GPT-5.2

Product Introduction

1. Definition: GPT-5.2 is OpenAI’s most advanced frontier large language model (LLM) optimized for professional knowledge work and long-running agentic tasks. It belongs to the generative AI category, leveraging transformer architecture with enhanced multimodal capabilities (text, code, vision).
2. Core Value Proposition: GPT-5.2 unlocks unprecedented economic value by automating complex workflows—like spreadsheet modeling, code refactoring, and multi-step tool orchestration—at expert-level accuracy. It targets enterprises and professionals seeking productivity gains through AI-driven task automation.

---

Main Features

1. Expert-Level Task Automation

   • How it works: Uses advanced reasoning (xhigh effort mode) and tool integration to execute GDPval tasks (spanning 44 occupations) at 70.9% win/tie rate against human experts. Generates polished outputs (e.g., cap tables, financial models) 11x faster than professionals.
   • Technologies: Fine-tuned on domain-specific datasets; integrates with tools like Python, Notion, and Zoom APIs for real-time collaboration.

2. State-of-the-Art Coding

   • How it works: Solves 55.6% of SWE-Bench Pro tasks (multi-language software engineering) and 80% of SWE-bench Verified (Python). Handles agentic workflows like bug fixes, UI generation (e.g., 3D interfaces), and repo-wide refactoring.
   • Technologies: Trained on diverse codebases; supports interactive coding via Warp, JetBrains, and Augment Code integrations.

3. Long-Context Reasoning (256K Tokens)

   • How it works: Achieves near-100% accuracy on 4-needle MRCRv2 tasks (multi-document co-reference resolution) at 256K tokens. Maintains coherence across contracts, research papers, and multi-file projects.
   • Technologies: Sparse attention mechanisms; compact API endpoint for context window extension.

4. Enhanced Vision & Tool Calling

   • How it works: Halves error rates on CharXiv Reasoning (scientific charts) and ScreenSpot-Pro (GUI understanding). Identifies spatial relationships (e.g., motherboard components) and achieves 98.7% on Tau2-bench Telecom for multi-turn tool orchestration.
   • Technologies: Vision Transformer (ViT) integration; tool-calling API with latency optimizations for reasoning.effort='none' mode.

---

Problems Solved

1. Pain Point: High-cost, slow manual execution of specialized tasks (e.g., LBO modeling, code migration).
2. Target Audience:
   • Investment Bankers: Automates spreadsheet modeling (68.4% accuracy vs. 59.1% in GPT-5.1).
   • Software Engineers: Reduces debugging time with SOTA SWE-Bench Pro performance.
   • Data Scientists: Analyzes 256K-token documents (92% accuracy on BrowseComp).
   • Researchers: Solves 40.3% of FrontierMath Tier 1-3 problems and 93.2% of GPQA Diamond science questions.
3. Use Cases:
   • Generating investor-ready presentations from raw data.
   • Resolving customer support chains requiring multi-tool coordination (e.g., rebooking + compensation).
   • Accelerating scientific discovery via proof assistance (e.g., statistical learning theory).

---

Unique Advantages

1. Differentiation:
   • Outperforms GPT-5.1 by 30%+ on GDPval and coding benchmarks.
   • Priced at $1.75/1M input tokens—lower cost-per-quality than competitors despite higher token price.
   • Integrates with enterprise tools (Shopify, Databricks) for seamless workflow embedding.
2. Key Innovations:
   • xhigh Reasoning Effort: Maximizes output quality for mission-critical tasks.
   • Cached Input Discounts: 90% cost reduction on repeated inputs.
   • Mental Health Safeguards: 30% fewer undesirable responses in sensitive conversations vs. prior models.

---

Frequently Asked Questions (FAQ)

1. How does GPT-5.2 improve over GPT-5.1?
   GPT-5.2 increases GDPval win rate by 32.1%, coding accuracy by 4.8% (SWE-Bench Pro), and halves vision errors. It adds xhigh reasoning, cached inputs, and enhanced safety protocols.

2. What is the cost of GPT-5.2 API access?
   Input tokens cost $1.75/1M; output tokens cost $14/1M. Cached inputs are $0.175/1M. GPT-5.2 Pro ranges from $21–$168/1M tokens based on configuration.

3. Can GPT-5.2 process images and documents?
   Yes, it achieves 86.3% on ScreenSpot-Pro (GUI analysis) and 88.7% on CharXiv Reasoning (scientific figures), supporting PDFs, spreadsheets, and screenshots.

4. Is GPT-5.2 suitable for enterprise deployment?
   Absolutely. It offers SOC 2 compliance, data residency controls, and 99%+ tool-calling reliability (Tau2-bench), making it ideal for finance, healthcare, and legal sectors.

5. How does GPT-5.2 handle 256K-token contexts?
   Via MRCRv2-optimized attention and the /compact API endpoint, it maintains >77% accuracy at 256K tokens for tasks like cross-document synthesis.