GPT-5 vs Mistral Small 3.1 24B

Across our 12-test suite GPT-5 wins 11 categories, Mistral wins none, and they tie on long context. Key head-to-heads: tool calling — GPT-5 scores 5 vs Mistral 1; GPT-5 is “tied for 1st with 16 other models out of 54 tested” on tool calling, while Mistral ranks 53 of 54. That gap matters for function selection, argument accuracy, and sequencing. Structured_output — GPT-5 5 vs Mistral 4; GPT-5 is tied for 1st of 54 on JSON/schema tasks, so it’s safer for strict format adherence. Strategic_analysis — GPT-5 5 vs Mistral 3; GPT-5 ranks tied for 1st, so nuanced tradeoff reasoning and multi-step numeric decisions favor GPT-5. Faithfulness — GPT-5 5 vs Mistral 4; GPT-5 is tied for 1st of 55 on sticking to source material. Creative_problem_solving — GPT-5 4 vs Mistral 2; GPT-5 ranks 9 of 54. Classification and persona consistency similarly favor GPT-5 (classification 4 vs 3; persona consistency 5 vs 2). Long-context is a tie: both score 5 and are “tied for 1st with 36 other models out of 55 tested,” but note GPT-5 offers a 400,000-token window vs Mistral’s 128,000. External benchmarks: on SWE-bench Verified (Epoch AI) GPT-5 scores 73.6%; on MATH Level 5 GPT-5 scores 98.1% (rank 1 of 14), and on AIME 2025 GPT-5 scores 91.4% (rank 6 of 23). Mistral Small 3.1 24B has no external benchmark scores in the payload. In short: GPT-5’s higher scores translate to fewer format failures, more reliable tool-driven agents, and stronger math/reasoning performance; Mistral’s strengths are long-context parity and much lower cost, but it lacks tool-calling capability (quirk: no_tool calling).

Output cost per million tokens: GPT-5 $10.00 vs Mistral Small 3.1 24B $0.56 (price ratio ≈ 17.86). Input cost per million tokens: GPT-5 $1.25 vs Mistral $0.35. Output-only monthly costs: for 1M tokens GPT-5 = $10, Mistral = $0.56; 10M → $100 vs $5.60; 100M → $1,000 vs $56. If you bill for both input+output tokens at a 1:1 ratio, totals are: 1M → GPT-5 $11.25 vs Mistral $0.91; 10M → $112.50 vs $9.10; 100M → $1,125 vs $91. Who should care: startups and high-volume apps (10M–100M tokens/month) will see large savings with Mistral; enterprise teams prioritizing accuracy, tool integration, or agentic workflows may justify GPT-5’s higher per-token spend.

Choose GPT-5 if you need: advanced tool-calling/agentic workflows, top-tier reasoning and faithfulness, strict structured outputs, or best-in-class math (e.g., MATH Level 5 98.1%). Choose Mistral Small 3.1 24B if you need: a low-cost model for high-volume chat or content generation where tool-calling/agentic planning isn’t required, or equal long-context handling at a much lower price (128k window vs GPT-5’s 400k). If you must balance both, test Mistral for throughput-first workloads and reserve GPT-5 for mission-critical flows that break on occasional hallucinations or mis-ordered tool calls.