What are the Codex CLI effort levels?

Codex CLI supports reasoning effort levels that typically include low, medium, high, and xhigh. Each controls how deeply the model reasons before responding: higher levels produce more thorough output on complex tasks but are slower and use more tokens. The exact levels available depend on the model you're using. Check the Codex CLI documentation for the current options.

When should I use xhigh effort in Codex CLI?

Use xhigh when a lower effort level has already produced an unsatisfactory result on the same task, or when the task genuinely requires extended multi-step reasoning: complex bugs, architectural decisions, or security-sensitive changes where a wrong answer is costly. Don't default to xhigh for every task; it doesn't improve results on routine work, it just costs more.

Does higher effort always produce better code?

No. On well-defined, routine tasks, lower effort levels often produce equivalent results. Higher effort helps most on ambiguous problems, multi-file reasoning, and novel challenges. The best approach is to test your actual task types at different levels and use the results to set defaults, rather than assuming higher effort is always better.

How do I set a default effort level in Codex CLI?

You can pass the effort flag on each invocation, or set a default in your Codex CLI configuration file. The configuration options and their syntax are in the codex --help output and the official OpenAI documentation. Check both for the most current options.

Does effort level affect token usage and cost?

Yes. Higher effort levels typically use more tokens because the model performs more internal reasoning steps. The exact usage and cost depend on the model and current OpenAI pricing. For high-volume automated use, the difference between levels can be significant — test the cost at your expected volume before setting a default for a pipeline.

Codex CLI Effort: Choose a Reasoning Level for the Task

Codex CLI's effort setting controls how deeply the model reasons before responding. Here's how to pick the right level and track your team's agreed defaults.

August 17, 2026Updated October 21, 20266 min read

Codex CLI's effort setting determines how much reasoning the model applies before producing a response. Higher effort levels are more thorough but slower and costlier; lower levels are faster and cheaper but less reliable on complex problems. The right choice is not always the highest setting: matching the effort level to the task type produces better results per dollar than always running at maximum. Tools like fenbs keep the team's agreed defaults and the evaluation that justified them, so every developer works from the same reasoned choice.

The available effort levels and their exact behavior depend on the model and your Codex CLI version. Check the OpenAI documentation (opens in a new tab) for the current flag options and what each level means for the model you're running.

What the effort levels do

Codex CLI's effort levels — typically low, medium, high, and xhigh — control the depth of the model's internal reasoning before it outputs a response. At low, the model uses a compact reasoning path and responds quickly. At xhigh, it works through more reasoning steps, which can surface solutions to harder problems but takes noticeably longer and uses more tokens.

This is not a quality dial that goes from bad to good. A low effort response to a well-scoped, familiar task is often just as accurate as an xhigh response — and much faster. The effort level affects how the model handles ambiguity, multi-step reasoning, and novel problems. On a straightforward task with a clear right answer, extra reasoning often doesn't change the output.

How to set the effort level

Pass the effort level as a flag when invoking Codex CLI. The flag name and accepted values are in the codex --help output and the official documentation. You can also set a default in your Codex CLI configuration file so you don't have to specify it on every call.

Example: passing an effort flag

codex --effort high "refactor this function to reduce nesting"

Not all effort levels are available for all models. If you specify an unsupported level, Codex CLI will report an error or fall back to a default. Verify which levels your model supports before building a workflow that depends on a specific one.

Matching effort to task type

The clearest way to pick a level is by the task's complexity and the consequences of an error. A wrong answer on a routine task is easy to spot and fix. A wrong answer on a complex architectural decision is harder to detect and expensive to reverse.

Effort level guidance by task type
Task type	Suggested effort	Reason
Boilerplate generation, formatting, simple renaming	low	Well-defined, easily verified. Extra reasoning rarely changes the output.
Implementing a feature with clear requirements	medium	Needs some reasoning about structure and edge cases, but not extended deliberation.
Debugging a non-obvious error or refactoring across multiple files	high	Requires tracking more context and reasoning about cause and effect.
Complex architecture decisions, security-sensitive changes, or problems you've already tried at lower effort	xhigh	Use extended reasoning when the task is genuinely hard or when lower levels have already failed.

Cost and speed across levels

Each step up in effort level increases token usage and response time. The exact multipliers depend on the model and current pricing, which OpenAI publishes on its pricing page. For a solo developer running Codex CLI interactively, the cost difference between levels is usually small. For a team running automated pipelines with many tasks per day, the accumulated difference can be significant.

Don't default to xhigh for everything. It doesn't make routine tasks more reliable — it just costs more and takes longer. Use it deliberately on tasks that have actually shown poor results at lower effort. Codex CLI pricing details are at the /blog/codex-cli-pricing guide.

Testing your team's defaults

The best defaults come from observation, not assumption. Pick a set of representative tasks across your codebase — the kinds your team runs most often — and test them at each effort level. Note where lower effort produces equivalent results and where higher effort produces meaningfully better ones. A few hours of structured testing produces a table of task types and recommended levels that applies to your actual work.

Review the defaults when you update Codex CLI or switch models. Model updates sometimes shift the point where higher effort pays off. A decision that was right for one model version may not be right for the next.

Other Codex CLI settings like fast mode are covered at /blog/codex-cli-fast-mode. For Codex CLI usage in automated pipelines, see /blog/codex-subagents.

How fenbs helps

Record the effort defaults and keep them current

In fenbs, Decisions and rules is where your team's Codex CLI effort defaults live: which task types use which level, the evaluation that justified it, and who made the call. When a model update changes the tradeoffs, record the updated decision with the new evaluation — don't lose the history of what changed and why.

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How to do this with fenbs

Use fenbs to record your effort level decisions alongside your Codex CLI project context. This makes onboarding faster and keeps the reasoning behind the defaults accessible.

Create a free account. A personal board is free. For team usage, set up a team workspace.
Add a project for your Codex CLI development work.
Run the effort level evaluation and record the results as a task: which levels you tested, the task types, the quality and speed outcomes.
Write the agreed defaults in Decisions and rules: one rule per task category with the recommended effort level and the reasoning.
Connect a supported assistant over MCP from Integrations. When a teammate or AI asks what effort level to use for a given task, the decision is in the project record.