Deep Evaluation of Chinese LLMs for Complex Frontend Generation: Capability Boundaries and the Value of Open Source

Introduction: When LLMs Start “Writing Projects”

Over the past year, code-generation large language models (LLMs) have evolved far faster than expected. From function-level autocompletion to generating complete, accessible frontend projects from a single requirement document, models are shifting from “assistive tools” to “engineering collaborators.”

A recent Chinese LLM released a new version, with official and community evaluations focusing on its ability to match international frontier models. Social media is full of superlatives like “amazing” and “game-changer,” but such emotional language offers limited value for developers who need to use these tools in real work. A more pragmatic question is: If I hand a real frontend requirement document to this model, what level of output can it actually deliver?

This article is based on a complete hands-on test. It breaks down the model’s real performance on complex frontend generation tasks from an engineering perspective and further discusses the significance of its open-source nature for the broader AI ecosystem.

Test Design: Validating with Real Requirements, Not Demos

Test Tasks

To ensure engineering relevance, this test avoided toy tasks like “generate a to-do list.” Instead, a real frontend requirement with three typical pages was selected:

• Login page: Carousel images, form validation, responsive layout
• Product list page: Multi-condition filtering, pagination, data display
• Form page: Multi-field input, dependent logic, validation rules

The requirement was provided as a document, explicitly describing functionality, interaction logic, and field structures. No tech stack was pre-specified; the model was free to choose the framework.

Evaluation Dimensions

Four dimensions were used for evaluation:

Dimension	Description
Functional completeness	Are all documented features implemented
Comprehension depth	How well filters, field relations, and validation logic are reproduced
Delivery format	Code snippets only or a complete runnable project
Generation efficiency	Time cost from requirement submission to delivery

Test Results: Breaking Down Capability by Layer

Access Experience: Resource Scheduling Is Still a Bottleneck

The first friction point appeared at the entry stage. Multiple attempts returned “too many users, please queue or pay,” and generation could only begin after repeated refreshes. This reflects the current supply-demand imbalance in inference resources—even if model capability is sufficient, service availability remains a key variable in real user experience.

For enterprise use, a stable service SLA matters more than peak capability. If generation tasks require queuing for dozens of minutes, the model’s value in production workflows drops significantly.

Login Page: A “Production-Ready” Delivery with High Completeness

The login page was the best-performing of the three.

The requested carousel was correctly implemented, the form structure was complete, and the overall page functioned properly with no obvious omissions. From an engineering standpoint, this page reached the level of “minor adjustments away from going live.”

The model chose React as the tech stack, and the final output was not just code snippets but a directly accessible project URL. This matters practically: the model not only generated code but also handled scaffolding, dependency installation, and deployment.

Product List Page: Comprehension Begins to Degrade

Problems became evident on the product list page.

The document clearly specified filter conditions and interaction logic, but filters were not implemented. The page structure itself was correct—table, pagination, data display—but functional understanding clearly dropped.

Critically, this was not a one-off issue. Across multiple list-page generations, missing filters were a consistent problem. This indicates a systematic capability gap in handling “structured data plus multi-condition interaction” requirements, rather than random errors.

From a technical perspective, filtering logic involves state management, condition composition, and data filtering across multiple interconnected steps, which demands strong multi-step reasoning. The model performs reasonably on isolated single-point features but tends to produce “correct structure with missing functionality” on complex cross-module logic.

Form Page: Insufficient Fidelity on Complex Requirements

The form page further confirmed this assessment.

The page ran, but many fields, validation rules, and interaction details were not fully implemented as documented. The tester believed the document was clear enough, yet the model’s ability to understand and reproduce complex form requirements was notably insufficient.

A core challenge in complex forms is dependency between fields—one field’s value may affect the visibility, options, or validation rules of another. Such requirements demand understanding not just individual fields but the topological relationship between them. The test results show significant room for improvement in this area.

Overall Style: Engineering Usability First, Limited Design Sensibility

In terms of UI style, the output was average:

• Better than most prototype tool outputs
• Still below the level of directly applying mature design systems (e.g., Ant Design, Tailwind-based component libraries)
• Design leans toward simple engineering aesthetics, lacking a “wow” factor

A fair one-line summary: engineering usability > product aesthetics. For internal tools or MVP validation, this level is acceptable; for consumer-facing products, designer intervention is required.

Gap with International Frontier Models: The Core Is “Comprehension Depth”

Coincidentally, the same set of features had previously been implemented once with an international frontier model. A direct comparison makes the gap clearer:

Page	Chinese Model	International Model
Login page	Near-complete	Near-complete
List page	Filters missing	Full filters and field structure
Form page	Some fields and validations missing	Accurate field relations and interactions
Generation speed	Over 30 minutes	About 3 minutes

The core gap is not “whether code can be generated” but the precision of understanding the requirement document. The international model extracts field structures, filter conditions, validation rules, and other constraints more completely, then maps them accurately into code.

Speed differences are also significant. To be fair, free versus paid tiers involve different resource scheduling, so this comparison is not fully apples-to-apples. But even accounting for resource factors, the gap in comprehension depth remains objective.

Strategic Significance of the Open-Source Release

Open Source Is Not “Free” — It Is “Controllable”

A common misconception when discussing open-source Chinese models is equating it with “free usage.” The core value of open source lies in technical controllability:

• Enterprises can deploy privately, ensuring data never leaves their domain
• Models can be fine-tuned and customized for specific business scenarios
• Model behavior can be audited to meet compliance requirements
• No dependence on external API availability or pricing policies

For industries like finance, government, and healthcare, where data security is critical, private deployment of open-source models is a hard requirement.

Open Source Drives a Positive Iteration Loop

Open-source models have a structural impact on the entire AI ecosystem:

1. Lower research barriers: Universities and research institutions can experiment with these models without training from scratch
2. Accelerate application innovation: Small teams can quickly build vertical applications on open-source foundations
3. Create a feedback loop: Community usage feedback and contributions feed back into model iteration
4. Promote talent development: Developers gain experience with these models, building a talent pool

Practical Value for Developers

For frontline developers, the significance of open-source models appears in concrete workflows:

# Example: model invocation under private deployment
from model_inference import LocalModelClient
client = LocalModelClient(
    model_path="/path/to/open-source-model",
    device="cuda",
    max_length=8192
)
# Generate code on internal network, data stays within domain
response = client.generate(
    prompt="Generate frontend page code based on the following requirement document...",
    temperature=0.2
)

This pattern lets developers handle code generation in an intranet environment, avoiding the compliance risk of uploading business requirement documents to external services.

Capability Boundaries and Model Selection Recommendations

Current Capability Boundaries

Based on this test, the current capability envelope can be outlined as follows:

Strong scenarios:

• Single-page, clearly structured UI generation
• Rapid construction of standard forms and display pages
• Prototype validation and MVP development

Weak scenarios:

• Multi-condition filtering and complex state linkage
• Forms with intricate field dependencies
• Interaction logic requiring deep business understanding

Selection Recommendations

Different scenarios call for differentiated strategies:

• Prototype validation: Use these models to quickly generate interactive prototypes and accelerate requirement confirmation
• Internal tool development: With human review, use the model for base code generation, with developers filling in business logic
• Core product development: Human-led development remains advisable, with the model assisting on localized code generation

Expectations for Future Model Iteration

From a technical evolution perspective, the next key breakthroughs should target:

1. Long-document comprehension: More accurately extracting structured constraints from requirement documents
2. Multi-step reasoning: Handling field linkage, condition composition, and other complex logic
3. Generation speed optimization: Reducing the wait time from requirement to delivery
4. Design enhancement: Improving visual quality beyond engineering usability

Summary

Chinese LLMs have crossed the “usable” threshold for complex frontend generation. On clearly structured pages like the login page, they perform commendably, and the delivery format has evolved from code fragments to fully accessible projects.

However, the gap with international frontier models remains real, and the core difference lies in comprehension depth for complex requirements—the missing filters on the list page and the omitted validations on the form page both trace back to the same issue: a significant shortfall in scenarios requiring multi-step reasoning and cross-module coordination.

The open-source release gives these models strategic value beyond “capability benchmarking.” It provides enterprises with a controllable option, researchers with an accessible experimental foundation, and the ecosystem with a basis for a positive cycle. Capability gaps can be closed through iteration, but the ecosystem value of open source is long-term and structural.

For developers, the rational stance is: use these models within their capability boundaries and keep human involvement beyond them. The value of a tool is not whether it fully replaces humans, but whether it significantly improves efficiency in specific stages of the workflow. By that standard, current Chinese LLMs are already worth integrating into daily workflows.