Mistral Large 3 2512 vs o3

Across our 12-test suite: o3 wins 6 tests (strategic analysis 5 vs 4, agentic planning 5 vs 4, tool calling 5 vs 4, creative problem solving 4 vs 3, constrained rewriting 4 vs 3, persona consistency 5 vs 3). Mistral has no outright wins but ties on six tests: structured output (5/5), faithfulness (5/5), classification (3/3), long context (4/4), safety calibration (1/1), and multilingual (5/5). Rankings add context: o3 is tied for 1st on strategic analysis, agentic planning, tool calling, persona consistency, multilingual and structured output (see rankingsB), meaning in our tests o3 sits at the top tier for nuanced tradeoff reasoning, function selection/argument accuracy, goal decomposition, and maintaining persona. Mistral’s strengths in our data are its top score in structured output (tied for 1st) and top-tier faithfulness and multilingual performance (tied for 1st in those categories), so it will reliably follow JSON schemas and stick to source material. On external benchmarks (supplementary, Epoch AI): o3 scores 62.3% on SWE-bench Verified, 97.8% on MATH Level 5, and 83.9% on AIME 2025 — evidence that o3 is strong on coding/math benchmarks. Practically: choose o3 where tool calling, complex planning, and highest creative/problem-solving fidelity matter; choose Mistral where equal structured-output fidelity and a much lower cost per token are decisive.

Raw per-1K-token rates: Mistral Large 3 2512 charges $0.50 input / $1.50 output; o3 charges $2.00 input / $8.00 output. Assuming a 50/50 split of input vs output tokens, Mistral costs ~$1,000 per 1M total tokens (500k input + 500k output) versus o3 at ~$5,000 — a 5x gap. At 10M total tokens/month that becomes ~$10,000 vs ~$50,000; at 100M it’s ~$100,000 vs ~$500,000. The priceRatio in the payload is 0.1875 (Mistral ≈18.75% of o3 for the same token mix). High-volume apps, startups on tight budgets, and inference-heavy pipelines should care most about this gap; teams prioritizing raw task accuracy on planning, tool use, and math may accept o3's higher cost.

Choose Mistral Large 3 2512 if: you need the lowest-cost high-capacity model (context_window 262,144) for high-throughput services, require top-tier structured output and faithfulness at scale, or must minimize monthly inference spend. Choose o3 if: your priority is best-in-class tool calling, strategic analysis, agentic planning, creative problem solving, persona consistency, or top external math/coding scores (o3: SWE-bench 62.3%, MATH Level 5 97.8% per Epoch AI) and you can absorb higher token costs ($2/$8 per 1K tokens).