Code Generation Capabilities of Chinese LLMs: A Benchmark Analysis Based on a Real Frontend Project

1. Background: Code Generation as a Key Battleground for Large Language Models

As large language model (LLM) technology moves deeper into practical applications, code generation has become one of the core indicators of a model’s real-world value. Whether it is building frontend pages, orchestrating backend interfaces, or reconstructing complex business logic, developers increasingly expect models to genuinely understand requirements and turn them into runnable code. In recent months, Chinese models have continued to iterate on code generation capabilities, with some versions beginning to approach international mainstream models in public evaluations. This has sparked broader discussion about open-source attributes, technical approaches, and the significance of these advances for AI development.

This article does not evaluate or recommend any specific commercial product. Instead, it uses a real frontend project requirement as a sample to abstract the general technical problem of “LLM-based code generation.” It analyzes the results across completion quality, depth of understanding, and engineering usability, then explores the broader implications of improved open-source model capabilities for the AI ecosystem.

2. Experimental Design: Using a Real Frontend Requirement as the Test Case

To avoid a benchmark-style evaluation, this exercise selected a frontend project with three representative pages:

1. Login page: Includes an image carousel, form inputs, and basic interactions.
2. Product listing page: Includes multi-condition filtering, pagination, and data display logic.
3. Form page: Includes multi-field input, field dependencies, validation rules, and submission interactions.

The requirements were described in natural language documents without specifying a particular tech stack. The model was free to choose its own implementation approach. Evaluation dimensions included: page structure completeness, fidelity of functional logic, accuracy of interaction details, overall style and engineering usability, and end-to-end generation time.

The significance of this design is that it simulates a developer’s real workflow—from requirement document to runnable code—and therefore exposes a model’s capability boundaries in requirement understanding, code organization, and detail handling.

3. Results Analysis: Layered Performance in Completion and Understanding

3.1 Login Page: Structurally Complete and Ready for Minor Refinements

The login page achieved a high level of completion. The model correctly identified and implemented the carousel requirement. The page structure was complete, with no obvious omissions in the form area, button interactions, or basic styling. From an engineering perspective, this page was at the level of “ready for production after minor adjustments,” indicating that the model can reconstruct relatively linear, clearly structured requirements well.

This result aligns with a common characteristic of current LLM code generation: for highly templated pages with few logical branches, models can quickly produce usable code by drawing on common patterns in their training data.

3.2 Product Listing Page: Correct Structure but Degraded Functional Understanding

Weaknesses began to surface on the listing page. The requirement document explicitly described filtering conditions and interaction logic, but the filter options were not implemented in the generated output. This issue appeared repeatedly across multiple listing pages, pointing to a systemic capability gap rather than an occasional error.

From a technical perspective, the difficulty of the listing page lies in the compound logic of “state management + condition combination + data linkage.” The model could build the page skeleton, but when mapping naturally described conditions into executable filtering logic, it either misunderstood or omitted key elements. This suggests that models remain unstable when parsing business semantics under multi-condition constraints.

3.3 Form Page: Insufficient Restoration of Field Definitions and Validation Rules

The form page was runnable overall, but field definitions, validation rules, and interaction details were not fully implemented according to the document. Even with relatively clear requirement descriptions, the model struggled to accurately reconstruct details such as field relationships, validation timing, and error prompts in a complex form.

The core issue is that form scenarios involve numerous implicit constraints—required-field dependencies, format validation, pre-submission aggregate validation—that require the model to integrate scattered natural language descriptions into a coherent state machine logic. This remains a notable weakness of current code generation models.

3.4 Overall Style: Engineering Usability First, Design Polish Lacking

In terms of UI style, the generated results outperformed most rough prototypes but still fell short of mature design systems such as established component libraries combined with atomic styling approaches. The overall output showed an “engineering-first, function-prioritized” character, lacking fine-grained design refinement. In short: engineering usability > product aesthetics.

3.5 Cross-Model Comparison: The Gap Is in “Depth of Understanding”

When the same requirements were given to another type of model for comparison, layered differences emerged:

• Login page: Completion quality was comparable.
• Product listing page: The comparison model demonstrated a more complete understanding of filter conditions and field structure.
• Form page: The comparison model showed clearer advantages in restoring field relationships, validation logic, and interaction details.
• Generation time: Results varied significantly across models, but timing is affected by service load, quota limits, and other factors, so it should not be treated as the sole evaluation metric.

The core conclusion is that the gap lies not in “whether code can be written” but in “whether requirements can be accurately understood.” Page structure can be generated through templating, but accurate restoration of business semantics depends on deeper requirement comprehension.

4. Technical Attribution: A Three-Layer Capability Model for Code Generation

Based on the above observations, LLM code generation capabilities can be decomposed into three layers:

Layer	Capability Description	Typical Performance
L1 Structural Generation	Building page skeletons and organizing file structures	Achieved on the login page
L2 Logic Restoration	Mapping requirement semantics to executable logic	Missing filters on the listing page indicate weakness at this layer
L3 Detail Refinement	Validation rules, interaction timing, edge case handling	Form page shortcomings are concentrated here

Most current models are relatively mature at L1, unstable at L2, and still broadly deficient at L3. This explains why there remains a gap between “impressive demos” and “production-ready output.”

5. The Significance of Open-Source Attributes: Capability Gains and Ecosystem Spillover

When discussing whether Chinese models are approaching international mainstream models, the open-source dimension cannot be ignored. Its significance manifests in three main ways:

1. Lowering the barrier to technology access: Open-source models enable small teams and individual developers to deploy locally or in private environments, reducing reliance on closed APIs. This has practical value, particularly in data-sensitive scenarios.
2. Promoting transparency and reproducibility: Open weights and inference solutions allow the community to reproduce evaluation results, identify capability boundaries, and develop a more objective understanding of model capabilities—rather than relying on marketing-driven “breakthrough” narratives.
3. Accelerating vertical scenario adaptation: Developers can perform domain fine-tuning, quantization, and engineering optimization on open-source models, transforming general capabilities into usable solutions for specific business contexts. This fosters a layered ecosystem of “base model + scenario adaptation.”

It is important to note that open source does not mean “free is always best.” Model capability, inference cost, and engineering support together determine real-world outcomes. A rational view of open-source value should focus on the optionality and customizability it provides, rather than simplistic parameter comparisons.

6. Implications for AI Development

From a broader perspective, the competition in code generation reflects several trends in AI development:

• From “can generate” to “can understand”: The evaluation focus is shifting from code volume to requirement restoration accuracy. Depth of understanding is becoming the dividing line.
• From “single-point demos” to “engineering closed loops”: Runnable, maintainable, and integrable code is what constitutes real productivity.
• From “closed-source racing” to “open-source collaboration”: Open-source ecosystems create positive iteration cycles through community feedback and secondary development.
• Managing expectations rationally: Model capabilities are layered. Developers should choose appropriate toolchains based on task complexity and avoid over-generalizing from isolated cases.

7. Practical Recommendations

For teams planning to introduce LLMs into their code generation workflows, the following points are worth considering:

1. Structure requirement documents: Express filtering conditions and validation rules as checklists or pseudocode to significantly reduce semantic ambiguity.
2. Layer-based acceptance testing: Evaluate outputs separately at the “structure—logic—detail” levels rather than using the crude criterion of “the page opens, so it passes.”
3. Human-AI collaboration: Treat the model as a “draft generator,” with developers responsible for logic verification and detail completion—rather than pursuing fully automated end-to-end generation.
4. Prioritize maintainability: Prefer generated code with clear organization and explicit dependencies to reduce long-term maintenance costs.

8. Conclusion

Chinese LLMs have achieved usable structural generation capabilities in code generation, but there remains room for improvement in depth of requirement understanding and detail restoration. Open-source attributes provide an important foundation for technology accessibility and ecosystem adaptation, and their true value lies in promoting capability transparency and scenario-specific deployment. For developers, a pragmatic path to turning LLMs into real productivity involves rationally understanding capability boundaries, adopting layer-based acceptance testing, and embracing human-AI collaboration.