OpenAI Releases GPT-5.5 with Agentic Capabilities for Coding, Research, and Autonomous Tasks
OpenAI released GPT-5.5 on April 24, 2026, describing it as its smartest and most intuitive model yet. The model is designed for complex, multi-step tasks including writing and debugging code, online research, data analysis, and operating software interfaces autonomously. It is available immediately to ChatGPT Plus, Pro, Business, and Enterprise users.

OpenAI released GPT-5.5 on April 24, 2026, its most capable model to date, built for complex, multi-step tasks that require planning, iteration, and persistence through ambiguity.
The model handles writing and debugging code, online research, data analysis, document creation, and operating software interfaces without constant human direction. It matches previous latency while using fewer tokens, making it more efficient for extended tasks.
GPT-5.5 is available immediately to ChatGPT Plus, Pro, Business, and Enterprise users. A GPT-5.5 Pro version for higher-stakes work and API access are coming soon.
Benchmarks show the model leading in Terminal-Bench 2.0 at 82.7%, Expert-SWE, FrontierMath, and CyberGym, outperforming Claude Opus 4.7 and Gemini 3.1 Pro across most categories. It trails slightly in some zero-shot reasoning tasks.
Safety features include OpenAI's strongest safeguards to date, with red-teaming for cybersecurity and biology risks, stricter classifiers, and a "Trusted Access for Cyber" program for verified security researchers.
Early testers praised the model's conceptual clarity in system architecture and debugging. OpenAI says the model is designed to function as a reliable co-worker for software engineering, scientific research, and knowledge work.
The release intensifies competition among frontier AI labs. DeepSeek also unveiled a preview of its V4 model on the same day, offering a 1.6-trillion-parameter Pro version and a lighter Flash variant with a 1-million-token context window. DeepSeek's model leads open-source benchmarks and trails only Google's Gemini-Pro-3.1 among closed-source leaders, while emphasizing drastically reduced training and inference costs.


