Quick answer
The local whoburnedmore CLI command is npx whoburnedmore --local. Decline its optional publish offer to keep the report local. The standard command signs in and submits daily aggregates. Neither mode submits prompts, source code, or filenames.
What this guide answers
The reader is deciding whether a ChatGPT plan upgrade is worth it for Codex.
Best for
Codex CLI and Codex cloud users on individual ChatGPT plans.
Count real limit hits
A single bad day is not enough evidence. Count how often limits stop work and whether the work was important.
- Record date and task.
- Note local or cloud.
- Estimate delay impact.
Separate task complexity from plan fit
Large tasks consume more because they require more context, tool calls, and review. Sometimes the fix is a smaller brief, not a larger plan.
- Split ambiguous tasks.
- Ask for check-ins.
- Run tests between agent passes.
Watch cloud task behavior
Cloud tasks can be easier to start than to supervise. If cloud work dominates your limit pressure, tighten acceptance criteria before upgrading.
- Use narrow scopes.
- Require diffs and test output.
- Avoid open-ended cleanup prompts.
Make the plan decision weekly
Use a weekly review to compare blocked time, successful shipped work, and total burn. If those numbers consistently point up, the upgrade case is stronger.
- One-week baseline.
- One-week constrained workflow.
- Then decide.
Mistakes to avoid
Optimizing before measuring
It is tempting to change plans, switch tools, or clamp down on usage as soon as codex plus vs pro limits becomes a concern. That usually hides the real issue. Measure the current workflow first, then decide whether the problem is volume, scope, model choice, team policy, or one unusually expensive session.
Comparing vendor units directly
A request, credit, ACU, message, token, and quota are not interchangeable units. Convert each tool back to the work it produced: the feature, bug fix, review, prototype, or incident response. That makes cross-tool comparison fair enough to act on.
Treating high burn as automatically bad
A high-burn session can be waste, but it can also be the session that unblocked a release. Add outcome notes before judging the number. The goal is not low usage; the goal is useful, explainable usage that the team can repeat.
Practical playbook
What to measure first
Start with the signal most likely to change behavior for this topic: limit hits. For someone searching codex plus vs pro limits, the useful answer is not a generic definition. It is a repeatable way to decide whether the current workflow is healthy, whether the cost is justified, and which next action will reduce waste without killing useful AI experimentation.
How to turn it into a habit
Use a simple weekly rhythm: measure the biggest burn, label the task, record whether it shipped value, and change one prompt or routing rule. The sections above cover count real limit hits, separate task complexity from plan fit, watch cloud task behavior, and make the plan decision weekly. Those are the pieces that make the guide actionable instead of another pricing summary.
How whoburnedmore fits
whoburnedmore is the measurement layer, not the policy layer. It reads local AI coding-agent usage, keeps source code out of the upload path, and gives you a shared burn view. That means this guide can stay focused on decisions: when to upgrade, when to narrow context, when to switch tools, and when a high-burn session was actually worth it.
Decision checklist
Can you explain why codex plus vs pro limits matters for a real task this week?
Do you know which tool, model, project, or workflow created the largest burn?
Is the next action a smaller prompt, a different tool, a plan change, or a team policy update?
Can you review the result without uploading source code or raw prompt content?