Quick Answer
Codex pricing runs six tiers, from free to $200 a month, but the sticker price is not your real bill. OpenAI Codex pricing 2026 charges by the token, not the plan, a change that took effect in April. Plus is $20, Pro starts at $100, and everything past that depends on how many files you let the agent read.
Most Codex pricing guides hand you a price list and call it done. That is like pricing a taxi ride by the door handle. The meter is what matters, and OpenAI put a real one on Codex this year.
At CloudZero we spend our entire day untangling exactly this kind of bill for finance teams, so here is the version with the meter included.
OpenAI Codex pricing plans in 2026
| Plan | Price | What you get |
| Free | $0/month | Trial access, no card required |
| Go | $8/month | Light, occasional use |
| Plus | $20/month | The default for most developers |
| Pro 5x | $100/month | 5x Plus usage |
| Pro 20x | $200/month | 20x Plus usage |
| Business | Pay-as-you-go seats | Token billing, no fixed seat fee |
| Enterprise / Edu | Custom | Contact sales |
Plus and every tier above it include Codex on web, CLI, IDE extension, and iOS. Free and Go are scoped for lighter, occasional use rather than full daily-driver access. There is no standalone Codex subscription separate from ChatGPT. Codex pricing plans, sometimes searched as Codex plan pricing, differ mainly in headroom, not core functionality once you are on Plus or higher.
If you already pay for ChatGPT, Codex pricing through a Plus or Pro plan, you are not paying twice. Codex rides on the same subscription. Pro used to be a single $200 plan. OpenAI split it on April 9, 2026 into Pro 5x and Pro 20x, so heavy users are not subsidizing light ones anymore, and light users are not overpaying for headroom they never touch.
One more date worth flagging for anyone budgeting a rollout: as of June 24, 2026, OpenAI stopped offering new pay-as-you-go Codex-only seats to new Business signups. Existing seats keep working as before. Standard ChatGPT Business seats still include Codex within their usual plan limits either way.
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260 senior finance leaders (more than half CFOs) told us why the speed of seeing AI spend, not the size of it, separates who pulls ahead on AI from who gets burned.
What each plan is actually for
Free gets you limited trial access with no card required. Fine for kicking the tires, not for daily work.
Go, at $8 a month, is the plan most guides gloss over. It covers light use but does not unlock cloud task delegation, the background-agent feature that makes Codex genuinely useful for handing off work. For most developers, the $12 jump to Plus buys real functionality, not just a bigger number.
Plus, at $20 a month, is the practical default. Multiple independent trackers report the same range: roughly 10 to 60 cloud tasks per five-hour window, depending on task size and model. That is wide on purpose. A one-line fix and a full-repo refactor are not the same task, even if they are both “one task” on paper.
Pro 5x and Pro 20x exist because a single $200 plan used to serve both a solo developer running occasional deep sessions and a team running parallel automations around the clock. Splitting it let OpenAI price each group closer to what they actually use.
Business drops the fixed seat fee entirely for Codex-only access, useful for piloting Codex with a small group before deciding on broader rollout. Some teams search this as Codex team pricing, though OpenAI’s actual product name for this tier is Business. Enterprise and Edu, what most people mean by Codex enterprise pricing, replace fixed limits with a shared organization-wide credit pool, which tends to be more efficient than per-seat billing when usage is uneven across a large team.
Codex pro pricing rewards volume. If you are running Codex in parallel across several repos, 20x is often the cheaper option per task despite the higher sticker price.
What actually drives your bill
Here is the mechanic. On April 2, 2026, OpenAI moved Plus, Pro, and Business off per-message billing entirely. Legacy Enterprise, Edu, Health, and Gov plans followed on April 23. Every plan now runs on credits, and credits are just tokens wearing a costume.
Three token types feed the meter: input, cached input, and output. Cached input costs roughly a tenth of fresh input, which is the single biggest lever you have. Reuse context instead of reloading it, and your bill drops without changing a line of code.
Here is the GPT-5.5 rate card:
| Model | Input (credits/1M tokens) | Output (credits/1M tokens) | Cached input | Output multiplier |
| GPT-5.3-Codex | 43.75 | 350 | 4.375 (10% of input) | 8x |
| GPT-5.4 | 62.5 | 375 | 6.25 (10% of input) | 6x |
| GPT-5.5 | 125 | 750 | 12.5 (10% of input) | 6x |
The output multiplier is not one flat number across every model. GPT-5.3-Codex runs at 8x input, confirmed against its official API price ($1.75 input to $14 output). GPT-5.4 and GPT-5.5 both run at 6x, confirmed directly in OpenAI’s own GPT-5.5 launch announcement, which lists $5 per million input tokens and $30 per million output tokens for the API. Do not assume one ratio applies to a model you have not checked.
One credit works out to roughly four cents, confirmed by cross-referencing the GPT-5.3-Codex credit figure against its dollar-based API price. A typical task on GPT-5.5 burns 5 to 45 credits, straight from OpenAI’s own help documentation, which is why the same $20 plan can feel generous to one developer and stingy to another.
(Rate cards shift with every model release. Pull the live numbers from the Codex rate card before this goes to print.)
The official 5-to-45 credit range for a single GPT-5.5 task is itself a useful data point: that is roughly a 9x spread between the lightest fix and the heaviest refactor, straight from OpenAI’s own numbers, no extrapolation needed.
How to keep your Codex bill down
OpenAI publishes its own advice here, and it holds up. Five habits move the needle:
- Trim your prompts. Precise instructions with no dead weight use fewer input tokens per turn.
- Shrink AGENTS.md. Every token in that file loads on every single task. A bloated one taxes every request, not just the big ones.
- Limit active MCP servers. Each one adds context overhead. Disable what you are not using that session.
- Route routine work to GPT-5.4-mini. Save the flagship models for tasks that actually need the extra reasoning.
- Lean on cached input. Reusing context instead of reloading it costs roughly a tenth as much, the single biggest lever in the whole system.
None of this requires a new tool or a policy change. It is prompt hygiene, and it is the difference between a $100 month and a $200 one on the same plan.
What Codex actually costs one team
Numbers help more than tables. Say you run 15 developers on Pro 5x. Base subscription cost: $1,500 a month, done.
Now say a third of that team spends one sprint on a heavy migration, the kind that reads forty files before writing ten. That is output-heavy work, and output tokens cost six to eight times more than input tokens depending on the model. Those five developers can plausibly double or triple their normal usage that month.
The plan price never moves. The credits behind it do. This is exactly the gap between Codex price and Codex cost, and it is the gap that catches finance teams off guard every single time.
Codex API pricing
Codex api pricing skips the credit system entirely. Run Codex through your own API key and you pay OpenAI’s standard token rates, the same structure we broke down in our OpenAI API pricing guide, no subscription, no included allowance.
For GPT-5.3-Codex, that is $1.75 per million input tokens and $14.00 per million output tokens, a rate confirmed independently across OpenAI’s model documentation and third-party pricing trackers. A typical coding session lands around $0.50 to $2.00 at these rates. If you want the per-token math spelled out further, our OpenAI API cost per token breakdown walks through exactly how that bill adds up.
The math has a rough breakeven point, based on independent calculations rather than an OpenAI-published figure: somewhere under 10 to 40 sessions a month, the API tends to beat Plus, and under 50 to 200 sessions, it can beat Pro 5x. Above that, a flat subscription usually wins. Treat these as directional, not exact, since actual usage patterns vary widely.
The API route also drops some conveniences: no automatic GitHub code review, no Slack integration, and new models typically arrive here after ChatGPT subscribers get them.
Codex CLI pricing
Good news on this one: Codex CLI pricing does not exist as its own line item. The CLI is free software. What costs money is the plan behind it, whether that is Free, Plus, or Enterprise.
Run a heavy CLI session and a heavy web session in the same five-hour window, and they draw from the same pool. The CLI is not a side door around plan pricing. It is just another door into the same room.
Codex usage limits, without the headache
Limits reset every five hours, not monthly. What eats them fastest:
- Fast mode. Runs at 2.5x the standard credit rate on GPT-5.5 and 2x on GPT-5.4.
- Image generation. Burns through included limits three to five times faster than a normal turn.
- Bigger models on big tasks. More capability, more tokens, more credits, every time.
Hit the wall mid-task and Codex lets you finish that turn under fair use before cutting you off, a policy OpenAI states directly in its own Codex documentation. After that, Plus and Pro users can buy extra credits without upgrading plans. Codex usage limits are less a wall and more a parking meter that occasionally wants another coin.
One Plus user summed up the real-world experience on Reddit’s r/OpenAI: request counts run high, in the 30 to 150 per five-hour window range, and the user called the result “crazy good” for the time it saves. That tracks with what the official ranges suggest, wide allowances that flex hard based on task size.
Codex vs. Claude Code pricing
For anyone comparing the two directly: entry tiers land close – both start free and hit $20 at Plus. The meaningful difference is token behavior on the same task. Codex is optimized for background agent work across repos, reading many files before writing few. Claude Code tends to spend tokens differently on interactive, turn-by-turn sessions. For most teams the honest answer is that the sticker price is not the deciding factor – run both on a real task and compare the credit burn. See our full Claude Code pricing guide for the plan-by-plan breakdown. Plenty of teams also run Cursor alongside either one rather than choosing.
Why finance teams should care about a token meter
A subscription price is easy to budget. A per-token meter attached to an engineering team’s habits is not, and that gap is not hypothetical. In CloudZero’s 2026 AI ROI survey of 260 finance leaders, 46% called managing AI spend their most stressful responsibility. Tools like Codex, billed by behavior instead of by seat, are a big reason why.
This is the same problem we dig into in how much does AI actually cost, and it is exactly the gap covered in our guide to OpenAI cost optimization.
Codex is rarely the only token-billed tool on a team’s stack, and the biggest question we hear from finance leaders is not “what does Codex cost,” it is “what does everything cost, together, this month.”
See what your team is actually spending
Plan pricing is the easy part. What your team spends once Codex, Claude Code, and everything else are running side by side is a different question, and it is the one CloudZero answers.
CloudZero’s OpenAI integration does the actual work, not just the explaining. It meters and allocates OpenAI spend, Codex included, down to the model, feature, and customer level, not just a single line item on an invoice.
That gap is common: CloudZero’s own data shows only 32% of companies allocate even half their OpenAI bill to the teams or features that actually generated it.
Real deployments back this up. CloudZero’s OpenAI integration is live at a financial services company valued at $85B, a smart building technology company valued at $26B, and a writing software company valued at $13B, plus a digital media company with more than $35M in annual cloud spend that deployed the integration in minutes.
Three things that matter for a token-billed tool like Codex specifically:
- Cost attribution. Spend gets tied to the developer, repo, or project that generated it, so “why did the bill jump” has an actual answer instead of a guess.
- Anomaly detection. A sprint that burns triple the normal credits gets flagged while it is happening, not thirty days later on the invoice.
- GitHub, Jira, and Slack integrations. The team that caused a spend spike gets a notification in the tools they already use, not a spreadsheet finance has to chase down.
to see real AI spend in one place, run a free cloud cost assessment to find the leaks, or take the self-guided product tour if you would rather look around first.
Codex pricing FAQs
