Benchmarking LLM Coding Ability: From Requirement Docs to Runable Frontend

1. Background and Test Objectives

Large language models (LLMs) have advanced rapidly in code generation over the past few years. Early models could only complete individual functions; today they can generate full pages or even runnable projects from a textual requirement description. However, a recurring issue has emerged: models often perform well in demo scenarios but struggle with real-world project requirements.

To more accurately assess the current usability of LLMs in frontend development, I designed a comprehensive test. Instead of letting the model improvise, I provided a frontend requirement document that closely mirrors a real work scenario. The model was asked to fully implement a small project that includes a login page, a product list page, and a complex form page.

The test focuses on four key dimensions:

1. Whether the model can accurately interpret business logic described in the document;
2. Whether the implementation is complete, with minimal omissions;
3. Whether the output is engineering-ready, rather than just code snippets;
4. How well the generated result performs in terms of visual quality and interaction completeness.

The findings from this test provide useful insight into where current LLMs can be effectively applied in frontend development, and where they should be used with caution.

2. Test Setup and Overall Experience

No specific tech stack was mandated in the test. The model ultimately selected React as the base framework. During generation, the platform experienced high load, and multiple prompts appeared requiring queueing or payment. Several refreshes were needed before the model became usable, which is a significant factor in real-world usage.

The entire process, from start to finish, took roughly half an hour. Compared to other code-generation models I have tested, this was relatively slow. On a positive note, the model did not produce fragmented code snippets. Instead, it delivered a directly accessible project URL and a full project structure. This means that in terms of “producing runnable results,” it met the basic threshold for engineering validation.

Still, there was an obvious bottleneck: generation time was long, and the feedback loop was slow. This makes iterative adjustment costly for scenarios that require frequent changes.

3. Page-Level Test Results

3.1 Login Page: High Fidelity in Feature Implementation

The login page was the first module tested. The requirement document specified a carousel, login form, basic validation, and page layout. The final result was mostly successful:

• The carousel function worked correctly;
• The login form included all required fields;
• The page structure was clear, with no obvious omissions;
• The visual style followed a conventional, engineering-oriented design—not outstanding, but acceptable.

This level of requirement is fairly standardized, the technical approach is mature, and similar samples are abundant in model training data. Therefore, the login page’s performance met expectations. It demonstrates that LLMs can achieve near-practical usability in generating pages with clear, well-structured patterns. For many standard pages in internal management systems, this capability can significantly reduce repetitive coding effort.

3.2 Product List Page: Observable Decline in Understanding

Problems began to surface on the product list page. The requirement document explicitly outlined filter conditions, field structures, and interaction logic. However, the generated output showed:

• Incomplete implementation of filter features;
• Field display mismatched with the document description;
• Missing interaction state transitions;
• The same issues appeared across multiple list pages, indicating systemic problems rather than isolated errors.

This phenomenon warrants closer analysis. Compared to the login page, the list page involves significantly higher business context complexity. Filter conditions are not standalone UI elements—they need to work in conjunction with data fields, query logic, reset logic, and empty-state displays. Although the model can generate a page structure that resembles a list page, it struggles with understanding and connecting the business semantics behind those elements.

In other words, the model’s structural generation capability is stronger than its logical comprehension capability. It can infer which sections a page should have, but it fails to accurately model how those sections interact.

3.3 Complex Form Page: Significant Shortfalls in Advanced Requirements

The form page represented the largest performance gap in this test. The requirement document described field types, validation rules, interaction dependencies, and submission logic in detail. The model’s output diverged from the document in several areas:

• Some fields were missing or had incorrect types;
• Validation rules were not implemented as specified;
• No interdependencies between fields were reflected;
• Interaction details did not match actual business requirements.

Based on these results, a basic conclusion can be drawn: for simple forms with limited fields, LLMs handle the task relatively well. However, as form complexity increases—especially when cross-field dependencies, conditional visibility, dynamic validation, and group structures are involved—the model’s ability to accurately replicate the requirements drops significantly.

The root cause is that complex forms are not just UI components. They are a set of coupled business constraints. During generation, due to context length limitations, attention dilution, and the lack of a continuous constraint-checking mechanism, the model often “understands the first half correctly but misses the second half.” This is a common weakness of current LLMs in complex engineering tasks.

4. Visual and Design Quality

The overall UI style is average. It is better than a low-fidelity wireframe, but it does not match the quality of pages built with mature component libraries and design systems.

Specific observations include:

• Page structure is generally reasonable but lacks fine-grained polish;
• Component consistency is acceptable, but visual hierarchy is weak;
• Interaction states are not fully covered—loading states, empty states, and error states are often overlooked;
• The style leans more toward “engineering output” than “product-grade aesthetics.”

This indicates that current LLMs are better at solving the “does the function exist” problem than the “is it polished and user-friendly” problem. For projects with high design standards, experienced frontend engineers or designers are still required to refine the output.

5. Cross-Model Comparison: The Difference Lies in “Depth of Understanding”

For the same requirement document, I had previously used another model with strong code-generation capabilities. A side-by-side comparison shows that the difference is not about “whether code can be generated,” but “how deeply the requirement is understood.”

In the case of the login page, both models achieved similar results, suggesting that mainstream LLMs are roughly on par for standardized page generation. However, on the list and form pages, the gap was more pronounced:

• Understanding filter conditions: The tested model omitted certain functions, while the baseline model identified fields and interaction logic more thoroughly;
• Restoring form field relationships: The tested model missed fields and dependency logic, while the baseline model more faithfully reproduced validation rules and interaction details;
• Generation speed: The baseline model completed the task in about 3 minutes, whereas the tested model took considerably longer—partly due to free-tier resource scheduling.

This comparison suggests that the essential difference in LLM coding ability lies in the depth of requirement comprehension and the precision of logic restoration, rather than in generation speed or the number of pages produced.

6. Where This Capability Fits in Practice

Based on this round of testing, several pragmatic recommendations can be made:

Tasks well suited for LLM-generated code:

• Standard login and registration pages with fixed structures;
• Basic CRUD pages in internal management systems;
• Rapid generation of runnable prototypes for requirement discussions;
• Standard page construction based on existing component libraries.

Tasks that require caution:

• List pages with complex business filtering logic;
• Complex forms with extensive cross-field dependencies;
• Pages with strict requirements for interaction details and exceptional states;
• Functional modules that require precise matching with backend data structures.

Recommended collaboration model:

Rather than asking an LLM to generate an entire project in one pass, it is more effective to use it as an assisted coding tool with a workflow like: “manually decompose tasks → have the model generate partial code → review and correct.” For complex modules, let the model generate the basic structure first, then have a developer complete the business logic. This approach balances efficiency and quality more effectively.

7. Implications for Developers

The core conclusion from this round of testing can be summarized as follows: LLMs are now capable of handling a portion of standardized frontend development work, but they still have clear limitations in complex business comprehension and long-chain logic implementation.

This is not a rejection of the value of LLMs. Instead, it helps developers identify their capability boundaries more accurately. For everyday tasks that involve repetitive and structured page generation, using LLM-assisted coding can substantially improve efficiency. However, for tasks that require deep business understanding, tightly coupled logic, and high-quality interactions, human design and development remain irreplaceable.

From a longer-term perspective, LLM coding capabilities are evolving rapidly. The current gap should not be mistaken for a permanent ceiling. Developers who learn to collaborate effectively with such tools—understanding their limits and adopting optimal usage patterns—will be better positioned in the efficiency race.

Tools are just tools. Understanding them, knowing how to use them well, and neither overestimating nor dismissing them is the right mindset for technical professionals.