Evaluating Chinese LLMs for Code Generation: From Prototype to Production

1. Background: When Code Generation Becomes Standard

Over the past year, code generation capabilities have evolved from a nice-to-have feature into a core competency of large language models (LLMs). Whether building frontend interfaces, orchestrating backend APIs, or writing data pipeline scripts, developers increasingly rely on LLMs to turn requirement documents into functional projects.

However, a significant gap exists between “generating code” and “delivering a production-ready application.” Recently, several Chinese LLMs released new versions claiming code capabilities on par with leading international models, sparking discussions within the developer community. I decided to conduct a comprehensive hands-on evaluation using a real-world frontend project—ignoring the marketing hype and focusing entirely on practical outcomes.

This article breaks down the testing process, abstracts a general framework for evaluating code generation capabilities, and explores the practical value and limitations of open-source models in the AI development lifecycle.

2. Test Design: Using a Real-World Project

2.1 Task Definition

I selected a medium-complexity frontend requirement encompassing three typical pages:

• Login Page: Includes a carousel, form validation, and login state simulation.
• Product List Page: Includes multi-condition filtering, pagination, and sorting interactions.
• Form Page: Includes field linkage, dynamic validation, and submission feedback.

The requirements were provided via a natural language document without specifying a tech stack, leaving the model to choose its own implementation approach.

2.2 Evaluation Dimensions

Dimension	Description
Functional Completeness	Whether all feature points in the requirement doc are implemented
Understanding Accuracy	Whether filtering logic, field relationships, and validation rules are accurately restored
Interaction Details	Whether edge cases, loading states, and error prompts are handled
Engineering Usability	Code structure, runnability, and maintainability
Generation Efficiency	Time cost from prompt submission to a runnable state

3. Test Results: Layer-by-Layer Breakdown

3.1 Login Page: Acceptable Completeness, “Usable with Minor Tweaks”

The login page performed adequately overall. The carousel requirement was correctly understood and implemented, and basic form validations (required fields, formats) were covered. The generated page was directly accessible and not just an isolated code snippet.

Evaluation: This page reached a “prototype-ready” level but remains far from production-grade. It lacked edge-case logic such as preventing duplicate submissions, CAPTCHA refreshing, and handling network exceptions.

3.2 List Page: Noticeable Drop in Comprehension

When moving to the list page, issues became prominent:

• Filtering conditions explicitly stated in the requirements were missing.
• Similar issues occurred across multiple list pages, indicating a systematic rather than occasional omission.
• The page structure was correct, but the “functional semantics” were compromised.

This means the model could identify “this is a list page” but failed to fully parse the implied semantics of “what interactive capabilities a list page requires.” Features considered standard by human developers—such as filtering, sorting, and pagination—were not proactively completed by the model.

3.3 Form Page: Insufficient Restoration of Complex Logic

The form page successfully ran the main workflow, but details like field linkage, dynamic validation, and conditional display were not implemented according to the document.

The core issue here wasn’t an inability to write code, but rather a lack of precision in understanding complex requirements. When dependencies exist between fields, the model tends to simplify the logic rather than strictly following the document’s specifications.

3.4 Overall Style and Efficiency

• UI Style: Standard and functional. Better than a bare-bones prototype, but lacking the polish of mature component library templates.
• Design: Engineering-focused, lacking product-level refinement.
• Generation Speed: Generating the full project took considerable time, with noticeable queue waits on free access tiers.

4. The Gap with Leading International Models

The exact same requirements were previously implemented using a leading international model. The comparative conclusions are as follows:

Dimension	Leading International Model	Evaluated Chinese LLM
Login Page	High completeness	Close
List Page	Filtering and field structure fully restored	Missing filtering
Form Page	Field relationships and validation logic accurate	Partially simplified
Generation Speed	Minutes level	Noticeably slower

The core gap is not about “whether the code is syntactically correct,” but rather the depth of requirement comprehension. Leading international models can more accurately extract complete functional semantics from natural language documents, rather than stopping at the surface-level page structure.

5. Implications for Open-Source AI and Development Workflows

5.1 The True Value of Open-Source Models

The primary significance of open-source models lies not in being “free,” but in being customizable, auditable, and deployable privately. For enterprise users, this means:

• Data sovereignty, meeting compliance and security requirements.
• Ability to fine-tune for specific vertical scenarios.
• No vendor lock-in, reducing long-term risks.

Even if the current versions lag behind international leaders in complex tasks, the open-source approach provides a foundation for continuous community iteration. Every weight release serves as a starting point for collective optimization.

5.2 Impact on the AI Development Lifecycle

The maturation of code generation models is transforming how developers work:

1. Accelerated Prototyping: Time from idea to interactive page is compressed from hours to minutes.
2. Reduced Repetitive Labor: Boilerplate code and CRUD pages can be delegated to the model.
3. Shifting Focus: Developers can redirect energy toward architecture design, edge-case handling, and performance optimization.

The prerequisite, however, is that model outputs must be reviewed and hardened, rather than pushed directly to production.

5.3 Realistic Positioning at the Current Stage

Objectively speaking, current Chinese LLMs are at the “engineering-usable > product-polished” stage for code generation. They are suitable for:

• Quickly validating ideas.
• Generating project scaffolding.
• Assisting with repetitive code writing.

They are not suitable for:

• Directly generating production-grade complex business logic.
• Replacing deep comprehension of requirement documents.
• Unattended automated delivery.

6. Practical Recommendations for Developers

1. Structure Requirement Documents: Use lists, tables, and examples to clarify feature points and minimize model ambiguity.
2. Generate Modularly: Do not feed the entire project at once. Break it down by page or component and verify incrementally.
3. Never Skip Human Review: Focus heavily on filtering logic, validation rules, and edge cases.
4. Establish an Evaluation Baseline: Use a fixed set of requirements to compare different models, forming an internal baseline for team tool selection.
5. Follow Open-Source Ecosystems: Open-source models often iterate faster than closed-source ones; maintain active tracking.

7. Conclusion

The progress of Chinese LLMs in code generation is undeniable. Moving from “writing code” to “writing correct code” and finally to “writing good code” takes time and continuous feedback. The shortcomings exposed in this test—comprehension depth, complex logic restoration, and generation efficiency—are precisely the key directions for the next iteration phase.

For developers, rather than obsessing over whether these models “match international standards,” it is more productive to treat the model as a junior engineer requiring collaboration. Provide it with clear requirements, review its output, and reinforce critical paths. By doing so, you can extract tangible value regardless of how the model’s capabilities evolve.