GPT-5.2 vs Mistral Small 3.1 24B

Overview — in our 12-test suite GPT-5.2 wins 10 benchmarks, Mistral wins 0, and the two tie on 2. Details by test (scoreA vs scoreB, then ranking context and task meaning): - Strategic analysis: GPT-5.2 5 vs Mistral 3. GPT-5.2 is tied for 1st (tied with 25 others out of 54). This indicates superior nuanced tradeoff reasoning for financial models, pricing decisions, or product strategy. - Constrained rewriting: GPT-5.2 4 vs Mistral 3. GPT-5.2 ranks 6th of 53 (many ties) — better at tight-character compression for SMS or UI copy. - Creative problem solving: GPT-5.2 5 vs Mistral 2. GPT-5.2 tied for 1st; expect more non-obvious, feasible ideas and proposals. - Tool calling: GPT-5.2 4 vs Mistral 1. GPT-5.2 ranks 18/54; Mistral ranks 53/54 and also has a documented quirk (no_tool calling=true). For building agentic workflows or selecting and sequencing functions, GPT-5.2 is the clear winner. - Faithfulness: GPT-5.2 5 vs Mistral 4. GPT-5.2 tied for 1st (out of 55); better at sticking to source material and avoiding hallucination. - Classification: GPT-5.2 4 vs Mistral 3. GPT-5.2 tied for 1st in our test set (29 others share the score); better for routing and tagging. - Safety calibration: GPT-5.2 5 vs Mistral 1. GPT-5.2 tied for 1st of 55 (only 4 others share that top score); expect much stronger refusal behavior on harmful prompts. - Persona consistency: GPT-5.2 5 vs Mistral 2. GPT-5.2 tied for 1st of 53; better at maintaining character and resisting injection. - Agentic planning: GPT-5.2 5 vs Mistral 3. GPT-5.2 tied for 1st of 54; stronger goal decomposition and recovery. - Multilingual: GPT-5.2 5 vs Mistral 4. GPT-5.2 tied for 1st of 55; higher-quality non-English output in our tests. - Structured output: tie 4 vs 4. Both rank 26/54 (27 models share this score) — both handle JSON/schema adherence similarly. - Long context: tie 5 vs 5. Both tied for 1st (tied with 36 others of 55) — both perform well on >30K-token retrieval in our tests. External benchmarks (supplementary): on SWE-bench Verified (Epoch AI) GPT-5.2 scores 73.8% and ranks 5 of 12 in our records; on AIME 2025 (Epoch AI) GPT-5.2 scores 96.1% and ranks 1 of 23. Mistral has no SWE-bench/AIME entries in the payload. Practical takeaway: GPT-5.2 delivers measurable wins where correctness, safety, tool interaction, and complex reasoning matter; Mistral matches long-context behavior and structured output while being far cheaper but lacks tool calling and lags on safety and creative/strategic tasks.

Pricing (per 1,000 tokens): GPT-5.2 — input $1.75, output $14.00; Mistral Small 3.1 24B — input $0.35, output $0.56. Price ratio for output is 25x (payload priceRatio=25). Example costs assuming a 50/50 input/output split: - 1M tokens/month: GPT-5.2 ≈ $7,875 (500k input → $875; 500k output → $7,000); Mistral ≈ $455 (500k input → $175; 500k output → $280). - 10M tokens/month: GPT-5.2 ≈ $78,750; Mistral ≈ $4,550. - 100M tokens/month: GPT-5.2 ≈ $787,500; Mistral ≈ $45,500. If your usage is output-heavy, the gap widens because GPT-5.2's $14/mTok output is the dominant cost. High-volume consumer chat, large-scale analytics pipelines, or any application with 10M+ tokens/month should favor Mistral for cost; teams that need top accuracy, safe refusals, tool integration, or state-of-the-art math should budget for GPT-5.2.

Choose GPT-5.2 if you need: - Tool-enabled agents or function orchestration (tool calling 4 vs 1; Mistral has no_tool calling), - High safety and refusal accuracy (safety calibration 5/5, tied for 1st), - Top-tier math/analysis (AIME 96.1%, ranks 1/23), - Best-in-class persona, faithfulness, and strategic reasoning for customer-facing or high-risk apps. Choose Mistral Small 3.1 24B if you need: - Dramatically lower cost at scale (example: ~$455 vs ~$7,875 per 1M tokens at 50/50 split), - Strong long-context retrieval and structured-output parity (long context 5/5 tie; structured output 4/4 tie), - A multimodal text+image->text model for high-throughput workloads where tool calling and top-tier safety are not required.