Evaluating Open-Source LLMs for Frontend Generation: Capabilities, Gaps, and Engineering Boundaries

Recently, the industry’s discussion around Large Language Model (LLM) capabilities has intensified. The core question is no longer whether a model can generate code, but rather to what extent that code is truly usable in production.

When domestic open-source models are benchmarked against international flagship models, the evaluation becomes highly specific:

• Do they genuinely possess capabilities close to top-tier international models?
• Is this capability confined to benchmark performance?
• Can they reliably fulfill complex requirements in real-world projects?
• For developers and enterprises, what are the current optimal use cases and limitations of these models?

To answer these questions, I opted for an approach that mirrors real-world development: instead of running generic benchmarks or using fixed prompt templates, I provided the model with actual frontend requirements for a real project and asked it to implement the complete solution.

This article does not promote any specific product. Instead, from an engineering perspective, it analyzes the actual performance of current LLMs in frontend generation, complex requirement comprehension, and engineering usability based on a hands-on test.

1. Testing Methodology: Why Real-World Project Requirements?

When evaluating LLM capabilities, the standard practice is to use a set of standardized questions to test performance in algorithms, code completion, and bug fixing. While statistically significant, this approach has a glaring limitation: it fails to replicate the real-world development lifecycle of “ambiguous requirements -> iterative clarification -> complete delivery.”

Real-world projects typically exhibit the following characteristics:

• Requirements are rarely delivered all at once; they are iteratively refined.
• Pages have business logic dependencies rather than existing in isolation.
• Fields, validation rules, and interaction logic are often implicit in documentation.
• The final deliverable must be runnable and maintainable.

Therefore, I prepared a frontend requirements document covering a login page, a product list page, and a form page. The document detailed the business context, data structures, filter criteria, and interaction logic. Based on this, the model was tasked with generating a runnable frontend project from scratch.

I deliberately omitted specifying a framework in the prompt to observe the model’s autonomous decision-making in selecting a technical architecture, closely mimicking real-world engineering scenarios.

2. Overall Results: Clear Capability Stratification

Overall, the model moved beyond the limitations of earlier versions that could only generate code snippets or simple pages. It successfully output a frontend project with a complete directory structure. More importantly, it provided an accessible project URL rather than stopping at static code.

This indicates a significant enhancement in the model’s understanding of “project-level tasks,” demonstrating emerging engineering awareness spanning file organization, component decomposition, and page routing.

However, the performance was uneven. The quality varied notably across different page types, primarily in requirement comprehension depth and interaction fidelity.

3. Page-Level Breakdown: Assessing True Boundaries via Completion Fidelity

3.1 Login Page: High Completion Rate, Near Production-Ready

The login page yielded the highest quality output in this test.

The requirements document specified basic elements like carousels, form fields, and button states. The generated result implemented these features with a clear page structure and no obvious omissions. While the design aesthetic was unspectacular, the overall usability was high, reaching a “ready for business use with minor modifications” standard.

This result demonstrates that for relatively standard pages with mature component patterns, the model possesses strong generation capabilities. Such tasks are heavily represented in training data, allowing the model to reliably apply layout patterns, component structures, and interaction logic.

3.2 List Page: Decent Structure, but Declining Business Comprehension

Issues began to surface with the product list page.

The document explicitly defined filter criteria and interaction methods, such as filtering by status, sorting by time, and compound queries. However, the generated output failed to implement these capabilities fully. This issue occurred across multiple list pages, indicating a capability deficit rather than a sporadic error.

Technically, the difficulty of a list page lies not in the UI structure, but in:

• Understanding the relationship between “filter criteria -> data structure -> interaction outcome.”
• Abstracting field meanings and business semantics.
• Accurately restoring data linkage behaviors following state changes.

The model generated pages that “looked like lists,” but exhibited clear deviations in understanding “how and why the list operates.”

3.3 Form Page: Insufficient Restoration of Fields and Validation Logic

The gap was even more pronounced on the form page.

While the page itself was runnable, the number of fields, field types, validation rules, and linkage logic did not fully align with the requirements document. Even with clear requirement descriptions, the model exhibited omissions and deviations in complex form scenarios.

This suggests that the current model is better suited for form tasks with “clear boundaries and simple rules.” Once multi-condition validations, cross-field linkages, or dynamic fields are introduced, its comprehension accuracy drops rapidly.

4. Style and Engineering: Usability Over Design

Visually, the generated pages were mediocre. They resembled real-world projects more closely than standard prototype tools, but a gap remained compared to mature design systems. The aesthetic leaned heavily toward engineering utility, lacking product-level polish; the style was consistent but lacked refinement.

This means the current positioning of LLMs in “frontend generation” is closer to:

• Rapidly building runnable prototypes.
• Providing a foundational engineering scaffold.
• Reducing repetitive page development.
• Acting as an auxiliary implementation tool for developers.

While it is not yet capable of “directly generating high-quality product UIs,” it holds tangible value as an engineering efficiency tool.

5. Comparison with International Flagship Models: The Gap Isn’t About Capability

I had previously implemented the exact same requirements using an international flagship model. A comparison revealed that the gap is not about “whether the task can be completed,” but rather about “depth of understanding” and “restoration precision.”

5.1 Minimal Gap on Simple Pages

For standard pages like the login page, completion rates were similar. This indicates that in mature patterns, the foundational capabilities of different models are converging.

5.2 Widening Gap on Complex Pages

For list and form pages, the international flagship model demonstrated significantly deeper comprehension, accurately restoring filter criteria, field relationships, and interaction logic. The gap stems not from generation ability, but from semantic comprehension and business abstraction capabilities.

5.3 Efficiency and Experience Differences

In terms of generation speed, the international flagship model was noticeably faster in this test, while the evaluated model took considerably longer. It should be noted that speed is heavily influenced by compute power, concurrency, and service strategies, and does not strictly reflect intrinsic model capability. However, from a developer experience perspective, this latency impacts practical usability.

6. The Implications of Open-Source Accessibility

If such models possess open-source or accessible attributes, their significance to the industry becomes highly noteworthy.

First, they lower the barrier for technical validation. Developers can deploy the model locally or in private environments for capability testing and scenario adaptation without relying entirely on closed-source APIs.

Second, they provide a reproducible research foundation. For teams researching LLM code generation, frontend construction, or AI Agent collaboration, access to model weights or open capabilities allows for deeper engineering exploration.

Third, they help enterprises build controllable capability chains. Under requirements for data compliance, private deployment, and cost control, open-source attributes allow enterprises to integrate models into their own infrastructure and build native applications around them.

However, open-source does not equate to capability leadership. If an open-source model still exhibits noticeable gaps in complex tasks, its value lies primarily in being “modifiable, customizable, and privately deployable,” rather than serving as a direct replacement for flagship closed-source models.

7. Recommended Best Practices for Current Stage

Based on the test results, these models are currently best suited for:

• Rapidly generating frontend page prototypes.
• Providing initial project structures and component frameworks.
• Assisting with standardized, highly repetitive development tasks.
• Acting as efficiency tools within the development workflow, rather than replacing developers entirely.

For complex business scenarios—especially those involving extensive field relationships, multi-condition linkages, and state management—manual verification, modification, and optimization by developers remain essential.

Enterprises looking to integrate LLM capabilities into their actual development workflows should start with “assisted implementation,” applying the model to low-risk, high-repetition tasks first. Concurrently, establishing human review and testing mechanisms is crucial to avoid over-trusting generated outputs.

8. Conclusion: Converging Capabilities, but Boundaries Must Be Acknowledged

The most profound takeaway from this test is that LLM capabilities in frontend generation are converging rapidly. Models can now complete the end-to-end process of reading requirement documents, generating project files, and outputting fundamentally usable pages.

However, a visible gap remains between this and “understanding complex business logic for accurate implementation.” This gap is not merely a generation issue, but stems from underlying capabilities in semantic comprehension, logical reasoning, and long-chain task restoration.

For developers, the key is understanding the realistic boundaries of the model, rather than being swayed by “flagship-equivalent” marketing claims. For enterprises, the priority is finding a suitable integration approach rather than blindly adopting touted “explosive results.”

From a long-term perspective, as the gap between open-source and flagship closed-source models narrows, true competition will no longer be about “who generates simple pages faster.” It will be about “who can consistently deliver reliable results in complex, real-world, and uncertain business environments.”