In September 2025, our team began using ByteDance’s Trae AI programming tool for everything from front-end page development to back-end interface writing and small project setup, utilizing it for a full 8 months. During this time, we witnessed Trae’s rapid iteration and experienced the convenience and advantages of a domestic AI programming tool. However, in May 2026, we ultimately decided to switch to OpenAI’s Codex.

Many may wonder why we made this choice: Trae is free, user-friendly in Chinese, and has fast domestic access. Why switch to the more expensive, English-only Codex? Today, we will objectively analyze the core differences, applicable scenarios, and our real usage data from the past 8 months to help you avoid pitfalls in choosing the right AI programming tool.

1. Honest Review: Satisfied with Trae After 8 Months

When we initially chose Trae, we were drawn to its domestic origin, free access, excellent Chinese support, and integrated IDE. Over the past 8 months, it has indeed solved many practical problems, significantly improving our efficiency.

1. Zero Barrier to Entry, Comfortable Chinese Development
   Trae is an AI-native IDE developed by ByteDance, modified from VS Code, requiring no extra plugins. The most noticeable advantage is its precise understanding of Chinese. Whether writing requirements, comments, or describing error issues in Chinese, it comprehends instantly without needing to translate commands repeatedly.
   For instance, when developing a back-end management system, we simply input “Create a back-end page with login permissions, data table pagination, and add/edit pop-ups based on Vue3 + Element Plus,” and Trae’s Builder mode can directly generate the complete project structure, including code, dependencies, and configurations, producing a runnable prototype in just 5 minutes. For developers in our team who primarily use Chinese and have average English skills, this experience is far superior to using purely English tools.

2. Free Enough for Small Projects, Maximizing Efficiency
   The personal version of Trae is completely free, with no limits on auto-completion and a free quota for cloud tasks, making it particularly friendly for individual developers and small teams. We often write business logic, debug simple bugs, and generate basic components, and Trae’s performance is entirely sufficient, with fast code generation and smooth local operation without the hassle of environment configuration.
   Additionally, its Chat mode is very practical; selecting code to ask about logic, dragging in error messages for repair suggestions, or requesting code simplification all receive quick responses, effectively addressing daily development pain points.

3. Stable Domestic Access, No Network Lag
   As a domestic tool, Trae’s servers are located in China, providing fast access and low latency without the need for VPNs or proxies. Compared to early experiences with overseas tools, which often suffered from frequent lags, timeouts, and slow responses, Trae’s network experience has been excellent, which was a key reason for our initial choice.

4. 8 Months of Data: 50% Efficiency Improvement in Small Projects
   We compiled usage data over 8 months: the development time for small projects (under 1000 lines of code) was reduced by 50%, with basic components and repetitive logic not needing to be handwritten; the efficiency of fixing simple bugs improved by 60%, eliminating the need to search documentation or solutions, as AI provided the repair code directly; and the onboarding speed for beginners improved by 40%, allowing zero-based developers to quickly generate projects and lower the programming barrier.
   In summary, for small projects, Chinese scenarios, and basic development, Trae is almost a “perfect tool,” and we are genuinely satisfied with our experience over these 8 months.

2. Turning Point: 3 Core Pain Points Trae Couldn’t Handle

Given Trae’s advantages, why did we decide to switch? The core reason lies in the increasing scale and complexity of our projects, along with higher engineering requirements, which made Trae’s shortcomings increasingly apparent, ultimately affecting development efficiency and project quality. These three pain points were key to our switch.

1. Insufficient Capability for Complex Logic, Failing in Deep Projects
   Trae is based on ByteDance’s self-developed Doubao-Seed-2.0-Code model, performing well with simple logic, CRUD operations, and basic components. However, when faced with complex algorithms, multi-module dependencies, architectural design, and large project refactoring, its capabilities were clearly inadequate.
   Last December, we initiated a medium-sized back-end project (50,000 lines of code) involving multiple service calls, database sharding, and cache strategy design. While developing with Trae, we encountered numerous problems:

   • Cross-file dependency understanding was inaccurate, leading to frequent errors when modifying one module that affected related modules;
   • Complex algorithm generation logic was chaotic; for core logic like order settlement and inventory deduction, the generated code had many loopholes requiring manual rewriting;
   • Weak architectural design capability, only able to generate basic structures without providing reasonable layering or decoupling solutions, resulting in high maintenance costs later.
     In short, while Trae is a “magic tool” for small projects, for medium to large complex projects, it can only serve as an “assistant,” with core logic still relying on manual input, ultimately reducing efficiency.

2. Weak Long Task Execution, Unable to Operate Independently
   The current core trend in AI programming tools is the Agent mode (autonomous execution of long tasks), where the AI can independently break down tasks, write code, run tests, and fix bugs without human supervision. However, Trae lags significantly in this area.
   Although Trae’s SOLO mode claims to support autonomous development, in practice, longer tasks often get interrupted, logic goes awry, and frequent interventions are needed. For example, when tasked with “building a quantitative backtesting system from scratch, including data fetching, strategy writing, backtesting execution, and result analysis,” Trae could only generate basic scripts, and when errors occurred, it would get stuck without self-diagnosing or correcting the code, requiring step-by-step guidance from us, essentially remaining “human-led, AI-assisted.”
   In contrast, Codex’s Agent mode can run long tasks for several hours, autonomously identifying issues and correcting code without human intervention. This represents a significant difference in complex project development, directly impacting efficiency.

3. Insufficient Ecosystem and Engineering, Limiting Team Collaboration
   Trae’s ecosystem is still in its infancy, with low integration with mainstream development tools and team collaboration tools, making it unsuitable for medium to large team collaboration.

   • It cannot deeply integrate with Git or GitHub, requiring manual operations for code submission, PR reviews, and issue management without automated processes;
   • It does not support parallel work with multiple Agents, limiting the ability to handle front-end, back-end, and database modules simultaneously, forcing serial development;
   • Poor adaptability to team standards, lacking the ability to customize coding and submission standards, resulting in inconsistent code styles and high review costs later.
     In contrast, Codex, backed by OpenAI and the ChatGPT ecosystem, deeply integrates with GitHub, supports parallel work with multiple Agents, and allows customization of team standards, automatically handling PR reviews, issue classification, and code reviews, fully meeting the engineering needs of medium to large teams.

3. In-Depth Comparison: Trae vs Codex, Core Differences at a Glance

To provide a clearer view of the differences between the two, we will comprehensively compare them across six core dimensions: core positioning, model capabilities, applicable scenarios, pricing, ecosystem, and Agent capabilities, based on our 8 months of practical data. After reading, you’ll know which tool to choose.

1. Core Positioning: Lightweight IDE vs Engineering Agent

   • Trae: A self-developed AI-native IDE by ByteDance, focusing on lightweight, Chinese-friendly, and quick onboarding, akin to an “enhanced version of VS Code,” suitable for individual developers, small teams, Chinese scenarios, and small projects.
   • Codex: An AI programming Agent introduced by OpenAI, based on the GPT-5.4/GPT-5.5 model, focusing on engineering, complex tasks, long-chain execution, and team collaboration, resembling a “professional back-end engineer,” suitable for medium to large teams, complex projects, and English scenarios.

2. Model Capability: Strong in Chinese, Weak in Complexity vs Strong in English, Strong in Engineering

   • Trae: Advantages include precise understanding of Chinese, fast generation speed, and high quality in small logic; shortcomings include weak long-text context, poor complex algorithms, inadequate architectural capabilities, and limited language support.
   • Codex: Advantages include strong long-context understanding, precise complex algorithms, professional architectural design, mature multi-language support, and high code quality; shortcomings include average understanding of Chinese, purely English environment, high onboarding difficulty, and the need for proxies for domestic access.

3. Applicable Scenarios: Small Projects in Chinese vs Large Projects in Engineering

   • Trae is suitable for: Individual practice, small projects (≤10,000 lines of code), front-end page development, simple back-end interfaces, Chinese requirements, beginner entry, and basic completion/debugging.
   • Codex is suitable for: Medium to large projects (≥30,000 lines of code), complex algorithm development, architectural design, large-scale refactoring, team collaboration, engineering processes, and autonomous execution of long tasks.

4. Pricing: Free and Friendly vs Paid and Professional

   • Trae: The personal version is permanently free, with unlimited auto-completion and two free cloud tasks; the enterprise version is paid, but affordable (Lite version at $3/month, Pro version at $10/month).
   • Codex: ChatGPT Pro users can use it directly at $25/month; the team version is more expensive, charged per account, resulting in higher costs.

5. Ecosystem: Domestic Independent vs Global Top-Tier

   • Trae: A domestic ecosystem, integrating the Doubao model, with fast domestic access and rich Chinese documentation; however, it has few third-party tool integrations, a small community, limited plugins, and slow updates.
   • Codex: Backed by the ChatGPT and GitHub ecosystem, integrating all mainstream tools like Git, GitHub, VS Code, and JetBrains, with a large community, abundant plugins, and fast updates, supported by global developers.

6. Agent Capability: Weak Autonomy vs Strong Autonomy

   • Trae: The SOLO mode has weak autonomy, with long tasks prone to interruption, requiring frequent intervention, and can only handle simple autonomous tasks.
   • Codex: The Agent mode has strong autonomy, supporting goal workflows, allowing tasks to be paused/resumed, capable of writing code, running commands, analyzing results, and fixing bugs independently, running for several hours without supervision.

A summary table of core differences:

4. Transitioning to Codex: 1 Month of Testing, Efficiency Exceeds Expectations

After switching to Codex for a month, we migrated our previously challenging medium-sized back-end project to Codex, and the results exceeded our expectations, with significant improvements in both core efficiency and quality.

1. 40% Improvement in Complex Project Development Efficiency
   Previously, using Trae to develop a medium-sized back-end project required manual coding for core logic, leading to many bugs and prolonged debugging. A module that would take a month to complete with Trae was finished in just 20 days with Codex.

   • Cross-file dependency understanding was precise, with automatic adaptation of related modules when modifying one;
   • The quality of complex algorithm generation was high, with fewer loopholes in core logic like order settlement and inventory deduction, requiring only minor modifications to be usable;
   • Professional architectural design provided reasonable layering and decoupling solutions, significantly reducing future maintenance costs.

2. Agent Mode Frees Up Hands, Long Tasks Require No Supervision
   This was the most surprising aspect; Codex’s Agent mode truly operates autonomously. We tasked it with “building a user management system from scratch, including registration and login, permission control, data statistics, API documentation, and finally deploying to Docker,” and it required no supervision:

   • It autonomously broke down tasks: initializing the project → designing the database → writing APIs → writing the front end → writing documentation → configuring Docker;
   • It ran tests autonomously: after writing the code, it automatically ran it, diagnosed errors, and corrected them by itself;
   • It iterated and optimized autonomously: identifying logical loopholes and performance issues, optimizing automatically until the task was completed.
     The entire process lasted 3 hours without any human intervention, resulting in a directly deliverable runnable project—an experience that Trae could never provide.

3. Code Quality Improvement, Bug Rate Reduced by 50%
   The code generated by Codex is significantly more standardized, robust, and maintainable than that produced by Trae.

   • The code style is consistent and adheres to team standards, eliminating the need for manual adjustments;
   • Boundary handling is thorough, considering edge cases, exceptions, and concurrent scenarios, leading to a substantial reduction in bug rates;
   • Comments are clear, and logic is easy to understand, making future code reviews and maintenance much easier.

4. Smoother Team Collaboration, Engineering Process Closed Loop
   Codex’s deep integration with GitHub automates code submissions, PR reviews, issue classifications, and code reviews, greatly enhancing team collaboration efficiency.

   • After developers finish writing code, Codex automatically submits branches and generates PRs;
   • It automatically reviews code, checking for standards, vulnerabilities, and performance issues, providing modification suggestions;
   • It automatically classifies issues, assigning them to the corresponding developers and tracking progress, creating a fully automated closed-loop process.

5. Rational Summary: There’s No Best Tool, Only the Most Suitable Tool

After using Trae for 8 months and Codex for 1 month, our biggest takeaway is that AI programming tools are not absolutely good or bad; they are only suitable for your project scale, technical scenarios, and team needs.

Choose Trae if you meet these three points:

1. You are an individual developer or a small team with small project scales (≤10,000 lines of code), primarily focusing on front-end pages and simple CRUD interfaces;
2. You primarily use Chinese, have average English skills, do not want to deal with English environments and proxies, and seek quick onboarding, free access, and stability;
3. You are a beginner wanting to quickly generate projects and learn programming without getting bogged down by complex configurations and command lines.

Choose Codex if you meet these three points:

1. You are part of a medium to large team with large project scales (≥30,000 lines of code), involving complex algorithms, architectural design, and large-scale refactoring;
2. You have high engineering requirements, needing team collaboration, code standards, automated processes, and autonomous execution of long tasks;
3. You can accept an English environment and paid costs, pursuing code quality, development efficiency, and long-term maintainability.

Our switch to Codex was not due to Trae being inadequate, but because our projects grew, demands became more complex, and Trae’s capabilities could no longer meet our needs. For small teams, Chinese scenarios, and small projects, Trae remains the first choice; however, for medium to large complex projects, Codex’s engineering capabilities and Agent capabilities indeed provide qualitative improvements.

The iteration speed of AI programming tools is rapid; in the future, Trae may address its shortcomings, and Codex may lower its barriers and optimize Chinese support. What we need to do is not blindly follow trends in tool switching but to choose rationally based on our actual needs, allowing tools to truly serve development rather than being bound by them.

Which tool do you usually use, Trae or Codex? Or other AI programming tools? Which tool is more suitable for your project scale and technical scenarios? Feel free to share your experiences and selection advice in the comments section for discussion.