GPT-5 Mini vs Mistral Medium 3.1

Summary of head-to-heads from our 12-test suite (scores shown are from our testing):

• GPT-5 Mini wins (4): structured output 5 vs 4, creative problem solving 4 vs 3, faithfulness 5 vs 4, safety calibration 3 vs 2. Structured output (JSON/schema compliance) is a clear GPT-5 Mini strength — it ties for 1st of 54 models (tied with 24 others), while Mistral is mid-pack (rank 26 of 54). Higher faithfulness (5 vs 4) means GPT-5 Mini more reliably sticks to source material in our tests.
• Mistral Medium 3.1 wins (3): constrained rewriting 5 vs 4, tool calling 4 vs 3, agentic planning 5 vs 4. Tool calling and agentic planning are practical wins: Mistral ranks 18/54 on tool calling (tied) vs GPT-5 Mini at rank 47/54 — expect better function-selection and sequencing from Mistral in our tests. Constrained rewriting (compression into strict limits) is also Mistral’s top area (tied for 1st).
• Ties (5): strategic analysis (5/5), classification (4/4), long context (5/5), persona consistency (5/5), multilingual (5/5). Both models tie at top ranks for long-context, persona consistency, classification, multilingual support, and strategic analysis — so for large-context retrieval or multilingual apps both are comparable in our benchmarks. External benchmarks (Epoch AI): GPT-5 Mini scores 64.7% on SWE-bench Verified, 97.8% on MATH Level 5, and 86.7% on AIME 2025 — useful supplemental evidence for coding and math performance. Mistral Medium 3.1 had no SWE-bench/MATH/AIME scores in the payload. Practical meaning: choose GPT-5 Mini when schema compliance, math fidelity, or source-faithful outputs matter; choose Mistral when you need stronger tool orchestration, tight-length rewrites, or agentic workflows.

Costs from the payload: GPT-5 Mini input $0.25/mTok, output $2/mTok; Mistral Medium 3.1 input $0.40/mTok, output $2/mTok. Absolute examples (1 mTok = 1,000 tokens):

• 1M tokens (all output): output = $2,000 for both; input-only difference = GPT-5 Mini $250 vs Mistral $400. With a 50/50 input/output split: GPT-5 Mini = $1,125 vs Mistral = $1,200 (GPT saves $75).
• 10M tokens (50/50): GPT-5 Mini = $11,250 vs Mistral = $12,000 (saves $750).
• 100M tokens (50/50): GPT-5 Mini = $112,500 vs Mistral = $120,000 (saves $7,500). Who should care: product/ops teams and startups with high monthly token volume — the input-cost gap scales linearly and becomes material at tens of millions of tokens. Single-user or low-volume prototypes will see small absolute differences, since both models share the same output rate ($2/mTok).

Choose GPT-5 Mini if you need: reliable structured outputs (JSON/schema), high faithfulness, strong long-context and math performance (GPT-5 Mini: structured output 5, faithfulness 5, MATH Level 5 97.8% per Epoch AI), or you expect high token volumes (lower input cost $0.25 vs $0.40). Choose Mistral Medium 3.1 if you need: better tool calling and orchestration (tool calling 4 vs GPT-5 Mini 3, Mistral ranks ~18/54 vs GPT-5 Mini 47/54), stronger agentic planning and recovery (agentic planning 5 vs 4), or top-tier constrained rewriting for tight length limits.