For business and technical teams, GPT-5.2 substantially upgrades day-to-day workflows like building spreadsheets and financial models, drafting complex presentations, performing deep document analysis over hundreds of thousands of tokens, and orchestrating multi-step projects that rely on tools and APIs. GPT-5.2 Thinking, in particular, is optimized for structured, long-horizon reasoning: it can integrate information spread across large reports, contracts, research papers, or multi-file codebases, and then generate polished outputs such as models, slide decks, and decision memos with higher coherence and fewer factual errors than GPT-5.1. Early adopters in domains such as investment banking, data science, and operations report that it reliably handles tasks like three-statement financial models, leveraged buyout models, agentic data science workflows, and complex customer-support resolution flows that previously required multiple tools or human handoffs.
GPT-5.2 also delivers major gains for software engineering, vision, and tool-based agents, making it a strong foundation for coding copilots, autonomous agents, and enterprise copilots embedded in existing products. On SWE-Bench Pro and SWE-bench Verified, GPT-5.2 Thinking achieves leading scores, translating into more dependable debugging, feature implementation, refactoring of large codebases, and generation of complex front-end interfaces, including rich 3D UIs, from a single prompt. Its vision capabilities reduce error rates on chart reasoning and UI understanding, while tool-calling performance reaches 98.7% accuracy on long, multi-turn agent benchmarks like Tau2-bench Telecom, enabling single “mega-agents” with large toolsets to execute end-to-end workflows more robustly than prior multi-agent systems.
