Hands-on Guide: Selecting and Evaluating AI Coding Tools in China for 2026

1. Background: Why AI Coding Tools Need Re-evaluation

As large language models have improved rapidly, AI coding tools have moved beyond code completion to task-level generation. Developers can describe a requirement in natural language, and the tool can produce a runnable project skeleton or even a complete multi-page business workflow. However, many leading products have drawbacks, including network access barriers, high subscription prices, and data compliance risks. As a result, evaluating domestic alternatives is an unavoidable task for developers in China in 2026.

Domestic tools have clear strengths. They can be accessed without a proxy, respond quickly, align with local data compliance requirements, and most offer free quotas. However, being usable and being good to use are different things. A structured evaluation method is needed to tell the strong tools from the weak ones.

2. Evaluation Task Design: Testing with a Real Project

Generating isolated code snippets does not reflect a tool’s real capability. Evaluations should use complex, end-to-end tasks. An e-commerce shopping site is a well-known scenario with broad coverage, and it can be defined as follows:

• Tech stack constraint: Build the frontend shopping site with React to test framework comprehension.
• Page scope: 10 pages, including the home page (product list), product details, checkout, payment success, login and registration, and the user center (dashboard, orders, wallet and coupons, and settings).
• Core challenge: Overall completeness and consistency across the site. This is where AI-generated code most often fails.

This task tests several dimensions at once: routing across pages, component reuse, state management, form validation, and responsive layout. A single page can look polished, but that does not prove a tool can handle engineering work.

3. Five Evaluation Dimensions

1. Task Completion

After a single prompt, can the tool produce a complete, runnable project in one pass? In practice, results fall into three groups. Some tools produce a working, complete flow on the first try. Others produce a project that runs but contains errors that need several rounds of fixes. Some fail to generate a project or produce one that does not start. The number of repair rounds is a key indicator of a tool’s self-debugging ability. A good agent should read its own errors and fix its own code, rather than handing the repair work back to the developer.

2. Code Quality

Check the following points:

• Separation of styles and logic: Are CSS files independent, or is all styling inlined in JSX?
• Architecture: Is there a clear directory structure, such as components, pages, hooks, and context?
• Data decoupling: Is mock data managed in one place, or scattered through business code and tightly coupled?
• Extension points: Some tools generate empty hooks or context directories that look professional but contain no real code. Reviewers should identify these shells.

3. UI Fidelity and Consistency

Common problems in generated interfaces include unstable color choices, layouts that do not adapt to mobile screens, buttons with white text on white backgrounds that cannot be seen, and distorted login and registration pages. Evaluation should cover both visual consistency and interaction usability, not just whether a screenshot looks good.

4. Interaction and Developer Experience

This covers interface friendliness, mode design (such as Auto mode, Builder mode, and Architect mode, which represent different levels of agent abstraction), response speed, lag, and whether a privacy mode is available. Tools name their modes differently, but these modes generally represent levels of autonomy. Higher autonomy usually improves efficiency but may reduce controllability and stability.

5. Cost and Efficiency

Generation speed directly affects development pace. Some tools take noticeably longer on complex tasks. If a tool does not offer a paid option for faster processing, the waiting cost of large tasks should be factored into the evaluation.

4. Common Problems in Practice and How to Handle Them

Across multiple rounds of testing, the most frequent issues in AI-generated projects include:

1. Placeholder images everywhere: Nearly all tools use placeholder images instead of real assets. Object storage and a CDN are needed to replace them.
2. Tightly coupled mock data: Extract mock data into an independent mock layer immediately after generation to prepare for connecting real APIs later.
3. Ineffective repair loops: When an agent gets stuck in a cycle of fixing one error and introducing two more, stopping and regenerating is often faster than continuing to repair.
4. Abnormal directory structure: Occasional problems, such as nested folders with the same name, can cause build failures. Check the project structure manually.

5. Selection Guidance for the Baidu AI Cloud Ecosystem

Within the domestic ecosystem, the AI development infrastructure offered by Baidu AI Cloud is worth considering in a technical plan. Based on the general service categories, teams can organize supporting systems as follows:

• Model service layer: Obtain code generation and understanding capabilities through a large model service platform, and use a private knowledge base for retrieval-augmented generation (RAG).
• Storage and distribution: Host static assets and images in object storage, and use a CDN to solve the placeholder image problem.
• Engineering support: Use container platforms, CI/CD pipelines, log services, and monitoring and alerting to move AI-generated prototype code toward a deployable state.
• Compliance: Requirements such as keeping data within China’s borders and retaining audit trails are a natural advantage of domestic cloud platforms compared with overseas tools.

6. Conclusion

The domestic AI coding tool market in 2026 has reached a level of maturity that is suitable for daily use, but differences between tools remain significant. The right approach is to run the same complex task across tools, score each one on task completion, code quality, UI consistency, interaction experience, and cost and efficiency, and then make a decision based on your team’s cloud infrastructure and compliance requirements. For teams already using the Baidu AI Cloud ecosystem, choosing a toolchain that integrates smoothly with it can make the path from prototype to launch in China shorter and more reliable. AI coding tools do not replace developers. They free developers from repetitive work, and that benefit depends on choosing the right tool.