasks in our tests; e.g., both score 4 on tool calling and rank 18 of 54, so function selection and sequencing are comparable per our suite. External benchmarks (secondary context): GPT-4o posts 31% on SWE-bench Verified (Epoch AI), 53.3% on MATH Level 5, and 6.4% on AIME 2025 (all per Epoch AI); those external math/coding numbers place GPT-4o at rank 12/12 on SWE-bench Verified and near the bottom on the math olympiad tests in our set, which signals limited performance on those specific third-party benchmarks in our runs. Note: Mistral Small 3.2 24B has no external SWE/MATH/AIME scores in the payload; absence of external data is reported, not penalized. All benchmark claims above reflect our 12-test suite and provided rankings.

Raw per-million-token output costs: GPT-4o = $10.00 per M output tokens; Mistral Small 3.2 24B = $0.20 per M output tokens (50x cheaper). Input costs: GPT-4o $2.50 per M input, Mistral $0.075 per M input. If you budget on outputs only: 1M output tokens/month = $10 vs $0.20; 10M = $100 vs $2; 100M = $1,000 vs $20. If you assume equal input+output volume (1M input + 1M output): GPT-4o = $12.50 per M tokens vs Mistral = $0.275 per M tokens — so 10M combined tokens = $125 vs $2.75, and 100M = $1,250 vs $27.50. Who should care: startups and hobbyists shipping prototypes will see large savings with Mistral at scale; product teams with high accuracy or persona requirements may accept GPT-4o's premium but should budget accordingly (tens to thousands of dollars monthly depending on volume).

Choose GPT-4o if: you need the best classification and persona-consistency behavior in our tests, or you require GPT-4o's modality set (text+image+file->text) and you can absorb the 50x output cost gap. Use cases: customer routing, character-driven assistants, and apps where small accuracy gains justify higher spend. Choose Mistral Small 3.2 24B if: cost per token and throughput matter and you need strong constrained-rewriting or equivalent performance on 8 tied tasks. Use cases: high-volume content generation, cost-sensitive prototypes, and production workloads where tight budgets trump marginal gains.