Evaluating Chinese LLMs on Engineering Capability and Open-Source Ecosystem Value: Lessons from a Real Front-End Project

1. Background: LLM Competition Enters the “Engineering Capability” Phase

Over the past year, the focus of competition in the large language model space has been shifting. Early on, the question was whether a model could produce a piece of code that runs. Now, with overall improvements in base capabilities, evaluation criteria have moved to deeper dimensions: can the model understand complex requirements, accurately reproduce business details, and deliver a complete, runnable project?

A Chinese open-source model recently released a new version, claiming benchmark performance comparable to top-tier international closed-source models on multiple public evaluation sets. Such announcements typically ignite developer communities, with reactions ranging from “game-changing” to “magic tool.” But from a practitioner’s perspective, the more important question is: How do these models actually perform in real projects?

This article uses a real front-end project requirement as a test case, systematically analyzes the code generation capability of a Chinese LLM from an engineering practice standpoint, and explores the deeper implications of its open-source nature for the broader AI ecosystem.

2. Test Design: Real Project Requirements as Evaluation Baseline

2.1 Methodology

To keep the evaluation objective, I adopted the following approach:

• Requirement source: A real product’s front-end requirements document, covering three core pages: a login page, a list page, and a form page
• Tech stack: No language constraint; the model was free to choose (it selected React)
• Delivery standard: A deployable project URL was required, not just code snippets
• Comparison baseline: The same set of requirements had previously been completed with a leading international model, enabling a side-by-side comparison

2.2 Evaluation Dimensions

I defined four core evaluation dimensions:

1. Completeness: Are all features fully implemented?
2. Depth of understanding: Are the interaction logic and business details from the document accurately reproduced?
3. Engineering usability: Is the code structure reasonable, and can it be deployed directly?
4. Efficiency: Generation speed and resource consumption

3. Test Results: Capabilities and Shortcomings Coexist

3.1 User Experience: Usability Constrained by Resource Scheduling

First, it is worth noting that during testing I repeatedly encountered “too many users, please queue” prompts. This reflects ongoing pressure on inference resource scheduling—no matter how capable a model is, if users cannot access it reliably, its engineering value is significantly diminished.

After successfully getting in, the model took about half an hour to complete the project generation. This is noticeably slower than leading international models (which typically finish in around three minutes). This may be partly attributable to using a free tier, but it also indicates room for optimization in inference efficiency.

On a positive note, the model directly provided an accessible project URL rather than just code snippets. This is very developer-friendly—being able to run matters more than “looking correct.”

3.2 Login Page: Relatively High Completeness

The login page performed well overall. The carousel requirement mentioned in the document was correctly implemented, and the page was feature-complete with no obvious gaps. The completeness of this page could be rated as “ready to ship with minor tweaks.”

This suggests that for pages with relatively simple structure and standardized interaction patterns, the current Chinese model already has strong generation capability.

3.3 List Page: Noticeable Degradation in Understanding

Problems began to surface on the product list page. I had explicitly specified filter conditions and interaction logic in the document, but the model did not implement the filter options. More critically, this was not an isolated issue—the same defect appeared across multiple list pages.

The page structure itself was correct (table, pagination, and action column were all present), but the “functional understanding” was clearly lacking. This reveals an important fact:

The model can “scaffold” but struggles to accurately “fill in the content”—especially in scenarios requiring business logic understanding.

3.4 Form Page: Insufficient Detail Reproduction

The form page was functional overall, but many fields, validation logic, and interaction details were not fully implemented as described in the document. Even with a relatively clear specification, the model’s ability to understand and reproduce complex form requirements remains insufficient.

The root cause of such issues: forms often involve inter-field dependencies, conditional validation, dynamic show/hide and other complex logic, requiring strong “structured understanding” capability—which is precisely where current models fall short.

3.5 Overall Style: Engineering Usability First, Design Aesthetics Weak

From a UI style perspective:

• Better than what most prototyping tools produce
• But still falls short of directly applying a mature design system (e.g., a common component library plus atomic CSS approach)
• The design feels simple and engineering-oriented—“functional but not impressive”

In one sentence: Engineering usability > product aesthetics.

4. Side-by-Side Comparison with a Leading International Model

I had previously implemented the same set of requirements with a leading international model. The comparison results are as follows:

Dimension	Chinese Model	International Model
Login page completeness	Close	Close
List page filter logic	Not implemented	Fully implemented
Form field relationship reproduction	Partially missing	Accurately reproduced
Validation logic	Incomplete	Complete
Generation speed	Slower	Faster
Depth of understanding	Moderate	Higher

The core gap centers on “depth of understanding”: the international model has a more complete grasp of filter conditions, field structures, and validation logic from the document, and can accurately reproduce field relationships and interaction details. The Chinese model performs comparably on simple pages but clearly loses ground on complex business logic.

5. Strategic Significance of the Open-Source Approach

Despite the shortcomings observed in testing, the fact that this Chinese model is open-source carries significant strategic value:

5.1 Lowering Technical Barriers

Open-source means small and medium-sized enterprises and individual developers can deploy privately without relying on external APIs. This is particularly critical for data-sensitive industries such as finance and healthcare.

5.2 Driving Ecosystem Co-Building

Open-source models allow the community to contribute fine-tuned versions, toolchains, and best practices. In the long run, the depth of the ecosystem determines a model’s vitality. However strong a closed-source model may be, it remains a single team’s product; an open-source model can evolve into a technology platform.

5.3 Accelerating Iterative Feedback

The transparency that open-source brings allows problems to surface faster. For example, if the “filter logic not implemented” issue mentioned in this article could be quickly identified by the community and fed back into training data, the next version’s improvements would be more targeted.

5.4 Building Differentiated Competitiveness

Catching up with top international models on general capabilities takes time, but through open-source plus vertical scenario optimization, Chinese models can establish differentiated advantages in specific domains (e.g., Chinese-language business systems, government and enterprise digitalization).

6. Implications for AI Development

6.1 From “Can Write Code” to “Understands Business”

The core bottleneck of current models is not code syntax but business understanding. Future optimization directions should focus on:

• Enhancing structured parsing of requirements documents
• Improving accuracy of multi-step logical reasoning
• Establishing more comprehensive code generation benchmarks (not just unit test pass rates)

6.2 Engineering Deployment Requires “Human-AI Collaboration”

At this stage, the most pragmatic approach is not “letting the model complete the project independently” but:

Developer: Break down requirements → Define interfaces → Write critical logic
Model: Generate boilerplate code → Fill in repetitive logic → Provide reference implementations
Developer: Review, correct, integrate

6.3 Open Source Is Long-Termism

The true value of an open-source model is not “matching a competitor in a single release” but the ability to iterate continuously. As long as the community is active and feedback channels remain open, shortcomings can be progressively addressed.

7. Conclusion

Through this real-world project test, we can draw the following conclusions:

1. Chinese LLMs already offer engineering usability for standardized pages; login-page-level requirements can be handled well
2. Complex business logic (filtering, validation, field dependencies) remains a clear weakness, with a perceptible gap compared to leading international models
3. The open-source approach provides strategic value: lowering barriers, driving ecosystem growth, accelerating iteration
4. The current best practice is human-AI collaboration: the model “scaffolds,” the developer “fills in the logic”

LLM competition is a marathon, not something decided by a single evaluation. For developers, the optimal approach today is to rationally understand each model’s boundaries and combine them flexibly in real projects. For Chinese models, staying open-source, iterating continuously, and focusing on business understanding is the right path to close the gap and build an ecosystem.