OpenAI Codex Models: How to Choose for a Coding Task
The word "Codex" in OpenAI's ecosystem now refers to a CLI tool and a cloud coding agent, not a single underlying model. Here is how to tell the tools apart and choose for a real task.
Updated 6 min read
The name "Codex" from OpenAI has shifted meaning more than once. The original Codex model — a code-focused model from 2021 — was deprecated in March 2023. Today, "Codex" mainly refers to two things: the OpenAI Codex CLI, a command-line coding agent, and the Codex cloud agent available within the ChatGPT interface. Both run on OpenAI's current reasoning models, not on a separate "Codex" model.
Choosing the right tool for a coding task means understanding which surface you are working in and what the task actually needs. Tools like fenbs help teams record the selection criteria and lessons from evaluating different options, so the next person — or a future session — starts from what was already learned. See chatgpt codex for more on how the cloud agent works.
What happened to the original Codex model
OpenAI's original Codex model was fine-tuned on code and released in 2021 as the engine behind GitHub Copilot's first version. It was accessed through the API and used to generate code completions from natural-language prompts. OpenAI deprecated the model in March 2023 as its GPT-4 family became more capable across both code and language tasks.
Since then, code generation in OpenAI's products has been powered by the general GPT and o-series models rather than a separately maintained "Codex" model. The name Codex was later repurposed for the CLI tool and the cloud agent product.
OpenAI coding tools today
OpenAI currently offers three main coding surfaces:
- Codex CLI. A command-line coding agent that runs locally. It reads your codebase, executes commands and makes changes to files. The underlying model is configurable;
codex-mini-latestis the default lightweight option. Check the Codex API key guide for setup. - The Codex cloud agent (in ChatGPT). An agent that works in a cloud sandbox, visible in the ChatGPT interface. It can handle longer, multi-step tasks asynchronously — useful for tasks you do not want to run on your local machine.
- Direct API or IDE tool use. Using GPT-4.1 or an o-series model directly through the API or in a tool like Cursor gives you more control over the model, context and output format. See cursor vs codex for a comparison of these approaches.
The models behind Codex tools
Neither the Codex CLI nor the Codex cloud agent runs on a special "Codex" model. The CLI default is a smaller, fast model called codex-mini-latest. The cloud agent uses OpenAI's current capable models. Both sit on top of the standard GPT and reasoning model infrastructure.
The model choice in the CLI is configurable. For tasks that need deeper reasoning — complex refactors, architectural changes, tasks with many constraints — using o3 or o4-mini via the CLI produces better results than the default lightweight model, at higher cost. Test on a representative task before committing a team workflow to any particular configuration.
How to choose a Codex tool for a coding task
- Short, focused tasks in your local codebase. The Codex CLI in a local session is the most direct option. It reads your files, makes changes and runs tests in your environment.
- Longer tasks you want to run in the background. The Codex cloud agent works asynchronously, so you can start a task and check in on it rather than keeping a local session open.
- Code tasks needing the deepest reasoning. Use the o-series models directly in Cursor or via the API. See codex vs github copilot for how these tools differ in daily coding workflows.
- Team coding workflows with shared context. Any of these tools can benefit from a shared project record that tells them what is in progress, what decisions have been made and what to check before committing.
Recording decisions about model and tool selection
Coding tool selection decisions are worth writing down, especially for teams. The questions that come up repeatedly include: which model configuration produced acceptable quality for the task type, what the cost implications of the o-series models were at scale, and what the team found when it compared the CLI to the cloud agent. Those are hard to reconstruct from memory six months later.
Lessons from evaluation are equally useful. If a particular model configuration failed on a class of refactoring task, writing that down means the next team member does not repeat the same evaluation. A shared project record that holds decisions and lessons is more durable than an email thread or a shared note.
Record what the team learned about Codex tools
Add model selection decisions to fenbs as Decisions and rules. Record evaluation lessons with fenbs's Lessons learned feature: what happened, what you learned, and what to do differently. Connected coding assistants read these at the start of a session. Check Integrations for which coding tools connect to fenbs. Browse the AI models topic for related guides.
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How to do this with fenbs
- Create a free account. Free for one person; no credit card needed.
- Add a project for the codebase or task area you are using Codex tools on. Write the goal, the current tech stack and any constraints the AI agent needs to respect.
- Record model configuration decisions: which model you use in the CLI, why you chose it over the alternatives, and what you tested.
- When an evaluation produces a useful lesson, add it to Lessons learned: what the model did well, where it fell short, what you would change.
- Connect a supported assistant from Integrations. Codex CLI connects over MCP; the setup guide is on the integrations page.
fenbs does not integrate with the Codex cloud agent directly. It keeps the project record that local coding assistants — including Codex CLI when connected — read before starting work.