GPT-5 Nano vs Mistral Large 3 2512

Summary of wins in our 12-test suite: GPT-5 Nano wins long context, safety calibration, and persona consistency; Mistral Large 3 2512 wins faithfulness; the remaining tests are ties. Detailed walk-through (score / ranking context):

• Long context: GPT-5 Nano 5/5 (tied for 1st, "tied for 1st with 36 other models out of 55 tested"). This means GPT-5 Nano is top-tier for retrieval and accuracy over 30K+ tokens in our tests; Mistral scores 4/5 (rank 38 of 55), so it is good but less reliable at extreme context lengths.
• Safety calibration: GPT-5 Nano 4/5 (rank 6 of 55) vs Mistral 1/5 (rank 32 of 55). In our testing GPT-5 Nano better refuses harmful requests and permits legitimate ones; Mistral underperforms on this dimension.
• Persona consistency: GPT-5 Nano 4/5 (rank 38 of 53) vs Mistral 3/5 (rank 45 of 53). GPT-5 Nano holds character and resists injection better in chat-style scenarios.
• Faithfulness: Mistral 5/5 (tied for 1st with 32 others out of 55) vs GPT-5 Nano 4/5 (rank 34 of 55). For tasks requiring strict adherence to source material, Mistral is the winner in our tests.
• Structured output: both 5/5 and tied for 1st (compliance with JSON/schema is equivalent in our tests).
• Tool calling & agentic planning: both score 4/5 and rank 18 and 16 respectively — both models are comparable at selecting functions, arguments, and planning.
• Strategic analysis, constrained rewriting, creative problem solving, classification, multilingual: ties (both models scored equally in our testing).
• External math benchmarks (supplementary): GPT-5 Nano scores 95.2% on MATH Level 5 and 81.1% on AIME 2025 (Epoch AI); those external scores indicate strong math/problem-solving performance for GPT-5 Nano in third-party tests and are shown alongside our internal results. Overall: GPT-5 Nano wins more categories important to production chat and long-context applications; Mistral’s clearest advantage is faithfulness.

Costs in the payload: GPT-5 Nano charges $0.05 per mTok input and $0.40 per mTok output; Mistral Large 3 2512 charges $0.50 per mTok input and $1.50 per mTok output. Using a simple 50/50 input/output split (1M tokens = 1,000 mTok), per‑month costs scale linearly: for 1M tokens GPT-5 Nano ≈ $225 (500 mTok input × $0.05 = $25; 500 mTok output × $0.40 = $200) vs Mistral ≈ $1,000 (500 mTok × $0.50 = $250; 500 mTok × $1.50 = $750). At 10M tokens: GPT-5 Nano ≈ $2,250 vs Mistral ≈ $10,000. At 100M tokens: GPT-5 Nano ≈ $22,500 vs Mistral ≈ $100,000. GPT-5 Nano therefore costs ~26.7% of Mistral per token (about 73.3% cheaper), so teams with heavy inference volume or tight margins should prefer GPT-5 Nano; teams prioritizing top-tier faithfulness and willing to pay a 3–4x premium per token might consider Mistral Large 3 2512.

Choose GPT-5 Nano if you need: high-throughput, low-latency production inference or large-context retrieval with strong safety and persona behavior, and you care about cost (GPT-5 Nano charges $0.05 input / $0.40 output per mTok). Choose Mistral Large 3 2512 if you must prioritize maximal faithfulness to source material and are willing to pay higher rates ($0.50 input / $1.50 output per mTok) for that advantage.