OpenAI Codex operates with a substantial token limit that accommodates large coding tasks and extensive codebase analysis. The current version of Codex, powered by the codex-1 model, supports a maximum context length of 192,000 tokens, which allows it to work with significant amounts of code and maintain context across large projects. This extensive token limit enables Codex to understand entire file structures, maintain awareness of relationships between different parts of a codebase, and generate comprehensive solutions that span multiple files. The large context window is particularly important for complex software engineering tasks that require understanding of how different components interact with each other.
The token allocation in Codex includes both input and output tokens, with the system designed to handle the variable nature of software development tasks. Unlike simple text generation where token usage is more predictable, coding tasks can vary dramatically in their token requirements depending on the complexity of the codebase, the scope of the requested changes, and the amount of context needed to understand the project structure. Codex manages this variability by intelligently prioritizing the most relevant context for each task, ensuring that the available tokens are used effectively to understand project requirements and generate appropriate solutions.
For users of the Codex CLI tool, there’s also access to a smaller, more efficient model called codex-mini-latest, which is optimized for faster, lower-latency interactions like code questions and quick edits. This model is priced at $1.50 per million input tokens and $6 per million output tokens through the API, with a 75% prompt caching discount that reduces costs for repeated interactions. The availability of both the full codex-1 model for complex tasks and the streamlined codex-mini for quick interactions provides flexibility for different types of development work. Users should be aware that extremely large codebases or requests might still approach token limits, but the generous 192k token context length accommodates the vast majority of real-world software development scenarios, making token limits less of a practical concern for most users compared to earlier AI coding tools.