GPT-5.6 Luna Reddit Codex Quota and Cache Cost: A Hands-on Measurement

One-sentence takeaway

A Luna-only Codex Plus session incurred an API-equivalent cost of approximately $2.43 based on 3.52M uncached input tokens, 73.79M cached input tokens, and 205K output tokens, yet consumed approximately 16–17% of the weekly quota. This shows that subscription quota weighting cannot be equated directly with public API prices.

Test environment

• Client: Codex, ChatGPT Plus.

• Model: The commenter said they used only GPT-5.6 Luna; the specific reasoning effort was not disclosed.

• Date: 2026-08-01.

• Task: One Codex session; the original post body was deleted, so the task type and complete tool trace are unavailable.

• Data source: The commenter transcribed input, cached input, output, and total processed tokens from account/session statistics, then calculated the cost using the API prices in effect at the time.

Input/configuration

The public ledger contains no prompt, codebase, task-success criterion, tool version, cache TTL, model snapshot, or complete log. It allows the arithmetic to be checked, but not the task quality or whether the cache hits came from the same prefix.

Result data

Quantities as given in the commenter's ledger:

The same discussion also contains conflicting personal experiences: one person said that two hours of Luna high consumed only about 1%, while another said that a normal task with Luna xhigh consumed 15%; one user noted that an explicit implementation plan, narrow tasks, and blocking conditions can reduce repetition, while another encountered a Luna sub-agent that was actually routed to Sol high.

Conclusions

• The public API prices of $0.20/$0.02/$1.20 can explain the API-equivalent ledger only; they cannot explain the weekly quota deduction logic for ChatGPT Plus/Codex.

• Luna's total cost depends on cache hits, context rereads, reasoning level, automatic compaction, sub-agents, and back-and-forth fixes; the price per prompt is not the cost of the final result.

• For Codex users, providing a detailed goal and implementation plan, specifying clear stop/blocking conditions, and breaking large tasks into a verifiable bug or feature can help control repeated Luna loops.

Limitations

• The original post was deleted, so the task context, success criteria, and complete screenshots are unavailable; the key figures are self-reported by a single commenter.

• The weekly quota conversion rules, cache billing, and model weighting may have changed; API-equivalent cost is not a bill.

• The experiences reported by multiple commenters conflict significantly, indicating that project size, reasoning level, sub-agent use, and the Codex account plan are confounding factors.

• This is not a controlled-variable benchmark, and there is no same-task comparison with Sol/Terra; it cannot establish Luna's general cost or quality.

Reproduction steps

1. Fix the ChatGPT/Codex plan, Luna model, and reasoning effort, and record the model snapshot and client version.

2. Choose a rollback-safe, testable task, and save the prompt, codebase size, tool calls, number of automatic compactions, and the actual model used by each sub-agent.

3. At the end of the session, transcribe uncached input, cached input, output, total processed tokens, and the account's weekly quota change.

4. Using the official API prices for that day, calculate uncached, cached, and output costs separately, then place them alongside the actual quota deduction; do not add the two together.

5. Repeat with at least 5 narrow tasks and 5 open-ended tasks, reporting the cost, elapsed time, rework, and number of escalations to Sol/Terra for each acceptable result.

Source excerpt or observation (short quote for compliance only)

The commenter cautioned, “You shouldn’t measure it per prompt or per task, but by the final result.” That is precisely the scope boundary of this ledger.