Getting Started with Codex: Four Product Forms Explained & How to Choose Between Cursor, Claude Code, and Codex

AI Coding Tools Are Evolving Fast — What Makes Codex Stand Out?

As large language models continue to improve, AI-assisted coding tools are iterating at a remarkable pace. From early editor plugins to today's standalone desktop applications, OpenAI's Codex has undergone a significant transformation in product form.

The evolution of AI-assisted coding tools has gone through several key stages. The earliest form was simple code completion plugins like GitHub Copilot (launched in 2021), which was built on OpenAI's early Codex model and could autocomplete code in real time within an editor. Then, with the leap in capabilities brought by models like GPT-4, AI coding tools evolved from "completing a few lines of code" to "understanding entire project contexts and generating complete feature modules." Since 2024, the field has entered the "Agent era" — AI is no longer just passively responding to developer instructions but can autonomously plan tasks, read and write files, execute commands, and even proactively discover and fix bugs.

The technical core of this "Agent" approach is a task planning and tool-calling framework built on top of large language models. Mainstream implementation paradigms include ReAct (Reasoning + Acting) and Tool Use — they give AI the closed-loop capability of "observe environment → reason and analyze → take action → get feedback → adjust strategy." Unlike traditional single-turn Q&A, an Agent can automatically break down a complex request like "implement a user login feature" into multiple steps: analyzing the existing code structure, creating route files, writing database models, generating frontend forms, running tests and fixing errors — with each step capable of invoking external tools like the file system, terminal commands, and package managers. This autonomy has evolved AI coding tools from "smart autocomplete assistants" to "virtual developers." The launch of the Codex standalone app is a direct product of this trend: rather than being attached to a specific IDE, it runs independently as an intelligent development assistant with full Agent capabilities.

This article is based on a hands-on Codex tutorial from a Bilibili content creator, systematically covering Codex's four core product forms, a comparative analysis with Claude Code and Cursor, and how different types of developers should make their selection decisions.

For developers who are just getting started or want to systematically master AI coding tools, understanding both "which tool to use" and "how to use it well" is equally important. This article will help you navigate the crowded tool landscape and avoid unnecessary detours.

The Four Forms of Codex: Why the App Is the Ultimate Form

According to the tutorial, Codex currently comes in four forms, each targeting different use cases:

• CLI (Command Line Terminal): Invoked via command line, ideal for developers who prefer terminal-based workflows. CLI (Command Line Interface) is one of the most fundamental ways developers interact with operating systems. In the context of AI coding tools, the CLI form means developers invoke AI capabilities by typing natural language instructions in the terminal, and the AI returns results, modifies files, or executes system commands directly in the command line. The advantage of CLI is that it's lightweight, flexible, and integrates seamlessly with Shell scripts, Git, and other development toolchains. The downside is also obvious — the lack of a visual interface makes it less ideal for frontend developers who need to frequently preview UI effects.
• Web-based: Used directly in the browser with no installation required
• Plugin: Integrated into development tools like VS Code, fitting into existing workflows
• App (Desktop Application): The latest standalone application with the most complete feature set

Many developers have traditionally integrated AI coding capabilities via plugins in their IDEs. But with the launch of the Codex standalone app, the tutorial creator explicitly recommends prioritizing the app form, for a straightforward reason: among all four forms, the app is undoubtedly the most powerful.

There's a fundamental architectural difference between the plugin form and the standalone app form. VS Code's plugin system runs on the Extension API, with each plugin executing in a restricted sandbox environment — it can only access specific interfaces exposed by the IDE (such as editor content, terminal, file tree, etc.) and cannot break through the host platform's permission boundaries. For example, plugins cannot freely create independent windows, cannot directly access OS-level file monitoring capabilities, and cannot run computation-intensive tasks in the background for extended periods. Additionally, a plugin's UI space is strictly limited to the IDE's sidebar, panels, or inline decorators, making it impossible to deliver an immersive interactive experience.

A standalone app form (typically built on frameworks like Electron or Tauri), on the other hand, has full control over its runtime environment — it has its own process, complete OS-level permissions, custom window management, richer visual preview capabilities (such as an embedded browser for real-time frontend page rendering), and stability unaffected by IDE version updates. This is similar to how Figma evolved from a browser plugin into an independent design platform: when functional complexity exceeds the host platform's capacity, becoming standalone becomes inevitable. The Codex app was born under this same logic — Agent-based AI coding requires file monitoring, multi-task parallelism, real-time previews, terminal control, and other compound capabilities that far exceed what an IDE plugin can handle.

You might not have noticed, but the Codex app doesn't reject traditional workflows. It can work alongside IDEs like VS Code, preserving the familiar coding environment while leveraging the app's more complete AI-assisted capabilities. This "standalone app + traditional IDE" combination is becoming an increasingly popular and efficient development model.

Codex vs. Claude Code: Which Wins on Price and Stability?

Among the many AI coding tools available, Codex is most frequently compared with Claude Code. The tutorial provides an intuitive comparison along two key dimensions: price and stability.

Price Comparison: Codex App Offers Better Value

From a cost perspective, the Codex app form is cheaper, while Claude Code is relatively more expensive. For individual developers and budget-conscious teams, the price difference directly influences tool selection.

Stability Comparison: Codex Delivers a Smoother Experience

In terms of day-to-day usability, the creator points out several pain points with Claude Code that make it "quite frustrating to use": it's prone to rate limiting and account bans. In contrast, Codex performs better in terms of stability, with no noticeable rate limiting or account suspension issues.

The rate limiting and account ban issues with Claude Code actually reflect the "inference economics" dilemma facing current large model service providers. Every AI code generation request consumes substantial GPU compute — for a moderately complex programming task, the input context can reach 100,000-200,000 tokens (equivalent to the entire codebase of a mid-sized project), and the model needs additional thousands of tokens of inference computation to generate a response. Based on current H100 GPU market prices, a single complex inference request can cost between $0.50 and $2.00, and heavy users might initiate dozens or even hundreds of such requests per day.

Under Anthropic's (Claude's parent company) subscription pricing model (e.g., Claude Pro at $20/month), some heavy users actually consume far more resources than their subscription fees can cover, forcing the platform to throttle usage to control losses. This is fundamentally a "pricing model vs. usage pattern mismatch" — a fixed monthly fee cannot accommodate the highly variable usage patterns in AI coding scenarios. Account bans may be related to anti-abuse policies — some users trigger risk control mechanisms by sharing accounts, using automation scripts, or reselling API access for high-volume calls. Codex's better stability in this regard likely benefits from its more mature infrastructure scale (a larger GPU cluster that can absorb traffic spikes), more flexible tiered pricing strategies, and large-scale user service experience accumulated from ChatGPT.

However, regarding the "quality degradation" issue, the tutorial creator honestly acknowledges this is debatable — some users have reported declining output quality from Codex as well, but from the creator's personal use case, they haven't noticeably experienced it. Technically, so-called "quality degradation" may correspond to several scenarios: the provider routing requests to smaller models during peak hours (Model Routing), dynamic truncation of the context window, or inherent performance fluctuations of the model on certain task types. This is a point of contention that readers should verify based on their own experience.

Frontend → Codex, Backend → Claude Code? A Deep Dive into Their Strengths

Beyond price and stability, the creator shares an important finding from personal practice — the two AI coding tools have distinctly different capability focuses, which directly determines their ideal development scenarios.

Claude Code: Rigorous Logic, a Backend Development Powerhouse

Claude Code leans toward logical correctness, code structure, and functional implementation. As a result, many backend engineers prefer it. When business logic is complex and rigorous architectural design is needed, Claude Code's advantages are particularly evident.

Codex: UI Detail-Oriented, More Frontend-Friendly

In contrast, Codex focuses more on UI details, color coordination, animation effects, and polishing interaction prompts. This makes it more popular in frontend development and app interface development scenarios.

The root cause of this capability difference lies in fundamental differences in training data and optimization direction of the underlying models. A large language model's final performance is determined by three stages: pre-training, supervised fine-tuning (SFT), and reinforcement learning from human feedback (RLHF).

OpenAI's models may have been exposed to more high-quality code from the frontend ecosystem during training — including CSS style specifications, animation libraries (like Framer Motion, GSAP), component libraries (like Shadcn/UI, Radix), and design system documentation. More importantly, during the post-training phase, OpenAI may have conducted specialized preference alignment for UI generation visual quality: having human evaluators compare different UI generation results, selecting the visually superior versions as positive samples, thereby guiding the model to learn to pay attention to spacing, color schemes, interaction feedback, and other design details.

Anthropic's Claude model, on the other hand, is known for its logical reasoning capabilities. Anthropic's technical approach has emphasized model "reliability" and "honesty" from the very beginning — its Constitutional AI methodology requires the model to perform strict logical consistency checks when generating content. This technical DNA makes Claude more proficient at handling core backend development challenges: complex business logic branches, database query optimization (such as SQL performance tuning), concurrency handling (such as lock mechanisms and transaction isolation levels), and system design tasks requiring rigorous reasoning chains. In other words, Claude's strength is "ensuring every line of code is logically correct," while Codex's strength is "making the final product look and feel great."

As a frontend developer, the creator candidly admits that Codex suits their daily work better in terms of user experience. Of course, they also emphasize this is just a personal impression, not an absolute conclusion. Claude Code, with its stronger logical capabilities, also has a considerable base of loyal users. This "to each their own" attitude precisely reflects the current industry reality where no AI coding tool has achieved absolute dominance.

You Need to Master All Three: Cursor, Claude Code, and Codex

The tutorial offers a very pragmatic viewpoint: while AI coding tools are proliferating, only three truly qualify as the "Big Three" — Cursor, Claude Code, and Codex.

Cursor is an AI-native code editor developed by Anysphere. It's essentially a deeply customized fork of VS Code. The term "fork" means Cursor is built directly on VS Code's open-source codebase (Microsoft's Code OSS project), so it naturally inherits VS Code's entire ecosystem — the extension marketplace, keyboard shortcuts, themes, Settings Sync, and more can all be seamlessly migrated. But Cursor has made deep modifications under the hood:

• Codebase Indexing: Cursor performs semantic indexing of the entire project (using embedding vector-based retrieval), enabling the AI to understand the project's global code structure, dependency relationships, and naming conventions when answering questions or generating code — not just the currently open file.
• Multi-Model Switching: Supports freely switching between multiple models like GPT-4o, Claude Sonnet, Gemini, etc., allowing developers to choose the most suitable model for each task type.
• Composer Mode: Allows AI to modify multiple files simultaneously, enabling cross-file refactoring and feature development that goes beyond traditional file-by-file completion.
• Tab Smart Completion: Context-aware real-time code completion with extremely fast response times (typically under 100ms), using small specialized models to ensure low latency.

Cursor's positioning falls between plugins and standalone apps: more powerful than the Copilot plugin (because it can modify the editor itself), but not as detached from the editor environment as the Codex app. Its monthly fee is approximately $20 (Pro version), offering the most balanced experience for developers who want to "do everything within the editor" without changing their existing development habits. Cursor's valuation exceeded several billion dollars in 2024, demonstrating the market's strong endorsement of this product form.

The creator recommends that developers master all three tools rather than specializing in just one. The reasons are practical:

• Different company environments: Different companies may provide accounts for different tools
• Varying team requirements: Different project teams may mandate different AI tools
• Flexible switching is a core competency: Mastering multiple tools allows you to adapt seamlessly when switching environments

From a personal growth perspective, the creator shares their own tool migration journey: they initially used Cursor for over six months but "felt the pain every time they renewed their subscription"; they later switched to Claude Code and connected it to domestic models, which lowered costs but resulted in a subpar overall experience, and they didn't particularly like the terminal CLI interaction style; ultimately, Codex with its standalone app became the choice that best fit their workflow.

There's No One-Size-Fits-All Answer, But the Methodology Is Reusable

The biggest takeaway from this tutorial isn't that "Codex is definitely the best AI coding tool," but rather a clear, reusable selection methodology: evaluate comprehensively across four dimensions — price, stability, capability focus, and product form — then make your decision based on your development direction and work environment.

Here's a quick summary of the selection recommendations:

Dimension	Codex	Claude Code	Cursor
Price	Lower	Higher	Moderate
Stability	Good	Rate limiting risks	Good
Frontend Capability	★★★★★	★★★	★★★★
Backend Capability	★★★	★★★★★	★★★★
Recommended Form	App first	Primarily CLI	Editor integration

For frontend developers and less experienced newcomers, Codex offers a friendlier onboarding experience thanks to its attention to UI details and the app's ease of use. For logic-intensive backend scenarios, Claude Code remains a strong contender. Cursor is best suited for developers who want powerful AI capabilities within a familiar editor environment without switching their toolchain — it's the "minimum migration cost" option.

It's worth noting that these three tools are not mutually exclusive. Many efficient developers use a workflow like this: Cursor for everyday coding and quick completions, switching to Claude Code for deep conversations when encountering complex backend logic problems, and opening the Codex app when they need to build a frontend interface from scratch or polish the UI. This "combo" approach may well be the optimal practice for AI-assisted programming at the current stage.

Regardless of which tool you ultimately choose, the key to boosting programming efficiency is becoming truly proficient with it. Tools are just means to an end — real competitive advantage always comes from a developer's ability to master their tools.

Key Takeaways

• Codex comes in four forms — CLI, web, plugin, and app — with the standalone app being the most powerful since it's free from IDE sandbox limitations and has full system permissions
• Compared to Claude Code, Codex has advantages in price and stability, with less risk of rate limiting or account bans
• There's a clear capability split: Codex excels at UI details and frontend development, while Claude Code excels at logical reasoning and backend development
• Cursor, Claude Code, and Codex are the current Big Three of AI coding tools — developers are advised to master all three to handle different work environments
• Selection methodology: Evaluate comprehensively across four dimensions — price, stability, capability focus, and product form — and make decisions based on your development direction