OpenAI’s new model, Codex, offers significant improvements in coding efficiency, code refactoring, and integration with developer tools, making it a powerful AI assistant for software engineering tasks despite some issues with UI generation, search functionality, and confusing branding. While the model shows promise with better token usage and open-source tooling, the creator highlights the need for a smoother user experience and clearer naming conventions to fully realize its potential.
OpenAI has released a new model called Codex, specifically designed for developers to improve coding tasks. The creator of the video was given early access to test the model and shares his experiences, highlighting both its strengths and weaknesses. Codex shows significant improvements in code refactoring and software engineering benchmarks compared to previous models like GPT-5. A key feature of Codex is its deep integration with tools such as the Codex CLI, web interface, and extension, aiming to create a more seamless developer experience. However, the naming conventions for these tools and models are confusing, with multiple products sharing similar names, which the creator finds frustrating.
One of the standout improvements in Codex is its efficiency in token usage. For simpler tasks, Codex uses dramatically fewer tokens—up to 93.7% less than GPT-5—making it faster and less resource-intensive. For more complex tasks, it can use more tokens to spend extra time reasoning, editing, and testing code, which is a positive sign of its flexibility. Despite these improvements, the creator notes that Codex behaves differently from previous models, especially in UI generation, where it sometimes produces buggy or less polished interfaces. This inconsistency means developers might need to switch between models depending on the task.
The video also discusses the challenges Codex faces with internet search capabilities. While it can access the web via the CLI, its search functionality is currently poor, often producing irrelevant or incorrect results. This limitation hampers its ability to fix errors or find up-to-date information effectively. Additionally, the user experience around enabling internet access and managing environments is cumbersome and unintuitive, with slow setup processes and confusing UI elements. These issues detract from the otherwise promising capabilities of the model and its associated tools.
OpenAI’s approach with Codex includes open-sourcing much of the related tooling under the Apache 2.0 license, which is unusual for a company of its size and suggests a strategy to foster wider adoption and community involvement. The model is designed to act like a co-worker, capable of running code, conducting code reviews, and identifying critical flaws more accurately than previous AI tools. Despite some rough edges, this makes Codex a compelling option for developers looking for AI assistance in coding, especially with its ability to reason through dependencies and validate code correctness by running tests in the cloud.
Overall, while Codex represents a significant step forward in AI-assisted coding with better token efficiency, deeper tool integration, and improved code review capabilities, it still has notable shortcomings. The web interface and editor extensions are clunky and buggy, the search functionality is weak, and the branding confusion may frustrate users. The creator remains cautiously optimistic, appreciating the hard work behind the product and its potential, but urges OpenAI to improve the user experience and clarify naming to avoid confusion. He invites viewers to share their own experiences with Codex as the ecosystem continues to evolve.