GPT-5 Mini vs Mistral Small 3.1 24B

Overview: In our 12-test suite GPT-5 Mini wins 11 benchmarks, Mistral wins 0, and they tie on long context. Below we compare each test (scoreA = GPT-5 Mini, scoreB = Mistral Small 3.1 24B) and explain task impact.

• structured output: 5 vs 4 — GPT-5 Mini (5) is tied for 1st of 54 models (“tied for 1st with 24 other models”); this means stronger JSON/schema compliance and format adherence in real integrations. Mistral’s 4 (rank 26/54) is competent but less reliable for strict schema enforcement.

• strategic analysis: 5 vs 3 — GPT-5 Mini tied for 1st of 54 (“tied for 1st with 25 other models”); it handles nuanced tradeoffs (numbers, recommendations) significantly better in our testing. Mistral’s 3 (rank 36/54) is middling for high-stakes decision reasoning.

• constrained rewriting: 4 vs 3 — GPT-5 Mini (4, rank 6/53) compresses/rewrites within hard limits more effectively; Mistral’s 3 is weaker for tight-character tasks (e.g., ad copy under strict limits).

• creative problem solving: 4 vs 2 — GPT-5 Mini is clearly better at producing non-obvious, feasible ideas; Mistral’s 2 (rank 47/54) scored poorly in our creative-gen tests.

• tool calling: 3 vs 1 — GPT-5 Mini scored 3 (rank 47/54) while Mistral scored 1 (rank 53/54). Payload also flags Mistral with "no_tool calling": true, so it cannot reliably select or sequence function calls — a practical blocker for agentic workflows.

• faithfulness: 5 vs 4 — GPT-5 Mini tied for 1st of 55 (“tied for 1st with 32 other models”); it sticks to source material. Mistral’s 4 (rank 34/55) is decent but more prone to loose paraphrase or omission in our tests.

• classification: 4 vs 3 — GPT-5 Mini (4, tied for 1st with 29 others) routes and labels more accurately; Mistral’s 3 (rank 31/53) is lower.

• safety calibration: 3 vs 1 — GPT-5 Mini scored 3 (rank 10/55) and better refuses harmful requests while permitting legitimate ones in our testing; Mistral’s 1 (rank 32/55) scored poorly on safety calibration.

• persona consistency: 5 vs 2 — GPT-5 Mini tied for 1st (strong at maintaining character and resisting injection); Mistral’s 2 (rank 51/53) is a weak point for persona-driven agents.

• agentic planning: 4 vs 3 — GPT-5 Mini (rank 16/54) decomposes goals and recovers from failures better. Mistral’s 3 is usable but less capable for multi-step planning.

• multilingual: 5 vs 4 — GPT-5 Mini tied for 1st (high multilingual parity); Mistral is solid (4) but behind in our non-English tests.

• long context: 5 vs 5 — both scored 5 and are tied for 1st of 55 (“tied for 1st with 36 other models”); both handle retrieval at 30K+ tokens comparably in our tests.

External benchmarks (supplementary): GPT-5 Mini also posts external scores we include from Epoch AI: SWE-bench Verified 64.7% (rank 8 of 12), MATH Level 5 97.8% (rank 2 of 14, shared), and AIME 2025 86.7% (rank 9 of 23). Mistral Small 3.1 24B has no external benchmark scores in the payload. These external results reinforce GPT-5 Mini’s strength on coding/math tasks where available (attributed to Epoch AI).

Pricing (from the payload): GPT-5 Mini charges $0.25 per M input tokens and $2.00 per M output tokens; Mistral Small 3.1 24B charges $0.35 per M input and $0.56 per M output. Assuming a 50/50 split between input and output tokens, per-million-token costs are: GPT-5 Mini = 0.5*$0.25 + 0.5*$2.00 = $1.125 per 1M tokens; Mistral = 0.5*$0.35 + 0.5*$0.56 = $0.455 per 1M tokens. At scale that yields: 1M tokens → $1.13 (GPT-5 Mini) vs $0.46 (Mistral); 10M → $11.25 vs $4.55; 100M → $112.50 vs $45.50. Output-cost ratio is ~3.57x ($2.00 vs $0.56), matching the payload priceRatio (3.5714). Who should care: high-volume applications (customer chat, large-scale generation, API businesses) will see large monthly savings with Mistral; teams that need schema compliance, faithfulness, tool calling, or advanced reasoning should budget for GPT-5 Mini’s higher cost because those are its strengths in our benchmarks.

Choose GPT-5 Mini if: you need strict structured outputs (JSON/schema), high faithfulness, persona consistency, strategic analysis, robust multilingual output, or tool-calling/agentic planning — trade higher cost ($0.25 input / $2 output per M tokens) for reliability. Choose Mistral Small 3.1 24B if: unit cost matters (output $0.56 per M), you operate at high token volumes, and your workload is long-context retrieval, basic multilingual or general chat without tool calling or strict schema enforcement.