GPT-5.2 vs Mistral Large 3 2512

Head-to-head across our 12-test suite: GPT-5.2 wins 8 categories, Mistral Large 3 2512 wins 1, and 3 are ties. GPT-5.2 wins strategic analysis (5 vs 4) and is ranked tied for 1st of 54 models on that test in our rankings, which matters for nuanced numeric tradeoffs and planning. GPT-5.2 also wins creative problem solving (5 vs 3; tied for 1st of 54), constrained rewriting (4 vs 3; rank 6/53), classification (4 vs 3; tied for 1st of 53), long context (5 vs 4; tied for 1st of 55) — indicating superior retrieval and coherence over 30K+ tokens — persona consistency (5 vs 3; tied for 1st of 53), agentic planning (5 vs 4; tied for 1st of 54) and safety calibration (5 vs 1; tied for 1st of 55), which means GPT-5.2 better refuses harmful requests while allowing legitimate ones. Mistral Large 3 2512 wins structured output (5 vs 4; Mistral tied for 1st of 54), which signals stronger JSON/schema compliance and format adherence for pipelines that require exact output shape. Tool_calling, faithfulness, and multilingual are ties (both score 4–5), so either model can be used when those are the only constraints. Supplementary external benchmarks: GPT-5.2 scores 73.8% on SWE-bench Verified (Epoch AI) and 96.1% on AIME 2025 (Epoch AI) — useful signals for coding and high-difficulty math tasks; Mistral has no external SWE-bench or AIME scores provided in the payload. In short: GPT-5.2 is measurably stronger for complex reasoning, long context, safety, and coding/math as shown by our internal scores and the cited Epoch AI benchmarks; Mistral is the clear leader for reliable structured outputs at a fraction of the cost.

Costs are materially different: GPT-5.2 charges $1.75 per mTok input and $14.00 per mTok output; Mistral Large 3 2512 charges $0.50 per mTok input and $1.50 per mTok output. Per 1M tokens (1000 mTok): GPT-5.2 = $1,750 input / $14,000 output; Mistral = $500 input / $1,500 output. If you run 1M input+1M output tokens/month, monthly spend approximates $15,750 on GPT-5.2 vs $2,000 on Mistral. At 10M in/out tokens: GPT-5.2 ≈ $157,500 vs Mistral ≈ $20,000. At 100M in/out: GPT-5.2 ≈ $1,575,000 vs Mistral ≈ $200,000. Teams with heavy production inference (10M+ tokens/month) or constrained budgets should favor Mistral for cost-effectiveness; teams requiring top-tier reasoning, safety calibration, and extremely long contexts may justify GPT-5.2’s ~9.33x price ratio (priceRatio = 9.3333) for higher task accuracy and reliability.

Choose GPT-5.2 if you need best-in-class strategic reasoning, long-context retrieval (30K+ tokens), strict safety calibration, persona consistency, or top-tier performance on math/coding benchmarks — and your budget can absorb the much higher per-token cost. Choose Mistral Large 3 2512 if you must keep inference costs low, need near-perfect structured/JSON outputs, or are scaling high-volume production where price per token dominates.