Hands-On Test: Chinese AI Coding Tools in 2026 — Which Handles Complex Projects?

In the rapidly evolving landscape of AI coding tools, developers’ expectations for intelligent assistants have shifted from simple code completion to “full-pipeline project delivery.” Particularly in 2026, a wave of intelligent integrated development environments (IDEs) has emerged in the Chinese market that can be used without special network configurations. These tools aim to compete with mainstream international products and take on complex tasks ranging from requirements understanding to code generation. However, there is often a vast gap between “usable” and “good.” To put these Chinese tools to the test, I set a standard real-world assignment: building an e-commerce website from scratch using React, with 10 core pages including a homepage (product listing), product detail, purchase flow, payment success confirmation, user login and registration, personal dashboard, order management, wallet and coupons, and settings pages.

The four tools selected for this test were: an intelligent coding assistant from a cloud provider, an intelligent IDE known for its interaction design, a coding companion from a veteran search engine company, and a cloud development tool built on large model capabilities. To avoid brand identification, the following evaluation uses the codenames “Tool A” (friendly interface), “Tool B” (interaction-continuity type), “Tool C” (multi-mode type), and “Tool D” (architecture-oriented type). The core challenge of this test lies in the site’s completeness and overall visual consistency, which tests not only the AI’s code generation ability but also the depth of its understanding of business logic.

Tool A: Friendly Interface, but Monolithic Architecture

As the first test subject, “Tool A” delivered a pleasant startup experience. Its interface design abandoned the cold feel of traditional IDEs and introduced pet IP interaction elements, making the development process feel more approachable. In practice, the tool offers a “Craft” mode — a generation approach oriented toward one-shot task orchestration rather than line-by-line code suggestions. Impressively, its cloud compilation speed was extremely fast with virtually no waiting or lag, which is rare among similar domestic products.

Generated output and code analysis: The pages generated by Tool A leaned toward mobile-first adaptation overall. Although simple responsive handling was applied, noticeable element deformation occurred in the layout transitions on the user login and registration pages. The main shopping flow worked, including basic front-end form validation logic (such as non-empty checks and format validation), but the product detail page was somewhat rough — it displayed only basic information without multi-dimensional attribute descriptions. All image assets used placeholders, which is standard practice but detracts from the visual result.

A deeper look at the generated code structure: styles and JavaScript logic were separated, which is good for later maintenance. However, the overall project architecture was overly simple — there was no reasonable component hierarchy, and mock data was hardcoded directly inside business components, which would require significant refactoring when backend APIs are connected. For developers seeking rapid prototype validation, Tool A can get the job done; but for incubating medium-to-large projects, its architectural scalability falls short.

Tool B: Comprehensive Features, but Rough Around the Edges

The second tool, “Tool B,” closely mimics the interaction patterns of mainstream international AI IDEs and offers a “privacy mode,” which is appealing to enterprise users handling sensitive commercial code. Its flagship “Builder” mode aims to generate an entire project skeleton through conversation. On the first task run, the generated project could run but came with multiple runtime errors. It took several rounds of collaborative human-AI debugging to display properly, exposing weaknesses in exception handling and edge-case consideration.

Feature highlights and areas for improvement: The form validation system generated by this tool was quite thorough, covering not only required-field validation but also password strength indicators and email regex matching. The product detail page also had richer information dimensions. However, its “lopsided” nature was obvious: the layout was acceptable on desktop, but on mobile viewports, buttons exhibited white-on-white overlapping issues, images remained placeholders, and the overall color scheme lacked design sensibility — jokingly referred to by developers as “mysterious aesthetics.” These issues would require substantial effort in style overrides later.

From a code engineering perspective, the code quality generated by Tool B was above average. It also achieved separation of styles and logic, but the stylesheet was not split into a separate file — instead, it was injected through inline style blocks. More notably, its project structure reserved directories for Hooks, Context, and other state management concerns, but these were all empty shells. This suggests the AI has some architectural awareness but does not yet truly understand how to apply complex state management in practice. Additionally, the tool’s response speed was slow. For large-scale tasks, the user experience approached “waiting for a heavy computation on the free tier,” which somewhat offset its feature comprehensiveness.

Tool C: Many Modes, but Easy to Go Astray

Tool C offers an extremely rich set of Agent mode options, from the fast-executing “Zulu” mode to the “Architect” mode that claims to handle complex logic, giving developers tremendous choice. However, more modes also mean higher decision costs and greater outcome uncertainty. In testing, “Zulu” mode excelled in speed, but in a high-difficulty use case, it generated a disastrous directory structure — a folder nested with multiple subfolders of the same name, causing complete path resolution chaos and ultimately preventing the entire project from starting.

Mode switching and rescue attempts: After switching to “Architect” mode, the project finally became runnable, but console errors kept appearing. Over multiple rounds of issuing “fix this problem” instructions, each fix introduced new related errors, falling into a cycle of “robbing Peter to pay Paul.” Eventually, due to time cost considerations, I reluctantly gave up. This phenomenon reveals a common industry pain point: AI has limited long-range memory for context. As the number of project files increases, it struggles to precisely locate cross-file dependency changes. Therefore, for Tool C, developers are advised to use it only for single-file-level code generation; complex architectures still require human architectural design.

Deep Summary: Leveraging AI’s Strengths While Avoiding Its Weaknesses

After completing the full from-scratch real-world exercise described above, the following conclusions can be drawn: as of 2026, Chinese AI coding tools are capable of building small-to-medium business systems, but they are still far from “fully autonomous driving.”

• On completeness: No single tool can deliver zero-defect production-grade code in one interaction. Developers must treat AI as a “senior pair-programming partner,” not a “replacement.” After generation, code review is an indispensable step.
• On architecture design: Current AI still tends to generate insufficiently modularized “spaghetti code.” It is recommended to explicitly request specific design patterns or directory structures in prompts, for example: Please create the component library following this structure: components/, hooks/, services/, utils/. This can significantly improve output quality.
• On performance and visuals: UI adaptation is a weak point. If a project has extremely high visual requirements, it is advisable to have designers first output design tokens (such as theme colors and spacing scales) and then feed them to the AI as reference specifications.
• On cloud service ecosystems: Some tools have already integrated with object storage, serverless function computing, and other cloud services, but this test did not cover those. When choosing a tool, if an enterprise is deeply invested in a particular cloud provider, it is advisable to prioritize that provider’s IDE, as integration with underlying services is typically tighter, reducing deployment costs related to pushing and pulling code later.

In the future, when large models can better interact with admin dashboards and monitoring/alerting systems, and possess true “memory” capabilities, AI coding tools may truly complete the transformation from “novice” to “expert.” Until then, mastering best practices for human-AI collaboration is the developer’s core competitive advantage.