Evaluating a Chinese LLM’s Front-End Code Generation: How Far Is It From Usable to Actually Good?

1. Background: Code Generation as a New Competitive Frontier for LLMs

As large language model technology evolves rapidly, code generation has become one of the core capabilities for measuring a model’s practical value. In front-end development especially, the ability to generate a runnable project end-to-end from a requirements document is redefining developer workflows. Recently, a Chinese LLM released a new version claiming its code generation capability is now on par with leading international models. This sparked widespread discussion: How do Chinese models actually perform in real engineering scenarios? How far are they from being truly “ready to use out of the box”?

This article presents a complete hands-on test of the model’s code generation capability based on a real front-end project requirement. It analyzes the model’s strengths and weaknesses from an engineering perspective and explores the development direction of Chinese LLMs in the code generation space.

2. Testing Methodology: Based on a Real Project

2.1 Test Task Design

The test used a typical mid-to-back-office admin dashboard front-end requirement, covering three core pages:

• Login page: Required carousel display, form validation, and login state management
• Product listing page: Required multi-condition filtering, pagination, sorting, and data display
• Form page: Required field linkage, validation rules, and submission logic

Requirements were provided as a natural language document with no specified tech stack—the model chose its own.

2.2 Evaluation Dimensions

Evaluation was conducted across four dimensions:

Dimension	Description
Functional completeness	Whether all functionality described in the document is covered
Comprehension accuracy	How faithfully filter conditions, field relationships, and validation logic are reproduced
Code runnability	Whether the generated project can be directly accessed and run
Generation efficiency	Time from requirement submission to completed generation

3. Test Results: Clear Performance Stratification

3.1 Overall Conclusions

The generation process took over half an hour, during which there were waits due to resource queuing. The output provided an accessible project URL rather than just code snippets—a point worth noting for engineering usability.

Overall, the model exhibited a distinct pattern of stratified capability: strong at scaffolding page structures, but with clear shortcomings in understanding and reproducing complex interaction logic. In one sentence: engineering usability takes priority over visual polish.

3.2 Login Page: Relatively High Completeness

The login page was the best-performing part of this test. The carousel requirement mentioned in the document was correctly implemented, and the page layout, form elements, and basic validation logic were all fully presented. The overall quality was at a “minor adjustments needed before deployment” level.

This indicates that the model has solid generation capability for pages with clear structure and linear logic.

3.3 Listing Page: Comprehension Begins to Degrade

Problems began to surface on the product listing page. The filter conditions and interaction logic explicitly described in the document were not fully implemented in the generated output. The page structure itself was correct, but filtering functionality was missing—and this issue appeared repeatedly across multiple listing pages, suggesting it was not an occasional error but rather insufficient comprehension of complex conditional logic.

// Example of the filtering logic required in the document (not implemented by the model)
const filteredData = data.filter(item => {
  return (!params.category || item.category === params.category)
    && (!params.priceRange || (item.price >= params.priceRange[0] && item.price <= params.priceRange[1]))
    && (!params.keyword || item.name.includes(params.keyword));
});

3.4 Form Page: Insufficient Detail Reproduction

The form page was runnable overall, but field linkage, validation rules, and interaction details deviated from the document’s description. Even though the requirements document was fairly clear, the model still showed weak ability to reproduce field relationships and validation logic in complex forms.

This reflects a deeper issue: when generating code, the model tends to apply common page templates rather than strictly following the requirements document for customized implementation.

3.5 Overall Style: Adequate but Unremarkable

In terms of UI style, the output was better than most prototyping tools but still fell short compared to directly using mature component library templates. The design leaned toward simple, engineering-oriented output—a “function-first” approach.

4. Comparison With a Leading International Model

For reference, the same set of requirements had previously been implemented by a leading international model. The comparison results are as follows:

Page	Chinese Model	Leading International Model
Login page	Comparable completeness	Comparable completeness
Listing page	Missing filter options	Complete understanding of filters and field structure
Form page	Insufficient field relationship reproduction	Accurate validation logic and interaction details
Generation speed	Slower	Approximately 3 minutes

The core gap is not about “whether pages can be generated” but about depth of comprehension of the requirements document. The leading international model has a clear advantage in reproduction accuracy for complex logic, accurately capturing field dependencies and validation trigger conditions.

5. Implications for Open-Source Attributes and AI Development

5.1 The Value of Open-Source Attributes

If this type of model is released as open source, its significance goes beyond “free to use”:

• Auditability: Developers can examine model behavior and understand its decision-making logic on specific tasks
• Customizability: Enterprises can fine-tune based on open-source weights to adapt to specific tech stacks and business standards
• Ecosystem co-building: The community can contribute evaluation benchmarks, prompt templates, and post-processing tools to accelerate capability iteration

5.2 Insights for AI Development

Several key directions can be distilled from this hands-on test:

First, code generation is moving from “snippet-level” to “project-level.” Models are no longer just completing functions—they are attempting to understand complete requirements and generate runnable projects. This places higher demands on context understanding and planning capabilities.

Second, depth of comprehension matters more than generation speed. At the current stage, differences in generation speed can be mitigated through engineering measures, but gaps in comprehension accuracy directly affect output quality. Models need to better parse conditional logic, field relationships, and interaction rules in requirements documents.

Third, engineering usability is the pragmatic goal for now. For most enterprise scenarios, “runnable, structurally correct, and usable with minor modifications” can already significantly improve efficiency. Pursuing “one-shot generation of perfect code” is not yet realistic, but “generating high-quality scaffolding” is achievable.

Fourth, evaluation systems need to keep up. There is currently a lack of standardized benchmarks for code generation quality, especially end-to-end evaluations targeting real project scenarios. Building public, reproducible evaluation sets helps objectively measure model capabilities and avoid being misled by marketing rhetoric.

6. Practical Recommendations: How to Make the Most of Code Generation Models

Based on this hands-on experience, here are recommendations for developers:

1. Structure your requirements document: Break requirements into page-level, component-level, and field-level descriptions to reduce ambiguity
2. Specify tech stack constraints: Designate framework, component library, and directory structure to reduce the model’s room for improvisation
3. Generate and verify step by step: Have the model generate the page skeleton first, then gradually add interaction logic
4. Establish post-processing workflows: Run linting, formatting, and unit test supplementation on generated code
5. Keep human review in the loop: Focus on checking filter logic, form validation, and edge case handling

7. Conclusion

Chinese LLMs have made significant progress in code generation, evolving from “unusable” to “usable” and, in some scenarios, “good to use”—the iteration speed is commendable. But the gap with top international models lies mainly in depth of comprehension of complex requirements and precision of detail reproduction.

Open-source attributes offer Chinese models a unique path to catch up: accelerating evaluation and optimization through community collaboration, building trust through transparency, and meeting enterprise needs through customizability. In the future, competition in code generation will not just be about model parameters—it will be a comprehensive contest of engineering capability, ecosystem building, and evaluation systems.

For developers, the most pragmatic attitude at this stage is: embrace the tools, but keep reviewing; leverage efficiency gains, but never compromise on code quality.