The accuracy of OpenAI Codex in generating code is quite high, particularly for common programming tasks and well-defined requirements. Based on OpenAI’s testing, the original Codex model could produce working solutions for approximately 70.2% of programming prompts when given multiple attempts, and the current 2025 version has shown significant improvements in accuracy due to its advanced training with reinforcement learning from human feedback. The system performs exceptionally well on standard programming tasks like implementing common algorithms, creating web application components, handling database operations, and following established coding patterns. For straightforward tasks with clear requirements, Codex often generates code that works correctly on the first attempt and follows industry best practices.
The accuracy varies depending on the complexity and specificity of the task. Codex is most accurate when working on well-defined problems that have standard solutions, such as creating REST API endpoints, implementing user authentication, or setting up database schemas. It also performs well when working within popular frameworks and libraries that were well-represented in its training data. However, accuracy can decrease with highly specialized domains, cutting-edge technologies that weren’t in the training data, or tasks that require very specific business logic unique to your organization. Complex multi-step problems that require deep understanding of intricate requirements may also present challenges, though the current version’s reasoning capabilities help it handle more sophisticated scenarios than previous iterations.
What makes the current Codex particularly reliable is its ability to test and iterate on its own code. Unlike simple code generation tools, Codex can run the code it writes, identify errors or failures, and refine its solution until it works correctly. This iterative approach significantly improves the final accuracy because the system can catch and fix issues that might not be apparent from the initial code generation. The system also provides detailed logs and evidence of its testing process, allowing developers to understand how the code was validated and what edge cases were considered. While the generated code still requires human review, especially for production systems, the combination of high initial accuracy and self-correction capabilities makes Codex a reliable tool for most software development tasks when used appropriately.