GPT-5.1 vs Mistral Small 4

Summary of our 12-test comparison (scores from our testing): GPT-5.1 wins 5 tests, Mistral Small 4 wins 1, and 6 tests tie. Detailed walk-through: - Faithfulness: GPT-5.1 5 vs Mistral 4 — GPT-5.1 is tied for 1st ("tied for 1st with 32 other models out of 55 tested"); Mistral ranks 34 of 55. This matters for tasks that must avoid hallucination. - Long_context: GPT-5.1 5 vs Mistral 4 — GPT-5.1 is tied for 1st ("tied for 1st with 36 others"); Mistral ranks 38 of 55. Use GPT-5.1 for retrieval, summarization, and 30K+ token workflows. - Classification: GPT-5.1 4 vs Mistral 2 — GPT-5.1 is tied for 1st ("tied for 1st with 29 other models out of 53"); Mistral ranks 51 of 53. GPT-5.1 is measurably stronger at routing/labeling. - Strategic_analysis: GPT-5.1 5 vs Mistral 4 — GPT-5.1 tied for 1st on nuanced tradeoff reasoning; Mistral ranks lower. - Constrained_rewriting: GPT-5.1 4 vs Mistral 3 — GPT-5.1 ranks 6 of 53 vs Mistral 31, so GPT-5.1 handles tight character limits better. - Structured_output: GPT-5.1 4 vs Mistral 5 — Mistral wins and is tied for 1st ("tied for 1st with 24 other models out of 54"); choose Mistral when JSON/schema compliance matters. - Ties (no clear winner in our tests): Creative_problem_solving (4/4; both rank 9 of 54), Tool_calling (4/4; both rank 18 of 54), Safety_calibration (2/2; both rank 12 of 55), Persona_consistency (5/5; both tied for 1st), Agentic_planning (4/4; both rank 16 of 54), Multilingual (5/5; both tied for 1st). External benchmarks: GPT-5.1 scores 68% on SWE-bench Verified and 88.6% on AIME 2025 (Epoch AI) — these are Epoch AI results and supplement our internal scores; Mistral Small 4 has no external scores in the payload. Practical meaning: GPT-5.1 is the stronger choice for long-context retrieval, faithful outputs, classification, and constrained rewriting; Mistral is the economical choice and the leader on structured outputs.

Per-million-token pricing (from the payload): GPT-5.1 charges $1.25 input / $10.00 output per M tokens; Mistral Small 4 charges $0.15 input / $0.60 output per M tokens. Output-only costs: 1M tokens = $10.00 (GPT-5.1) vs $0.60 (Mistral); 10M = $100 vs $6; 100M = $1,000 vs $60. If you count 1M input + 1M output (balanced workloads), GPT-5.1 = $11.25 per combined M-token pair vs Mistral = $0.75. The payload’s priceRatio is 16.67×, meaning GPT-5.1 is ~16.7 times more expensive on token billing. Who should care: high-volume API products, startups on tight margins, and edge devices should favor Mistral to save tens or hundreds of dollars per million tokens; enterprises prioritizing accuracy on long-context, classification, and faithfulness may accept GPT-5.1’s premium.

Choose GPT-5.1 if you need top-tier faithfulness, long-context handling, accurate classification, or better strategic reasoning and can absorb a ~16.7× token price premium. Use it for enterprise retrieval systems, high-stakes summarization, large-context code review, and accuracy-critical automation. Choose Mistral Small 4 if you need to minimize costs and require strict JSON/schema compliance or large-scale chat/formatting at low price (Mistral wins structured output and costs $0.60/M output vs GPT-5.1 at $10/M). Use it for high-volume product features, prototyping, and constrained-format outputs where budget beats the last 10–20% of accuracy.