AI Coding Tools in 2026: Evaluating Chinese Alternatives for a React E-Commerce Build

1. Background: Why “Replacement” Is Not the Only Question

By 2026, intelligent coding tools are no longer just assistants that complete code. They are gradually entering a full pipeline of “task breakdown — code generation — engineering delivery.” The question facing developers is no longer simply “which tool is stronger,” but rather: under a given network environment, team tech stack, project complexity, and delivery timeline, which tool is better suited for stable, reliable work.

From this perspective, rather than debating whether a particular tool can “replace” an overseas product, it is more useful to first clarify what Chinese AI coding tools can and cannot do in practice. Chinese intelligent coding IDEs currently available on the domestic market generally share the following characteristics:

1. Model services are accessible without additional network configuration;
2. Deep editor integration, supporting conversational interaction and code generation;
3. Some level of task planning or Agent execution capability;
4. Generated output lands directly in the workspace as project files, components, and configuration.

These capabilities mean the tools can be used for real development tasks, not just demos or snippet completion. However, the differences beneath these commonalities are also significant. The main variations lie in task execution stability, the engineering structure of generated code, UI fidelity, modification cost, and the ability to understand complex requirements.

To observe these differences more accurately, this article uses a unified practical task: building a front-end shopping site with 10 pages from scratch using React. The task is not meant to demonstrate business innovation. Instead, it uses a fixed set of requirements to evaluate each tool’s performance on complete project delivery.

2. Task Design and Evaluation Criteria

2.1 Task Requirements

The target project is a front-end shopping site with the following 10 pages:

• Home page, also serving as the product list page;
• Product detail page;
• Product purchase page;
• Purchase success page;
• User login page;
• User registration page;
• User center dashboard;
• Orders page;
• Wallet/coupon card page;
• Settings page.

The tech stack is React. No UI library is specified, allowing each tool to independently decide component organization, styling approach, and state management.

2.2 Prompt Design

The prompt needed to clearly define the page scope while leaving room for each tool to make its own implementation choices. The following prompt was used for this task:

Use React to build a front-end shopping site. The site needs 10 pages:
Home page (product list page), product detail page, product purchase page,
purchase success page, user login page, user registration page,
user center dashboard, user center orders page,
user center wallet (coupon card page), and user center settings page.
Pages should have complete structure, basic interactions, and form validation,
with consistent styling.

This prompt deliberately does not specify a component library, routing solution, or image resource source. The goal is to observe the default decisions each tool makes without human intervention.

2.3 Evaluation Criteria

The evaluation focuses on six dimensions:

• Task completion: whether all pages are generated in one pass and whether there are obvious omissions;
• Runnability: whether the generated project can start directly or requires additional fixes;
• Page consistency: whether layout, color scheme, and navigation structure are unified;
• Interaction completeness: whether form validation, shopping flow, and page navigation form a closed loop;
• Code quality: whether component separation, style management, and mock data organization are clear;
• Modification cost: how much effort is needed to integrate real data or make adjustments later.

3. Performance Comparison of Mainstream Chinese AI Coding Tools

This article tested multiple intelligent coding tools currently available to developers in China. To avoid turning the article into product promotion, the tools are referred to as A, B, C, and D below, with only the capability differences and common issues observed during actual testing described.

3.1 Tool A: Strong UI Experience and Task Closure

Tool A’s overall interaction design is user-friendly, and its conversational interface does not feel as rigid as traditional development tools. In its task execution mode, it was able to generate the entire shopping site in one pass, with stable service response throughout the process and no noticeable lag.

The generated result shows a high completion rate. All 10 pages were created, the shopping flow works end to end, and the login and registration pages include basic form validation. Visually, the site uses a consistent primary color, with a mobile-oriented style and some responsive handling. However, the login and registration pages show slight layout deformation on wide desktop screens.

In terms of code, styles and logic are separated, and the component structure is straightforward. But the project architecture is relatively lightweight. Mock data is tightly coupled with page components, meaning the data layer would need refactoring before integrating real APIs. Product images are all placeholders, and the detail pages have relatively low information density, making the output more suitable as a prototype or internal demo.

3.2 Tool B: Familiar Interface but Unstable Delivery

Tool B’s interface structure and interaction logic are similar to common overseas intelligent IDEs, and it offers a privacy mode. Its execution mode is also designed for full tasks. However, after the initial generation, although the project can start, several runtime errors remain and require multiple rounds of fixes before the full result can be viewed.

On the positive side, the form validation generated by this tool is relatively complete, and the product detail pages contain richer content than those from Tool A. The code includes pre-created directories for hooks, context, and similar structures, indicating some engineering awareness.

But the problems are also notable. The overall page design is fairly basic, with poor mobile adaptation. Some button text colors are too close to their background colors, hurting readability. Image resources are also placeholders, and the overall color scheme lacks unified planning. Most notably, the inference speed is slow — task wait times are significantly longer than with other tools, which affects development rhythm.

3.3 Tool C: Multiple Agent Modes Add Selection Overhead

Tool C offers multiple execution modes, but the differences between modes are not explained clearly enough in the interface, making it easy for users to feel confused when choosing.

One of its fast execution modes generates quickly, but the project structure is chaotic — it even creates duplicate directories with the same name inside folders, causing the project to fail to start. Another mode advertised as suitable for complex tasks eventually produced a runnable project, but runtime errors were frequent. After multiple repair attempts, the result still could not reach a usable state and was ultimately abandoned.

This outcome shows that offering multiple Agents or execution modes does not mean users can easily make the right choice. For average developers, too many modes can actually increase the cost of use and waste time as they switch back and forth between modes.

3.4 Tool D: Partially Runnable but Limited Overall Completion

Tool D can also generate a React project with multiple pages, but it falls short in page count, interaction depth, and visual consistency. Some pages only have routing placeholders, with highly simplified internal content. Although the project can start, it is still far from the goal of a “complete shopping site.”

This result reminds developers that a tool’s ability to generate page skeletons does not mean it has understood the full business flow. For a project with 10 pages, business closure matters more than the page count.

4. Common Observations from the Tests

4.1 A Clear Gap Remains Between “It Runs” and “It Is Production-Ready”

Almost all tools can generate a substantial number of project files in a short time. But whether the project is maintainable, extensible, and easy to connect to real data varies greatly. For prototype validation, rapid generation is sufficient. For real delivery, manual refactoring and business completion are still required.

4.2 Visual Consistency Is a Common Weakness

Tools generally do well at unifying the style of a single type of page. But when different scenarios are involved — login, registration, user center — the layout, spacing, button styles, and colors tend to drift. Mobile adaptation, in particular, is often handled only at a basic level, and the details rarely meet production standards.

4.3 Mock Data Management Is Overly Simple

Most generated projects embed mock data directly in component or page files, lacking a separate data layer and request layer. This approach is acceptable for demos, but replacing mock data with real APIs requires substantial code changes. A better approach is to explicitly require in the prompt that “mock data should be centrally managed and provide a replaceable data access layer.”

4.4 Prompt Quality Has a Significant Impact on Results

The same tool can perform completely differently under different prompts. Clear page boundaries, interaction requirements, data constraints, and tech stack limitations can significantly reduce ambiguity in generated output. For example, explicitly requiring that “all buttons must maintain sufficient contrast between text and background,” “all product images must use uniformly sized placeholders,” and “forms must include required field validation” can preempt common problems.

5. Practical Recommendations

5.1 Treat AI Coding Tools as “First-Draft Generators”

In projects with clear requirement boundaries and relatively simple interaction logic, AI coding tools can dramatically improve the efficiency of going from 0 to 1. However, developers still need to handle architecture review, code review, bug fixing, and experience optimization. Positioning these tools as first-draft generators is more realistic than treating them as complete delivery solutions.

5.2 Adopt a “Step-by-Step Generation + Manual Confirmation” Workflow

Rather than asking for ten pages in one shot, break the task into stages:

• Phase 1: project skeleton, routing, and layout;
• Phase 2: core shopping flow pages;
• Phase 3: user center pages;
• Phase 4: unified visual design, validation rules, and responsive handling.

Review the output immediately after each phase before moving to the next. This workflow requires more interactions but significantly reduces rework later.

5.3 State Engineering Constraints Explicitly in Prompts

For React projects, consider adding the following to your prompt:

Organize components into page-level and shared component directories;
Place all mock data in a dedicated mock directory and access it through service functions;
Use unified design variables for colors and spacing;
Ensure all pages work on both mobile and desktop;
Add complete required-field and format validation to login and registration forms.

These constraints do not depend on any specific product capability, but they can clearly improve the engineering readability of generated projects.

5.4 Build a Reusable Evaluation Checklist

Teams can formalize the six evaluation dimensions above into an internal acceptance template. Whenever testing a new AI coding tool or model version, use a unified task and checklist for horizontal comparison to reduce bias caused by individual subjective judgment.

6. Conclusion

By 2026, AI coding tools available in China have made clear progress in task execution capability. Some tools can already generate multi-page projects from scratch. However, real-world testing shows that output quality still depends heavily on task complexity, prompt quality, and the amount of human follow-up work. For developers, the more pragmatic strategy is not to search for a perfect replacement, but to establish a collaborative workflow of “tool generation — human review — iterative repair” that allows AI coding tools to deliver maximum value within controlled boundaries. As underlying model capabilities and engineering maturity continue to evolve, the distance between these tools and real project delivery is likely to shrink further.