Codex Credits: How Metering, Purchases, and Weekly Limits Work

OpenAI Codex does not have one universal message cap. Before you wait, buy credits, change plans, or switch to an API key, open the signed-in Codex Usage dashboard and run /status in the active CLI session.

The ordering answer is simple: included plan usage is consumed first; supported work draws from available credits after the relevant included limit; API-key work is billed separately on OpenAI Platform. Credits do not clear the weekly meter or change its reset date. They provide paid continuation after the plan limit.

Those two surfaces answer different questions. The dashboard owns your account's remaining entitlement and reset information. /status shows the active session, context usage, configuration, and available rate-limit information. If account-level /usage is available to you, use it for account token activity; do not treat it as interchangeable with /status.

Your next task is to identify which of these six counters actually stopped you:

What the five-hour Codex limit actually means

As checked on August 4, 2026, OpenAI's live Codex pricing documentation describes eligible local messages and cloud chats as sharing one five-hour window. It also says additional weekly limits may apply.

That is a reset window, not five hours of guaranteed working time. A complex repository task can consume much more of the allowance than a small edit. OpenAI says consumption varies with the model, task size and complexity, local versus cloud execution, context, reasoning, tool use, retrieval, and caching. Prompt length alone is not a reliable estimator.

The practical consequences are important:

• A local task can reduce the same five-hour pool used by an eligible cloud chat.
• A fresh five-hour window does not prove the weekly counter has reset.
• Two prompts that look similar can consume different amounts.
• Reaching a limit during an active turn does not necessarily terminate that turn immediately. OpenAI says Codex may finish the active turn, subject to fair-use limits; check the banner again after the turn completes.

Some agentic features can also share usage or credits when they are available on the plan. The current ChatGPT-plan help page names Codex, ChatGPT Work, ChatGPT for Excel, and Workspace Agents in that conditional shared pool. This does not mean every ordinary ChatGPT feature uses the Codex counter.

Do not turn a message range into a credit invoice

OpenAI's plan table shows ranges such as local messages / 5h; the credit rate card meters token types. The first describes included headroom, while the second prices continuation after included limits. There is no fixed conversion from one message to a credit amount because one task can touch a single known file and another can traverse a monorepo, call tools, and produce a long result.

Use the signed-in dashboard to decide whether you can continue. Use the current rate card to understand credit consumption. Model names, ranges, and promotions can change, so this article does not mirror the full plan table.

For a Pro-only account check, use the Pro Codex usage-limit guide. Enterprise and Edu still require a contract check: flexible-pricing workspaces scale with credits, while a small subset may remain on a legacy rate card.

Credits are not the plan window or the API bill

Codex credits can extend supported work after included limits are reached, but only where the account or workspace offers that route. OpenAI's current Codex rate card meters most current credit customers by input, cached-input, and output tokens. Model choice, output volume, and speed configuration change credit consumption.

Calculate credits from the rate card

For an account on the token-based rate card:

credits = input tokens / 1,000,000 × input rate + cached input tokens / 1,000,000 × cached-input rate + output tokens / 1,000,000 × output rate

The August 4, 2026 table lists GPT-5.4 mini at 18.75 credits per million input tokens, 1.875 per million cached-input tokens, and 113 per million output tokens. A task with 100,000 input, 400,000 cached input, and 20,000 output works out to 1.875 + 0.75 + 2.26 = 4.885 credits, or about 4.89. This is a worked reading example, not a bill promise; the live rate card, measured tokens, and dashboard rounding control the actual deduction.

Buy credits from the account that will spend them

Eligible Plus and Pro users can buy credits on the web or in the Codex app at Codex Settings > Usage > Credits. Some eligible accounts can enable auto top-up. When the balance drops below the chosen minimum, the default payment method buys only enough to reach the target balance; enabling it while already below the threshold may trigger a purchase immediately.

Before buying, confirm that the Usage dashboard shows an included-plan limit rather than a context issue or API 429, that the Credits control belongs to the intended account or workspace, and that the work justifies pay-as-you-go continuation. Purchased credits are valid for 12 months, do not roll over, are generally non-refundable, and are non-transferable. They may also be shared across other supported agentic features on the plan.

The order matters:

1. Included plan usage is consumed first.
2. If your plan supports it and you have credits, the product can continue against that credit balance.
3. An API key starts a different, usage-based Platform contract.

Purchased ChatGPT credits are not an OpenAI Platform API balance. Likewise, a Platform API key does not refill a ChatGPT five-hour or weekly counter. API-key work is billed by API tokens and remains subject to organization/project rate limits and budgets.

This is a deliberate route decision, not a workaround. Use the Codex API-key versus subscription guide before moving a workload. An API-key route also does not include every Codex cloud feature: the official pricing source specifically excludes Codex cloud features such as GitHub review and Slack from the API-key row.

Code review has a boundary of its own

GitHub-triggered or automatic Codex reviews use the Code Review meter. A review you run locally, or a review outside the GitHub-triggered flow, counts against general Codex usage instead.

Do not assume that writing “review this pull request” creates an isolated review quota. The trigger and surface determine the meter. If the official plan table shows Code Review as unavailable for a model or route, the local review still belongs to the general local-message path.

Why your allowance can drain faster than expected

The official driver list is more useful than a community formula. There is no trustworthy universal equation such as “one minute of reasoning equals X percent.” Instead, inspect the work itself:

• Model: deeper or more capable models can use more allowance for the same job.
• Repository and context: files, chat history, AGENTS.md, tool definitions, screenshots, and prior results all contribute context.
• Reasoning and output: long analysis and output-heavy tasks consume differently from narrow edits.
• Tools and retrieval: MCP servers, search, browser work, test logs, and repeated tool results add input and output.
• Execution surface: local messages and cloud chats can have different task shapes even while sharing the same five-hour window.
• Caching: cached input changes metered credit consumption, but it does not make a task free.

If the concern is specifically context and token occupancy, not plan entitlement, use the Codex token-usage guide. A context problem can often be reduced immediately; a consumed plan window cannot be rolled back by clearing the chat.

Measure one variable instead of guessing

Use one bounded task to learn how your own workflow behaves. This is not a benchmark for other accounts.

1. Open the Usage dashboard and record the remaining percentage and reset time.
2. Run /status; record the model, context usage, and whether the task is local or cloud.
3. Define one task with exact files, one outcome, and a stop condition.
4. Record the tools involved: MCP servers, retrieval, browser work, tests, image generation, or GitHub review.
5. Complete the task, then record the ending usage value.
6. Repeat only if useful, changing one factor: model, context size, tool set, task scope, or speed mode.

Do not change the model, clear context, disable tools, and rewrite the task at the same time. That may lower usage, but it will not tell you which change mattered. Also do not publish your result as “the Codex limit”; it is one observation under one account, model, repository, and date.

Choose the next action from the meter

Use the smallest action that matches the evidence:

• Five-hour window reached: let the active turn finish if allowed, then wait for the reset shown in the account.
• Weekly limit reached: wait for the weekly reset. Reopening sessions or repeatedly retrying will not turn it into a five-hour issue.
• Future consumption is too high: narrow context, scope AGENTS.md, disable unused MCP servers, reduce noisy logs, or use a lighter model for routine work.
• Credits are offered: check the rate card and expected workload before purchasing; credits are a paid continuation route, not a fix for bloated context.
• API rate limit: reduce concurrency or throughput and follow the API-specific headers and limits.
• API spend/quota: correct the organization, project, billing, or budget. Do not expect a ChatGPT subscription to pay it.
• The pattern persists every week: only then compare the plan-specific headroom with the cost of credits or a deliberate API route.

Use only reset, credit, and purchase controls shown in your signed-in account or current OpenAI documentation. Do not share accounts, resell credentials, use unofficial reset scripts, or treat a temporary promotion as permanent entitlement.

The useful answer to “What is my Codex usage limit?” is therefore not a single number. It is: which counter moved, where is its live owner, and what one action follows from that evidence?