Introduction
GPT-5 Codex is now live in API, marking a significant advancement in artificial intelligence applied to software development. This new specialized AI model has been optimized specifically for agentic and interactive programming tasks, offering advanced capabilities for developers and software engineering teams.
What is GPT-5 Codex
GPT-5 Codex is a specialized version of GPT-5 designed to excel at real-world programming tasks. The model has been trained with a particular focus on practical software engineering work, proving equally competent in quick interactive sessions and complex, extended tasks requiring hours of independent work.
Key Model Improvements
The model introduces several significant improvements over previous versions:
- Improved steerability: GPT-5 Codex delivers higher-quality code on complex engineering tasks like features, tests, debugging, refactors, and reviews without requiring lengthy and detailed instructions
- Adaptive reasoning: The model automatically adjusts reasoning time based on task complexity, being snappy in interactive sessions while capable of working independently for hours
- Excellence in code review: It has been specifically trained to conduct code reviews, navigating complex codebases and running code and tests to validate correctness
Integration and Compatibility
GPT-5 Codex is available through the Responses API and can be used by those with access to Codex CLI via API key. The model has been designed to seamlessly integrate into agentic programming applications and workflows, supporting Codex CLI, Codex IDE extension, Codex cloud environment, and GitHub.
It's important to note that this model is not a drop-in replacement for GPT-5, as it requires significantly different prompting techniques and does not support the verbosity parameter.
Best Practices for Prompting
For GPT-5 Codex, the core principle is "less is more." Best practices include:
- Starting with minimal prompts inspired by the Codex CLI system prompt
- Adding only the essential guidance truly needed
- Avoiding requests for preambles, which can cause premature model stopping
- Limiting tools to terminal tool and apply_patch
- Keeping tool descriptions concise by removing unnecessary details
Conclusion
GPT-5 Codex represents a significant evolution in AI for software development, offering specialized capabilities that make it ideal for agentic and interactive programming tasks. With its improvements in steerability, adaptive reasoning, and code review capabilities, it positions itself as an essential tool for modern developers and software engineering teams.
FAQ
What is GPT-5 Codex and how does it differ from GPT-5?
GPT-5 Codex is a specialized version of GPT-5 optimized for agentic and interactive programming tasks. It's not a drop-in replacement for GPT-5 as it requires different prompting techniques and is specifically trained for software engineering work.
How can I access GPT-5 Codex?
GPT-5 Codex is available through the Responses API for those with Codex CLI access via API key. It's only supported by the Responses API and does not support the verbosity parameter.
What are the main improvements of GPT-5 Codex over previous models?
Improvements include enhanced steerability for complex tasks, adaptive reasoning that adjusts to task complexity, and specific excellence in code review with abilities to navigate codebases and validate correctness.
What best practices should I follow for prompting with GPT-5 Codex?
The key principle is "less is more": use minimal prompts, add only essential guidance, avoid preambles, limit tools to terminal and apply_patch, and keep descriptions concise.
What use cases is GPT-5 Codex recommended for?
It's specifically recommended for agentic and interactive programming use cases, integrations with Codex CLI, IDE extensions, cloud environments, and GitHub work.
Can GPT-5 Codex completely replace other AI models for programming?
No, GPT-5 Codex is specifically optimized for Codex and is not a universal replacement. It's designed for specialized agentic programming use cases and requires different prompting approaches.