GPT-4o vs Mistral Small 3.1 24B

Overview: In our 12-test internal suite GPT-4o wins 5 categories, Mistral wins 2, and 5 are ties. Detailed results (our score scale 1–5):

• Creative problem solving: GPT-4o 3 vs Mistral 2 — GPT-4o wins. This suggests GPT-4o produces more non-obvious, feasible ideas in ideation tasks. (GPT-4o ranks 30 of 54.)
• Safety calibration: tie 1 vs 1 — both models are conservative on refusal/permissiveness in our tests.
• Constrained rewriting: tie 3 vs 3 — both handle hard character limits equivalently.
• Agentic planning: GPT-4o 4 vs Mistral 3 — GPT-4o wins on goal decomposition and failure recovery; GPT-4o ranks 16 of 54 here versus Mistral rank 42.
• Structured output: tie 4 vs 4 — both match JSON/schema needs similarly (rank 26 of 54 each).
• Tool calling: GPT-4o 4 vs Mistral 1 — GPT-4o decisively wins function selection and argument sequencing; Mistral has a quirk of no_tool calling in the payload and ranks 53 of 54 on tool calling.
• Long context (30K+ tokens): GPT-4o 4 vs Mistral 5 — Mistral wins and ties for 1st on long context (tied with 36 others), indicating stronger retrieval/accuracy over very long contexts.
• Multilingual: tie 4 vs 4 — parity on non-English quality in our tests.
• Classification: GPT-4o 4 vs Mistral 3 — GPT-4o wins and is tied for 1st with many models by our ranking display, making it safer for routing and categorization tasks.
• Strategic analysis: GPT-4o 2 vs Mistral 3 — Mistral wins on nuanced tradeoff reasoning with numbers.
• Faithfulness: tie 4 vs 4 — both resist hallucination similarly in our suite.
• Persona consistency: GPT-4o 5 vs Mistral 2 — GPT-4o strongly maintains character and resists injection, tied for 1st in our rankings for this metric. External benchmarks (supplementary): GPT-4o also has Epoch AI scores in the payload: SWE-bench Verified 31% (Epoch AI), MATH Level 5 53.3% (Epoch AI), AIME 2025 6.4% (Epoch AI). These external numbers are cited from Epoch AI and supplement our internal results; Mistral Small 3.1 24B has no external benchmark entries in the payload. Practical meaning: choose GPT-4o when you need accurate function calls, consistent personas, strong classification and agentic planning. Choose Mistral when you need best-in-class long-context retrieval and lower-cost strategic analysis.

Prices per million tokens: GPT-4o charges $2.50 (input) and $10.00 (output) per M-token; Mistral Small 3.1 24B charges $0.35 (input) and $0.56 (output). If you measure cost as 1M input + 1M output tokens per month, monthly spend is $12.50 for GPT-4o vs $0.91 for Mistral. At 10M input+10M output tokens: $125.00 vs $9.10. At 100M input+100M output tokens: $1,250.00 vs $91.00. The price ratio in the payload is 17.857, meaning GPT-4o costs ~18x more per token-pair than Mistral. Who should care: startups, consumer apps, or analytics pipelines that push tens of millions of tokens/month will feel a clear budget impact and should consider Mistral; teams needing tool calling, stronger persona handling, or agentic planning may justify GPT-4o's premium at lower volumes.

Choose GPT-4o if you need reliable tool calling, strong persona consistency, classification, and agentic planning — e.g., multi-step agents, customer-service bots that must call APIs, or apps where persona fidelity matters and the token budget is moderate. Choose Mistral Small 3.1 24B if you need long-context accuracy (30K+ tokens), better strategic numerical reasoning in our tests, or you operate at high token volumes and need to minimize cost — e.g., large-scale retrieval systems, long-document summarization, or high-throughput production APIs.