OpenAI has expanded Codex's context window to 1 million tokens, a major leap from the previous limits. This allows developers to feed entire large codebases into a single session, enabling more coherent and context-aware code generation. However, enabling this feature comes with trade-offs: higher token costs, increased latency, and potential degradation in response quality for very long contexts. The configuration process involves selecting the right model variant and adjusting parameters like temperature and max tokens. Teams should evaluate their specific use cases—such as refactoring large monorepos or analyzing legacy code—to determine if the benefits outweigh the costs. Early adopters report mixed results, with some praising the improved context retention and others noting performance issues. As with any AI tool, careful testing and monitoring are essential before full deployment. This update signals a trend toward larger context windows across AI models, which will shape future development workflows.
OpenAI's Codex now supports a 1M token context. Learn when to enable it, how to configure it, and the trade-offs involved.