A developer who used OpenAI Codex more heavily than Anthropic Claude for a week found Codex generally more contained, technical in tone, and less prone to adding architectural complexity. In Ruby and Ruby on Rails changes, Codex produced fewer comments, while Claude more often introduced abstractions, concepts, Sorbet signatures, and type aliases. When both tools implemented the same requirement from the same documents, Claude produced somewhat more complex code but handled additional cases, according to the developer. The developer felt Codex made initial changes faster, but its extensive test reruns and review work eliminated any overall time advantage in finishing pull requests. Codex also encouraged smaller, focused sessions rather than long-running conversations. Claude remained the preferred tool for urgent debugging because of familiarity, not because it was judged superior. Codex made branch-management errors, including rebasing a branch onto main rather than its target branch and creating a pull request with more than 4,000 additions. Codex was also less effective in the developer’s Jira and Atlassian CLI-based environment, while its MCP CLI login flow was preferred because it explicitly requested the `codex mcp login` command and opened the appropriate authorization process. The developer characterized Claude as more likely to anticipate and act on inferred needs, while Codex more closely follows explicit instructions and stops once it appears finished.

A customer experienced a sudden loss of 491.8 Codex credits, which vanished without any recorded usage. After purchasing an additional 1,000 credits for $48.40, those credits also failed to appear in the account.

Codex is reportedly causing excessive wear on devices due to its resource-intensive operations. Users have expressed concerns about the longevity and performance of their hardware when running Codex.

Frequent interaction with LLMs can lead to burnout, even with average usage rates. Users may engage with models like Claude Code and Codex for coding tasks while actively reviewing and revising outputs.

Working with language models like Claude and Codex can be mentally exhausting, often requiring long sessions to navigate complex options. Users may experience challenges such as context rot and model bloat, leading to a need for breaks to regain clarity and find effective solutions.