More compute. A different equation.

The price stayed. The allowance changed.

OpenAI now offers Pro 100, Pro 200 and Pro 500 at $100, $200 and $500 per month. New, non-grandfathered Pro 200 subscriptions have a lower included allowance. Eligible existing subscribers retain their previous allowance through October 29, 2026, then move to the lower allowance. [1]

Pro 500 includes Astra Ultrafast. Pro 200 does not gain it through grandfathering or extra credits. Pro is billed monthly; $6,000 is twelve monthly Pro 500 payments, not an annual billing offer. [1]

Check the account, not the headline.

The official tier page checked for this article does not establish the draft’s exact 20× / 10× / 25× quotas, 200-to-100 weekly message reduction, $2,500 transition credit, universal absence of rolling limits, or an 8× quota burn rate. We do not present those numbers as verified entitlements.

Check the current plan comparison and your account’s Usage view before budgeting. A model limit and a credit balance are different: extra credits only support eligible features. [1]

Buy back time you can actually use.

The upgrade costs an additional $300 each month. Our suggested decision rule: measure blocked working time, completed tasks, review effort and rework for a representative week. Upgrade when extra capacity or lower latency removes a real bottleneck.

At an illustrative value of $50 per productive hour, six genuinely recovered hours cover the extra $300. This is a planning example, not a promise of savings. Faster generation alone does not guarantee faster delivery.

Give Codex an outcome worth finishing.

These original prompts describe deliverables, constraints and evidence. Replace the placeholders with your actual repository context. Start a new task for a different objective; retain relevant decisions instead of repeatedly pasting the whole repository.

Implement [feature] for [user]. Success means [observable acceptance criteria]. Follow existing project conventions. Keep changes within [scope]. Resolve routine implementation choices and finish the work. Run relevant checks and report the result, changed files and any remaining blocker. Ask only when missing information materially changes the outcome.

Reproduce [failure] using [steps]. Identify its cause, implement a focused fix, and verify the failing behavior plus relevant regressions. Preserve [required behavior]. Report the evidence and any uncertainty.

Inspect [workflow] for repeated context loading, unnecessary tool calls and avoidable retries. Propose measurable improvements. Implement changes within [authorized scope], compare on [representative tasks], and report completion time, cost where available, and correctness.

A small configuration. A clear brief.

Codex reads user settings from ~/.codex/config.toml; project configuration can live in .codex/config.toml for trusted projects. The example below is a starting point, not a way to override plan limits. Confirm model availability in your account. [2]

Our recommendation: begin at medium effort, then compare lower or higher effort on the same real task. Use live search when freshness is necessary. Keep repository instructions short and applicable to the work; remove stale rules and duplicated skill guidance. [3]

model = "gpt-6-astra"
model_reasoning_effort = "medium"
web_search = "cached"

Count accepted work.

Track successful tasks, review minutes, retries and total spend together. Keep ChatGPT subscription allowances separate from API token billing. Evaluate API alternatives using actual input, output, caching and tool usage, not a subscription-to-token conversion.

For factory software, include a disconnected-device case, an invalid permission case and a recovery path in your acceptance criteria. Better generated code is useful only when the connected system behaves correctly.