GPT-4o-mini vs Mistral Small 3.1 24B

All claims below are from our testing. Summary: GPT-4o-mini wins 4 benchmarks, Mistral Small 3.1 24B wins 3, and 5 are ties. Details: - Tool calling: GPT-4o-mini 4 vs Mistral 1. GPT-4o-mini ranks 18 of 54 (tied with 28); Mistral ranks 53 of 54 — practical impact: GPT is far more reliable at function selection, argument accuracy and sequencing. - Classification: GPT-4o-mini 4 vs Mistral 3. GPT-4o-mini is tied for 1st of 53 models (with 29 others); Mistral ranks 31 of 53 — means GPT is more dependable for routing and categorization tasks. - Safety calibration: GPT-4o-mini 4 (rank 6/55) vs Mistral 1 (rank 32/55) — GPT refuses harmful requests and permits legitimate ones far more consistently in our tests. - Persona consistency: GPT-4o-mini 4 (rank 38/53) vs Mistral 2 (rank 51/53) — GPT better resists injection and maintains character. - Long context: GPT-4o-mini 4 (rank 38/55) vs Mistral 5 (tied for 1st of 55) — Mistral is clearly superior for retrieval and accuracy at 30K+ token contexts. - Faithfulness: GPT-4o-mini 3 (rank 52/55) vs Mistral 4 (rank 34/55) — Mistral sticks to source material more reliably in our testing. - Strategic analysis: GPT-4o-mini 2 (rank 44/54) vs Mistral 3 (rank 36/54) — Mistral is better at nuanced tradeoff reasoning. - Ties: structured output 4/4, constrained rewriting 3/3, creative problem solving 2/2, agentic planning 3/3, multilingual 4/4 — both models perform equivalently on schema compliance, constrained rewriting, creative idea generation (as measured here), basic planning, and non-English output quality. Supplementary external math signals: GPT-4o-mini scores 52.6% on MATH Level 5 and 6.9% on AIME 2025 (Epoch AI); Mistral has no MATH/AIME numbers in the payload. Practical takeaway: choose GPT-4o-mini for apps that need safe, reliable tool integration and routing; choose Mistral Small 3.1 24B for long-context retrieval and source-faithful tasks.

Pricing per 1,000 tokens: GPT-4o-mini charges $0.15 input and $0.60 output; Mistral Small 3.1 24B charges $0.35 input and $0.56 output. Per 1M input tokens: GPT-4o-mini = $150, Mistral = $350. Per 1M output tokens: GPT-4o-mini = $600, Mistral = $560. Using a realistic chat split (20% input / 80% output) for total monthly tokens: 1M total → GPT-4o-mini $510 vs Mistral $518; 10M → GPT-4o-mini $5,100 vs Mistral $5,180; 100M → GPT-4o-mini $51,000 vs Mistral $51,800. The gap is small at scale (~1.6% in this 20/80 example) but can flip if your app is input-heavy (GPT is cheaper for input tokens) or output-heavy (Mistral is slightly cheaper on output). High-volume integrators and cost-sensitive production teams should model their actual input/output split to see which saves more.

Choose GPT-4o-mini if you need robust tool calling, safer refusals, and top-tier classification (in our testing: tool calling 4 vs 1, safety calibration 4 vs 1, classification 4 vs 3). Use cases: multi-tool agents, orchestrated workflows, customer routing, and chatbots that must refuse or escalate correctly. Choose Mistral Small 3.1 24B if your priority is long-context accuracy and source faithfulness (in our testing: long context 5 vs 4, faithfulness 4 vs 3) — use cases: retrieval-augmented generation over large documents, long-form analysis, and tasks where sticking to source facts matters. If cost is the dominant factor, model the exact input/output token ratio — costs are close but flip depending on whether your workload is input-heavy or output-heavy.