dots and ChatGPT limits: what counts and what does not

Conversations with your dot do not count toward your ChatGPT usage limits. Tasks your dot starts or manages in Codex or ChatGPT Work do count, like any other task. They are two separate budgets, and understanding the split is what prevents end-of-month surprises.

The two budgets

Action	Uses ChatGPT limits?
Talking with your dot, asking questions, thinking out loud	No
Having the dot use Codex	Yes
Having the dot use ChatGPT Work	Yes
Reviewing your dot’s activity and computer	No

Why OpenAI separated them

A dot is meant to be a place to think and steer work, not only to execute it. If every exchange drew on your limits, the natural usage pattern — leaving context open and coming back through the day — would be penalized. By excluding conversation from the count, OpenAI nudges the behaviour that makes the product useful.

Heavy work, by contrast, flows through the products where compute is already metered: Codex and ChatGPT Work.

The dot’s own usage quota

Separately from ChatGPT limits, the plan includes a usage quota for deeper work your dot can do, with expanded limits during the first month after launch.

How to read it:

• It covers work the dot does on its own, on its computer.
• It is not unlimited: it is the real constraint of the plan.
• The first-month boost is temporary and should not be mistaken for permanent capacity.

Your Pro tier changes your allowance

Not all Pro tiers carry the same capacity. ChatGPT Pro has three monthly prices, and the dot’s deeper-work allowance scales with the tier.

Tier	Price	Note
Pro 100	$100/month	Entry point with dots included
Pro 200	$200/month	Reopened with a lower allowance
Pro 500	$500/month	Includes Astra Ultrafast

Two things worth checking before subscribing: Pro 200 has an announced allowance cut effective 29 October 2026, and Astra Ultrafast — the higher-speed mode — only arrives with the $500 tier.

Working without hitting the ceiling

1. Separate talking from delegating. If you are exploring an idea, do it in the conversation with your dot rather than by launching tasks.
2. Scope your assignments. A checkable objective costs less than an open, vague mandate.
3. Measure the first month. Use the expanded limits to learn how much deeper work your real case needs, and treat that number as your baseline.
4. Review before scaling. If the bottleneck shows up in month one, the problem is usually assignment size, not capacity.

OpenAI does not publish exact consumption numbers or top-up mechanics in a stable form. Values change often; check the current state in the app before planning a budget.