Codex Usage and Limits: How to Check, Reset, Use Less

#Quick answer: where to check Codex usage right now

Last checked: 2026-08-23

To check Codex usage, open an active Codex CLI session and run /status. It's free, and it reports the percentage used of your five-hour and weekly limits along with the reset time. Since 2026-04-02, Codex meters usage in credits and tokens rather than per-message counts, within a rolling five-hour window plus weekly limits that may apply. Codex comes bundled into ChatGPT plans, so "chatgpt codex usage" is the same allowance you see in the CLI.

Two web dashboards are easy to confuse. The top organic result for "codex usage dashboard" is a Reddit thread asking where it moved. chatgpt.com/codex/settings/usage is your subscription page (limits, credits, reset); platform.openai.com/usage tracks only metered API spend, so a blank API dashboard does not mean zero subscription usage. Here is every surface, fastest first:

/usage (v0.140.0+) and /statusline add token history and a live footer readout; send one message before /status, and note that ccusage reports API-equivalent cost, not subscription spend.

# --- Inside an active Codex CLI session (type these at the prompt) ---

# Remaining five-hour and weekly capacity, reset time, and active model.
# Send at least one message first — /status needs session state to report limits,
# and it shows percentages, not banked credits.
/status

# Token activity and history without leaving the terminal (Codex CLI v0.140.0+).
# Tokens only, machine-local — treat it as an activity log, not a billing view.
/usage

# Pin live five-hour and weekly usage to the terminal footer for an always-on readout.
/statusline

# --- Optional: third-party per-day token view (run in your shell, not the Codex prompt) ---

# The open-source ccusage tool parses ~/.codex session logs into per-day tokens.
# Cost shown is API-equivalent, not subscription spend.
npx ccusage@latest codex daily

#How Codex usage limits work

#The rolling five-hour window and weekly caps

On ChatGPT plans, your local CLI messages and cloud chats share a single rolling five-hour window, with weekly limits that may apply on top. Code Review counts separately only when Codex runs reviews through GitHub (tagging @codex on a pull request); reviews you run locally still count toward your general limits.

#When your usage resets

The five-hour limit is a sliding window, not a fixed clock reset: capacity you consumed roughly five hours ago frees up as the oldest usage ages out. There is no single "reset hour," so read your real reset time and percentages from /status.

#What happened to the five-hour window in July 2026

You may still see posts claiming the five-hour limit is gone. That was true briefly: on 2026-07-12 OpenAI temporarily removed it on Plus, Pro, and Business plans, while the weekly limit still applied. By 2026-07-30 the docs again listed local messages and cloud tasks as sharing a five-hour window, still active on 2026-08-23. Treat any "limits are gone" claim as time-bound.

#Documented vs measured

OpenAI publishes estimated message ranges per model per five-hour window, for example GPT-5.6 Sol at roughly 10–100 local messages on Plus. Independent trackers report roughly 10–60 cloud tasks (about 30–150 requests) per window on Plus, depending on task and model.

Ranges, not guarantees. Documented ranges come from OpenAI's per-model estimates; measured figures come from independent trackers on Plus, and vary by task size and model. Read your real numbers from /status.

Figure 1: How a Codex turn becomes metered usage

Each turn resends context, so tokens split into fresh, cached, and output. Cached input costs roughly a tenth of fresh. Tokens convert to credits at the model's rate card, drawn from the rolling five-hour window under a weekly cap.

For the full credit rate card and plan-by-plan pricing, see Codex plans, credits, and API costs.

#What consumes Codex usage faster

Two sessions that feel identical can consume very different amounts: model choice, context, tools, and caching all move the number, and prompt length by itself tells you little.

Model choice has the biggest spread: within the GPT-5.6 family, Sol costs far more per token than Terra, which costs more than Luna, roughly an order of magnitude across the family. Context is next: more files, retrieval, and longer history add tokens every turn, including AGENTS.md, whose every token loads on every task. Each active MCP server also adds context to every message, so unused tool servers still cost tokens, and retries and long sessions grow context over time. Fast/speed mode increases credit consumption for supported models, and image generation burns limits about 3–5x faster than a comparable turn.

#How to use less native Codex without making the agent worse

Each driver maps to a documented lever you can pull without degrading output. OpenAI's own tips for making usage last longer:

• Control prompt size and instructions; remove dead context.
• Point at relevant files and line ranges, not whole directories.
• Match output to need; ask for the format and length you want.
• Shrink and nest AGENTS.md so less context loads on every task.
• Disable MCP servers you are not using this session.
• Route routine work to a smaller model (Terra or Luna) to extend local-message limits.

If you hit the limit mid-turn, the agent can finish the active turn under fair use; Plus and Pro users can then buy extra credits without upgrading, or drop to a smaller model.

#When a separate Fast inference route makes sense

Sometimes the plan limit itself is the blocker and you still have work to finish. You can keep the Codex harness as it is and route model traffic to a selected, separately billed catalog model through an OpenAI-compatible Responses API. This does not raise your native OpenAI quotas and does not accelerate an unchanged proprietary model.

Install the Fast CLI with npm install -g @inference/fast, then sign in with fast login. To route Codex to a separately billed model, run fast codex on. It configures ~/.codex/config.toml, adds an inference-net provider pointing to https://api.inference.net/v1, and defaults to the glm-5.2 model. Pick another with fast codex on --model <catalog-id>. Run fast codex status to confirm, restart Codex, and verify end to end; fast codex off reverts the TOML snapshot.

# Install the Fast CLI and sign in (browser approval flow).
npm install -g @inference/fast
fast login

# Route Codex to a separately billed catalog model.
# This configures ~/.codex/config.toml, adds an inference-net provider pointing
# to https://api.inference.net/v1, and defaults to the glm-5.2 model.
fast codex on

# Choose a different callable catalog model instead of the default.
fast codex on --model <catalog-id>

# Confirm the connection state, then restart Codex and open a new session.
fast codex status

# Restore your original configuration — reverts the TOML snapshot and removes the backup.
fast codex off

# Note: this is a separate metered path on a per-token inference wallet.
# It does not raise native OpenAI quotas and does not accelerate an unchanged model.
# After sending a request, verify it in the Fast Usage dashboard: /dashboard/usage

Confirm the request landed in your Fast Usage dashboard. This runs on a separate metered balance. New Fast accounts start with a one-time $26 welcome credit, a per-token wallet distinct from any native subscription top-up. If you run more than one coding agent, the same reversible pattern applies; you can compare Codex and Claude Code to decide which harness to route.

#Related reading

• Run Claude Code on Fast Inference — the same reversible route for another coding agent.
• Make your first Fast Inference API request — verify a working route and read usage and cost.
• Inference Economics: What are your options? — per-token versus dedicated-capacity framing behind a separate metered path.

#References

Sources verified 2026-08-23.

1. OpenAI Codex pricing — https://learn.chatgpt.com/docs/pricing
2. Codex subscription usage — https://chatgpt.com/codex/settings/usage
3. OpenAI API usage — https://platform.openai.com/usage
4. Sessionwatcher — https://sessionwatcher.com/guides/how-to-check-codex-usage
5. Viberank — https://www.viberank.app/blog/codex-token-usage-leaderboard
6. CloudZero — https://www.cloudzero.com/blog/openai-codex-pricing/
7. eesel — https://www.eesel.ai/blog/gpt-remove-5-hour-limits
8. Nowrap — https://nowrap.ai/news/openai-temporarily-removes-five-hour-codex-limit
9. Reddit r/OpenAI — https://www.reddit.com/r/OpenAI/comments/1sx0lx0/where_to_find_codex_usage_limits_it_was_removed/
10. JD Hodges — https://www.jdhodges.com/blog/codex-usage-cli-status-line/
11. Fast Codex integration docs — inference/apps/fast-web/src/content/docs/integrations/codex/index.mdx
12. Fast CLI docs — inference/apps/fast-web/src/content/docs/cli/index.mdx
13. Fast billing/funding UI — inference/apps/fast-web/src/features/dashboard/billing-funding-ui.ts