GPT-5.6 is here, and it's changing the game for coding and AI agents with a 64.6% success rate in software engineering tests
OpenAI released GPT-5.6 on July 9, 2026, introducing three models built for coding, research, professional work, computer use, science, and AI agents. This new release matters because it brings significant improvements in coding and agent performance, making it a valuable tool for professionals. GPT-5.6 is designed to complete complex work with fewer tokens, fewer tool calls, and less time.
Readers will learn how GPT-5.6 can improve their workflow, increase efficiency, and enhance their overall work experience with its advanced features and capabilities.
What is GPT-5.6 and How Does it Work?
GPT-5.6 is the latest release from OpenAI, and it includes three models: GPT-5.6 Sol, GPT-5.6 Terra, and GPT-5.6 Luna. Each model is designed for specific tasks, with Sol being the flagship model for complex reasoning, coding, research, cybersecurity, and agent workflows.
The new release brings significant improvements in coding and agent performance, with GPT-5.6 Sol achieving a 64.6% success rate in software engineering tests. This is a 5.2% increase from the previous model, making it a valuable tool for professionals.
- GPT-5.6 Sol: The flagship model for complex reasoning, coding, research, cybersecurity, and agent workflows, with a pricing starting at $5 per 1M tokens input and $30 per 1M tokens output.
- GPT-5.6 Terra: A lower-cost model that balances capability, speed, and price, with a pricing starting at $2.50 per 1M tokens input and $15 per 1M tokens output.
- GPT-5.6 Luna: The fastest and cheapest model for high-volume or structured tasks, with a pricing starting at $1 per 1M tokens input and $6 per 1M tokens output.
Stronger Coding and Agent Performance
OpenAI reports that GPT-5.6 Sol improves across software engineering and terminal-based coding tests, with a 72.7% success rate in DeepSWE 1.1 tests and an 88.8% success rate in Terminal-Bench 2.1 tests.
These benchmarks suggest improvements in tasks that require planning, command-line work, file editing, testing, and tool coordination. That said, GPT-5.6 does not win every coding benchmark, and OpenAI's own results show Claude Mythos 5 scoring higher on SWE-Bench Pro.
- SWE-Bench Pro: GPT-5.6 Sol achieves a 64.6% success rate, a 5.2% increase from the previous model.
- DeepSWE 1.1: GPT-5.6 Sol achieves a 72.7% success rate, a 5.7% increase from the previous model.
- Terminal-Bench 2.1: GPT-5.6 Sol achieves an 88.8% success rate, a 3.2% increase from the previous model.
Programmatic Tool Calling
One of the most useful additions to GPT-5.6 is Programmatic Tool Calling, which allows the model to write and execute small in-memory programs that coordinate multiple tools, filter intermediate results, track workflow progress, and decide which information should return to the model.
This feature is available through the Responses API and is compatible with Zero Data Retention configurations, making it a valuable tool for tasks such as searching large document collections, reviewing code repositories, processing database or API results, running tests and analyzing failures, and combining information from multiple tools.
- Searching large document collections: GPT-5.6 can search and analyze large document collections with ease, making it a valuable tool for research and development.
- Reviewing code repositories: GPT-5.6 can review and analyze code repositories, making it a valuable tool for software development and testing.
- Processing database or API results: GPT-5.6 can process and analyze database or API results, making it a valuable tool for data analysis and visualization.
Multi-Agent Reasoning
GPT-5.6 also introduces stronger reasoning levels, with the new max mode giving the model more compute for difficult tasks, and the ultra mode using multiple subagents in parallel and combining their work into one response.
OpenAI is also offering multi-agent execution through the Responses API in beta, making it a valuable tool for tasks such as deep research, large codebase analysis, security reviews, financial research, technical planning, and multi-source reports.
- Deep research
