GPT-4o-mini vs Mistral Medium 3.1

Overview: In our 12-test suite Mistral Medium 3.1 wins 8 tests, GPT-4o-mini wins 1, and 3 are ties (structured output, tool calling, classification). Detailed walk-through: - Multilingual: Mistral 5 vs GPT-4o-mini 4 — Mistral is tied for 1st (tied with 34 others) on multilingual, so it reliably maintains quality across languages for global apps. - Long context: Mistral 5 vs GPT-4o-mini 4 — Mistral is tied for 1st on long context, meaning better retrieval/consistency at 30K+ tokens. - Agentic planning: Mistral 5 vs GPT-4o-mini 3 — Mistral ranks tied for 1st on agentic planning, so it handles goal decomposition and recovery better in our tests. - Strategic analysis: Mistral 5 vs GPT-4o-mini 2 — a clear Mistral win for nuanced tradeoff reasoning with numbers (ranks tied for 1st vs GPT-4o-mini low rank). - Constrained rewriting: Mistral 5 vs GPT-4o-mini 3 — Mistral excels at tight compression tasks. - Faithfulness: Mistral 4 vs GPT-4o-mini 3 — Mistral is stronger at sticking to sources in our evaluations. - Persona consistency: Mistral 5 vs GPT-4o-mini 4 — Mistral maintains character and resists injection better. - Creative problem solving: Mistral 3 vs GPT-4o-mini 2 — Mistral wins but both are mid-tier here. - Safety calibration: GPT-4o-mini 4 vs Mistral 2 — GPT-4o-mini ranks 6th of 55 on safety calibration in our tests, so it refuses harmful requests more appropriately while allowing benign ones. - Structured output, tool calling, classification: both scored 4 and tied — both models handle JSON/schema formatting, function selection/args, and routing/classification comparably. Additional math signals: GPT-4o-mini scores 52.6% on MATH Level 5 and 6.9% on AIME 2025 (Epoch AI); Mistral Medium 3.1 has no MATH/AIME scores in the payload. Rankings context: GPT-4o-mini ranks highly on safety calibration (rank 6/55) and is tied for 1st in classification; Mistral is tied for 1st in multilingual, long context, agentic planning, constrained rewriting, strategic analysis, and persona consistency. In practical terms: choose Mistral for multilingual, long-context, multi-step planning and faithful outputs; choose GPT-4o-mini when safety calibration and cost are higher priorities.

Pricing per mTok: GPT-4o-mini input $0.15, output $0.60; Mistral Medium 3.1 input $0.40, output $2.00. Per 1,000,000 tokens (1000 mTok): GPT-4o-mini costs $150 (input) + $600 (output) = $750 total; Mistral Medium 3.1 costs $400 + $2,000 = $2,400 total. At 10M tokens/month multiply by 10: GPT-4o-mini ≈ $7,500 vs Mistral ≈ $24,000. At 100M tokens/month multiply by 100: GPT-4o-mini ≈ $75,000 vs Mistral ≈ $240,000. The ~3.2x total cost gap means high-volume apps (search, analytics pipelines, large-scale chatbots) should care about per-token pricing; startups and hobby projects will find GPT-4o-mini materially cheaper. Enterprises focused on multilingual, long-context, or agentic planning may accept Mistral's higher cost for performance wins.

Choose Mistral Medium 3.1 if you need top-tier multilingual support, long-context retrieval (30K+ tokens), agentic planning, constrained rewriting, or strategic numeric reasoning — it wins 8 of 12 benchmarks in our tests. Choose GPT-4o-mini if your primary constraints are cost or safety calibration: GPT-4o-mini costs $0.15 input / $0.60 output per mTok (vs Mistral $0.40 / $2.00) and wins safety calibration in our suite. If you need balanced tool calling, structured outputs, or classification at lower cost, GPT-4o-mini is the pragmatic choice; if accuracy across languages and complex planning matter more than per-token spend, pick Mistral.