Brian Castle evaluates OpenAI’s Codex CLI against Claude Code (Cloud Code) for AI coding tasks, finding Codex CLI promising but currently lacking key features like sub-agents and project-level custom commands that Cloud Code offers. He concludes that a hybrid workflow using multiple tools—Cloud Code for large features, Cursor for quick fixes, and Codex CLI for medium tasks—maximizes productivity and supports his spec-driven development approach.
In this video, Brian Castle explores whether switching from Claude Code (Cloud Code) to OpenAI’s Codex CLI is worthwhile for daily AI coding tasks. Having used Cloud Code extensively throughout the year alongside Cursor, Brian has noticed growing frustrations with Cloud Code’s inconsistent performance, slow response times on simple tasks, and increasingly restrictive token limits. Meanwhile, OpenAI has been aggressively improving Codex CLI, especially with the release of GPT-5 models tailored for coding, which has sparked curiosity and momentum among developers considering a switch.
Brian approaches this evaluation from the perspective of a product builder focused on shipping quality software efficiently. His priority is tools that help him move from idea to shipped product while maintaining craftsmanship. He emphasizes that his tool choices are pragmatic rather than driven by hype or brand loyalty, and he often uses multiple tools depending on the task. The evolving AI coding landscape, with healthy competition between Cloud Code and Codex CLI, has prompted him to reassess his workflow and test Codex CLI firsthand.
Upon trying Codex CLI within his preferred IDE, Cursor, Brian finds many similarities to Cloud Code, such as a terminal-based interface, slash commands, and features like initializing projects and compacting conversations to manage context. He appreciates the adoption of the open-source agents.md file standard, which promotes interoperability between tools. However, he also notes some usability quirks common to terminal interfaces, such as clunky multiline input and limited clipboard image pasting capabilities, which affect his daily workflow since he frequently uses screenshots to guide AI agents.
Brian identifies two major gaps in Codex CLI compared to Cloud Code: the lack of sub-agents and project-level custom commands. Sub-agents, which Cloud Code supports, allow isolated context windows for focused tasks and are crucial for his spec-driven development approach. Although Codex CLI has a pull request indicating sub-agent support may be coming, it is not yet available. Similarly, while Codex CLI supports custom prompts, they must be defined globally in the user’s home directory rather than per project, limiting flexibility for project-specific workflows.
Ultimately, Brian concludes that Codex CLI shows promise but currently feels a few steps behind Cloud Code in terms of features and polish. Rather than fully switching, he plans to adopt a hybrid approach using three tools: Cloud Code for large autonomous features, Cursor for quick iterative fixes, and Codex CLI for medium-sized tasks requiring both speed and power. He encourages viewers to orchestrate multiple AI coding tools to maximize productivity and recommends spec-driven development as a framework that works well across different AI tools.