Why ChatGPT Power Users Run Out of Allowance So Fast

When a heavy user says they have "run out of tokens" on ChatGPT, the constraint is almost never their internet connection. It is a compute allowance, metered in several different ways at once, and the features power users prefer are the ones that drain it fastest.

Bandwidth is not the bottleneck

In India, mobile data costs about $2 for 20GB a month, so network speed rarely stops a heavy user. What stops them is that ChatGPT charges for computing power, and OpenAI has only so much of it to share.

Several meters, not one

Plain text chat is described as unlimited on every plan, subject to abuse safeguards. Everything heavier has its own meter: reasoning-model messages per week, Codex usage in rolling five-hour windows with weekly caps, Deep Research runs per 30 days, file uploads (80 per three hours on paid tiers, 3 per day on Free), and voice hours (AI Toolbox). OpenAI deliberately does not publish exact numbers for Thinking and image allowances, so the in-product warning is the only authoritative figure.

Context windows form a second ceiling. Reported figures are 27,000 tokens on Free, 54,000 for standard chat on Go, Plus and Business, and 128,000 on Pro, with reasoning models reaching 256,000 and 400,000 on the higher tiers.

Five reasons heavy users burn through it

Bigger models cost more. On Codex, the deepest model (GPT-5.6 Sol) is billed at 100 credits per million input tokens, against 5 for the fastest (Luna), a 20-fold gap, according to SimpleMetrics.

Long conversations re-send their history. Each turn re-processes the accumulated context. Thirty turns of about 1,000 tokens each means roughly 465,000 input tokens processed, not 30,000. Caching softens the effect: cached input is billed at one tenth of the normal rate.

Output is expensive. Output tokens cost five to six times the input rate on the same Codex model, so long generations, multi-file code edits and full-document rewrites drain allowance quickly.

Agents loop and use tools. Codex consumption depends on codebase size, task complexity, session length and whether the task runs locally or in the cloud. Large repositories, long instruction files and active tool connections add tokens to every step.

Images, voice and video have their own, heavier budgets. They are metered separately from text and tend to be tighter.

Why OpenAI keeps the caps

In April 2026 OpenAI reset its limits, citing fast growth in Codex and Sora and the fact that higher caps "weaken demand smoothing and fairness controls and make the system run into a capacity ceiling faster," as PAS7 Studio reports. The response had three parts: a new $100 Pro tier, fallback to a lighter model instead of a hard stop (for example, to a mini model after the weekly Thinking cap, reported at up to 3,000 messages a week on Plus), and credits for Codex use beyond the included allowance.

What users actually see

For most people the symptom is not a locked account. It is a quiet drop in quality when the model switches, or a bill when Codex moves from included usage to credits. That is why heavy users describe the change as "throttling" even when the stated cause is capacity.

A caution on the numbers

Model names and caps change often, and the tables that circulate online are third-party snapshots. Check ChatGPT's own usage page before planning a workload around any figure quoted here.