Codex vs Claude Code vs Cursor: how to choose

I want my coding tools to earn their place every month.

After a few weeks with one tool, the shortcuts are familiar and a collection of instructions has grown around it. You know how it plans, how it asks questions, and where it puts the diff. Switching starts to feel like abandoning work you already paid for.

Then the provider changes the usage allowance, moves a model into a different pool, or introduces another subscription, and the workflow you liked has a different price. If you want to keep an agent without adding a monthly plan, I wrote how to use a coding agent without another subscription.

I use the Codex desktop app, and I also work with Cursor and Grok Bot. I have written about the Codex app and how I divide work between Grok Bot and Cursor Projects. I don’t want my projects to depend on keeping any particular subscription forever.

So I choose a tool for the work in front of me, keep the project understandable outside that tool, and reassess when something material changes. A model release alone isn’t a reason to rebuild the whole setup.

The three tools

“Claude versus Cursor” mixes two comparisons. Claude is a model family, and Claude Code is the agent product built around it. Cursor is an editor that can offer models from several providers. So before comparing, it helps to know what each product is, where it runs, and how you pay for it.

Codex

Codex is OpenAI’s coding agent. It reads your project, edits files, and runs commands. The desktop app puts the chat, a terminal, a browser and the diff in one window, and that’s where I use it most. There’s also the Codex CLI, which runs in a terminal or over SSH and can run from a script with codex exec, plus an IDE extension.

OpenAI’s configuration guidance describes shared configuration layers across the CLI, the extension and the desktop app, so preferences carry between them. Integrations can still differ from one surface to another.

When you sign in with ChatGPT, Codex uses your plan’s allowance. OpenAI’s pricing documentation says ChatGPT Work and Codex share usage, and that task complexity and model choice affect how fast you consume it. Signing in with an API key uses API billing instead, separate from the subscription.

Claude Code

Claude Code is Anthropic’s coding agent, and it runs Claude models. You can use it from a terminal, and it also has IDE, desktop, and web interfaces. Local sessions work on the files in your folder, and cloud sessions run in a remote environment that starts from your GitHub repository.

The usual way in is an eligible Claude subscription. You can also use Anthropic Console billing, which is separate from the subscription. Claude Code’s cost documentation explains the difference between subscription usage and API costs, and describes additional usage credits. In the CLI, /usage shows where your account stands.

Cursor

Cursor is a code editor based on VS Code, with an agent that can find files, edit them and run commands. You can write code yourself in the editor, or work from the Agents Window, which keeps the conversation next to your running app. It also has a CLI, and Cloud Agents that run remotely.

Cursor has a free tier with limited AI usage. Cursor’s pricing documentation separates its new subscriptions from legacy plans. New paid plans come with the X subscription and have a weekly pool for Cursor models, shared with Grok products. Third-party models use prepaid credit on eligible plans. So “I have Cursor” doesn’t tell you how a particular Claude or GPT request will be billed.

Where I would start

The products overlap enough that a feature checklist leaves you with three acceptable choices. I would use the interface I want to work in as the first filter, then check the rest against a real task.

If you spend most of the day editing functions, an editor makes sense. If most of your work is describing a change and reviewing the result, a dedicated agent interface may fit better. I wrote about that shift in What changes when you stop opening the code editor?.

For an existing project, I would test the least convenient part of the workflow early. Say the work needs a local database and a browser session where you’re already signed in. An agent that writes the change remotely still needs a way to verify that state, and I’d work out that handoff before giving it a large feature.

Model or tool?

When a tool does badly, I want to know which layer failed. Did the model misunderstand the requirement? Could the agent reach the files? Did a permission stop a necessary command? Did the interface make a bad change hard to notice?

The same model inside another application can behave differently, because the instructions, context selection and available tools differ. And the fixes are different. A larger plan won’t fix unclear requirements, and a different model won’t give a cloud machine access to something that exists only on my Mac.

Check your own account

Two plans with the same monthly price can support very different working days. The allowance can depend on the model, the context or the mode, some usage shares a pool with another product, and an older account can have legacy terms that differ from the offer shown to new customers.

I would check the pricing pages above and my own account before comparing offers. A screenshot from someone else’s account can describe a deal I can’t buy, or a limit I don’t have.

Measure a completed change

Token prices are useful when budgeting an API integration. For fixing a bug in an unfamiliar app, what I care about is how much of the work survives review.

Say I ask two agents to fix the same broken import flow. One finishes quickly, changes six files, and says the tests pass. The other takes longer, notices an invalid input case, and changes two files. If the first one needs another hour of correction, the faster response didn’t save me time.

I would compare a few representative tasks, each from the same clean starting point. A contained bug, a feature that touches existing behavior, and a question about the architecture show different strengths. The second agent shouldn’t inherit the first agent’s fix.

For each task I’d write down the outcome, how much I had to step in, and how long the review took. Failures stay in the notes, because restarting until I get a result I like hides part of the cost.

I also want to see the actual verification. “Tests passed” means the relevant tests, on the final code, not a green check from before the last edit.

Before looking at the patch, I’d write a few acceptance cases of my own. For the import bug, an empty file, a duplicate record, and a retry after an interrupted upload. Otherwise the implementation and its new tests can agree on behavior I never wanted.

When an agent misses a case, I’d note whether one precise correction is enough. Restating the same requirement three times costs more than answering a sensible question once, even if the final code passes in both cases.

This is a small evaluation of my work, with my preferences, not a ranking of the providers.

Interruptions have a cost too

An allowance that runs out in the middle of a debugging session can hurt more than a smaller one with predictable resets.

Part of the cost is the wait. The other part is resuming elsewhere: explaining the failing behavior, showing what changed, and recovering the assumptions buried in the conversation. If that only exists in chat, switching tools turns into an emergency documentation exercise.

So I’d rather leave the state in the repository as the task progresses: what’s broken, what I tried, what I decided, what’s left to check. Another agent can read that and continue without guessing from a half-finished diff, and so can I when I come back to the project a week later.

Keep the project runnable without the agent

A repository should explain how to install, run, and check the application. If the only reliable way to start it is asking the original chat to remember its setup, the project depends on that conversation.

The same applies to tests. A command I can run from a terminal gives every agent a common way to check the result, and lets me investigate a failure without waiting for an assistant to explain what it did.

I like ordinary scripts for repeated work. They can validate input, return a useful exit status, and print an error someone can act on. The instructions then point at the script instead of repeating a procedure that changes in five places. One short command that does one job reliably is enough.

For project instructions, AGENTS.md is a good starting point. Tools discover instruction files differently, though, so I would check each tool’s documentation rather than assume it loaded the file.

Portability still needs some adaptation

I don’t expect to copy a setup between applications unchanged. Tool permissions differ, and so do plugin systems, skill discovery, and the way an agent starts subprocesses. A remote session might lack a local credential or a browser login, and an integration in one product might have no equivalent in another.

What I can do is keep the essential project knowledge independent of those details. The build commands and the decision about how authentication works belong with the project. A setting that selects a model belongs in that provider’s configuration.

When I switch, I want to be adapting the connection to the project, not recovering the project’s purpose from an old conversation.

I would have a new agent prove the connection works with a small task. Ask it to explain the application, run one relevant check, and say where it would make a contained change. That tells me more than copying every configuration file and starting a migration.

Choose where the agent needs to work

Some tasks belong close to my computer. A local app bug may need the local build tools, test environment and files. A repository cleanup is easier to delegate remotely, if its setup and verification work there.

I would decide that before choosing a model, or I end up paying for excellent reasoning that can’t reach the thing it needs to inspect.

Then there’s how I want to review the result. I like keeping the changes and their explanation close together, one task at a time. Someone who works through a team review queue may prefer a different interface. That’s a reason to pick an environment, and it says nothing about which model is smarter.

A second tool should have a job

Keeping a backup can be useful. Paying for several subscriptions without knowing why is easy too.

I would give a second tool a specific role: reviewing a difficult change independently, reaching a workflow the primary tool can’t, or letting me continue during an outage. Then I can ask whether it does that job often enough to justify the cost.

An independent review needs an independent brief. If I paste the first agent’s confident explanation and ask another one to agree, I’ve biased the review. I would give it the requirement, the relevant code, and what needs checking.

I also don’t want to split one task across tools so often that managing the handoffs becomes the task. If the primary agent can finish a routine change and I can verify it, a second conversation adds nothing.

When I would switch

A price change deserves attention when it affects my actual workload. So do repeated incorrect changes, missing tools, or an interface that makes review harder than it needs to be.

First I’d check whether the problem is my setup. An expensive session may have loaded irrelevant files, a failing task may need a clearer boundary, and a permission problem may have a supported configuration fix.

Then I’d try a realistic task in the alternative, counting the work needed to use it. A chart of downloads or page visits doesn’t tell me whether an agent will fix my app’s import bug.

The comparison worth doing is one I can repeat against my own requirements, like an OpenCode vs Codex CLI run on one bug fix. For a new Cursor model such as Muse Wonder, I would run the same kind of small comparison.

What I would keep after cancelling

If I cancelled tomorrow, the code should still build and the tests should still run. The important decisions should be readable, and the unfinished work written down.

I would keep investing in that, because it lets me pick Codex, Claude Code or Cursor for today’s work, and try something else when the terms or the quality change.

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