A dark industrial terminal with a glowing red screen, suggesting a command-line coding session.
All three tools wrap a large language model around your codebase. The difference is where you sit while it works.

Claude Code, Cursor, and OpenAI Codex are three of the most widely used AI coding tools in 2026. Each pairs a frontier large language model with your repository so it can read files, write code, run commands, and complete multi-step tasks. They differ in where the work happens: Claude Code lives in the terminal, Cursor is a full code editor, and Codex spans a command-line tool, an editor extension, and a cloud agent. This page explains what each one is, how it works, what it costs, and when to choose it.

What each tool is

Claude Code is Anthropic’s agentic coding tool. It started as a command-line program that runs in your terminal, reads your codebase, edits files, and runs shell commands. It now also ships as a Visual Studio Code extension, a desktop app, and a browser surface, all built on the same underlying engine. Claude Code uses Anthropic’s Claude models and supports the Model Context Protocol, subagents, and hooks for extending its behaviour.

Cursor is an AI-native code editor built by Anysphere as a fork of Visual Studio Code. You write and edit code inside the editor itself, and the AI features sit alongside the file tree, the diff view, and the integrated terminal. Cursor does not lock you to one model. It can route a request to Anthropic, OpenAI, or Google models, and it also ships its own in-house model, Composer, tuned for low-latency edits and codebase-wide search.

OpenAI Codex is OpenAI’s coding agent. It is an umbrella over several surfaces that share one account and one underlying model: a terminal CLI (open source, Apache-2.0 licensed), extensions for VS Code and JetBrains IDEs, a cloud agent inside ChatGPT, a macOS desktop app, and a GitHub bot. The cloud agent provisions a sandboxed environment, clones your repository, and works a task while you do other things.

How each one works

The three tools follow the same broad loop: read the code, plan, edit, run, and check. Where they differ is the interface and how much runs locally versus in the cloud.

Step 1Read contextThe tool indexes or scans your repository to understand the code.
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Step 2PlanThe model breaks the task into steps, sometimes shown to you first.
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Step 3Edit and runIt changes files, runs tests, and executes shell commands.
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Step 4ReviewYou inspect the diff and approve, reject, or ask for changes.

Claude Code runs this loop locally against your working directory, with the terminal or the VS Code panel as the control surface. Cursor runs it inside the editor, so the diff and the file you are editing are the same view. Codex can run it locally through its CLI or extension, or hand the whole task to a cloud sandbox and return a pull request. This is the core trade-off: a local agent keeps your code on your machine and gives you tight control, while a cloud agent lets you delegate long-running work and run several tasks in parallel.

Side-by-side comparison

Claude CodeCursorOpenAI Codex
What it isAgentic coding tool from AnthropicAI-native code editor from AnysphereCoding agent from OpenAI
InterfaceTerminal CLI, VS Code extension, desktop, browserFull code editor (VS Code fork)CLI, IDE extension, cloud agent, desktop app, GitHub bot
Where work runsLocal working directoryLocal editorLocal or cloud sandbox
Model supportAnthropic Claude modelsMultiple: Claude, GPT, Gemini, own Composer modelOpenAI GPT models
ExtensibilityModel Context Protocol, subagents, hooksMCP, model switching per requestMCP, structured agent commands
Pricing modelFlat Claude subscription or pay-as-you-go APIFlat subscription with credit pool, or APIIncluded with ChatGPT plan, or pay-as-you-go API
Entry pricePro at €20 (about $20) per monthFree Hobby tier; Pro at €20 (about $20) per monthBundled with ChatGPT Plus from €20 (about $20) per month
Best forDeep multi-file work in the terminalEveryday coding with a visual editorDelegating background and parallel tasks

Prices are converted from US dollar list prices and rounded. Check each vendor’s pricing page for current figures and regional VAT.

Pricing in detail

Claude Code is billed through your Claude plan or an API account. The Pro plan is about $20 per month and suits a few hours of use per day. Max plans are $100 per month (5x Pro usage) and $200 per month (20x Pro usage). Plans use a token budget that refills on a fixed window with a weekly cap. Heavy teams can instead pay per token through the Anthropic API.

Cursor has six tiers. Hobby is free with limited completions and agent requests. Pro is about $20 per month and includes a monthly credit pool, frontier model access, and cloud agents. Pro+ is about $60 per month with more credits, and Ultra is about $200 per month for the heaviest individual use. Teams is about $40 per user per month with central billing and admin controls, and Enterprise is custom-priced.

Codex has no separate price for the assistant. Access is bundled with ChatGPT plans (Free, Plus, Pro, Business, Enterprise), with usage limits that scale by tier. The open-source Codex CLI can also authenticate against an API key, in which case you pay standard GPT token rates. OpenAI has indicated that heavy Codex use can average roughly $100 to $200 per developer per month, with wide variance by workload.

Strengths and weaknesses

Claude Code. Strengths: strong performance on multi-file reasoning, full and reliable use of its large context window, and deep terminal control with scripting, subagents, and hooks. It is the natural fit for engineers who live in the command line. Weaknesses: it is tied to Anthropic models, so you cannot switch providers, and the terminal-first design has a steeper learning curve than a graphical editor for newcomers.

Cursor. Strengths: a familiar editor experience, model flexibility (you can pick Claude, GPT, Gemini, or Composer per request), and codebase-wide semantic search built in. It lowers the barrier for developers who want AI inside an IDE they already know. Weaknesses: the credit-pool pricing can make heavy agent use expensive and harder to predict, and routing across many models means behaviour can vary depending on which model you select.

Codex. Strengths: multiple surfaces under one account, an open-source CLI, and a cloud agent that runs long tasks and parallel work without tying up your machine. Token efficiency on equivalent tasks can keep API bills lower. Weaknesses: it is limited to OpenAI models, and the spread of surfaces (CLI, IDE, cloud, app, GitHub) can be confusing until you settle on one workflow.

When to use which

Choose Claude Code when your work is deep, multi-file editing and you are comfortable in the terminal. It rewards engineers who want to script the agent, isolate context with subagents, and keep the whole loop on their own machine.

Choose Cursor when you want AI assistance inside a full editor and value the ability to switch between models. It suits day-to-day coding where you stay in one window and want autocomplete, refactors, and an agent in the same view.

Choose Codex when you want to delegate work to a cloud agent or run several tasks in parallel, especially if your team already pays for ChatGPT. It fits background tasks, automated pull requests, and workflows that hand off between local and cloud surfaces.

Many teams use more than one. A common pattern is Cursor for interactive editing, a terminal agent for heavy refactors, and a cloud agent for queued background tasks. All three are AI agents that run agentic loops over your code, so the underlying mechanics are similar even when the surfaces differ.

Further reading

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