GPT-5.2 vs Mistral Small 4

Overview (our 12-test suite): GPT-5.2 wins 8 tests, Mistral Small 4 wins 1, and they tie on 3. Details: - Strategic analysis: GPT-5.2 5 vs Mistral 4 — GPT-5.2 tied for 1st of 54 models for nuanced tradeoff reasoning, so expect better numeric tradeoffs in decision tasks. - Structured output (JSON/schema): Mistral 5 vs GPT-5.2 4 — Mistral Small 4 is tied for 1st of 54 on schema compliance, so prefer it when strict JSON or format adherence is critical. - Persona consistency: tie 5/5 — both maintain persona well (GPT-5.2 tied for 1st in our tests). - Agentic planning: GPT-5.2 5 vs Mistral 4 — GPT-5.2 tied for 1st of 54, giving stronger goal decomposition and recovery. - Constrained rewriting: GPT-5.2 4 vs Mistral 3 — GPT-5.2 ranks 6 of 53, better for compression and exact-length edits. - Faithfulness: GPT-5.2 5 vs Mistral 4 — GPT-5.2 tied for 1st of 55, more reliable at sticking to source material. - Long context: GPT-5.2 5 vs Mistral 4 — GPT-5.2 tied for 1st of 55 on retrieval at 30K+ tokens, so it handles huge contexts better. - Classification: GPT-5.2 4 vs Mistral 2 — GPT-5.2 ranks tied for 1st of 53, while Mistral ranks 51 of 53, making GPT-5.2 much more reliable for routing/labeling. - Creative problem solving: GPT-5.2 5 vs Mistral 4 — GPT-5.2 tied for 1st, better for non-obvious idea generation. - Tool calling: tie 4/4 — both score similarly for function selection and sequencing (rank 18 of 54). - Safety calibration: GPT-5.2 5 vs Mistral 2 — GPT-5.2 tied for 1st of 55 in refusing harmful requests while permitting legitimate ones; Mistral sits mid-pack (rank 12 of 55). - Multilingual: tie 5/5 — both perform strongly across languages. External benchmarks (supplementary): GPT-5.2 scores 73.8% on SWE-bench Verified and 96.1% on AIME 2025 (Epoch AI), placing it 5th/12 on SWE-bench and 1st/23 on AIME in those external datasets. Mistral Small 4 has no external scores in the payload. In practice this means GPT-5.2 is a clear winner for long-context retrieval, reasoning-heavy tasks, safety-critical flows, classification, and math/competition-style problems, while Mistral Small 4 is preferable when strict structured output and low-cost inference matter.

Costs are radically different. Per mTok (thousand tokens): GPT-5.2 input $1.75, output $14; Mistral Small 4 input $0.15, output $0.60. Per 1M tokens (1,000 mTok): GPT-5.2 costs $1,750 (input) and $14,000 (output); Mistral costs $150 (input) and $600 (output). If your calls are 1:1 input:output and you consume 1M input + 1M output tokens/month, total monthly cost is $15,750 for GPT-5.2 vs $750 for Mistral. Multiply by volume: 10M (x10) → $157,500 vs $7,500; 100M (x100) → $1,575,000 vs $75,000. The ~23.33x priceRatio means high-volume apps (SaaS, consumer-facing chatbots, large-scale indexing) must weigh cost sharply; small teams, prototypes, and cost-sensitive deployments will favor Mistral Small 4 to reduce run costs.

Choose GPT-5.2 if you need top-tier reasoning, long-context handling (30K+ tokens), strong safety calibration, accurate classification, or best-in-class math performance (GPT-5.2 scores 96.1% on AIME 2025 in external Epoch AI data). Pay the premium when correctness and capabilities directly impact product value. Choose Mistral Small 4 if your priority is cost-efficiency and strict structured output (Mistral ranks tied for 1st on structured output) — ideal for high-volume APIs, inexpensive assistants that must adhere to JSON schemas, or multilingual apps where per-token cost dominates.