Benchmarking Domestic LLMs in Code Generation: Capabilities, Gaps, and AI Development Trends

As Large Language Model (LLM) technology rapidly evolves, code generation and assisted programming have become critical benchmarks for evaluating a model’s logical reasoning and ability to follow complex instructions. After multiple iterations, can domestic LLMs rival top-tier international models? And what does this mean for the open-source ecosystem and AI development?

To answer this, we stripped away specific product attributes and used a real-world, complex frontend project requirement as a benchmark to test a representative domestic LLM (hereafter referred to as “Domestic Model A”). We conducted a horizontal comparison with a top-tier international model (hereafter “Benchmark Model B”). The testing environment strictly adhered to neutral principles, focusing on the models’ depth of understanding regarding complex business logic and their engineering implementation capabilities.

1. Benchmark Specifications and Environment Setup

Rather than restricting the test to a specific frontend framework, we provided a detailed requirements document outlining the complete interaction logic, field definitions, and UI specifications. The models were free to choose a suitable tech stack based on their training data (both models defaulted to mainstream declarative UI frameworks).

The overall generation process for Domestic Model A took over half an hour. Compared to Benchmark Model B’s average generation speed of around 3 minutes, Domestic Model A showed a noticeable gap in initial response time and streaming output speed. However, Domestic Model A ultimately outputted not just a code snippet, but a complete, engineered project skeleton equipped with basic routing configurations that could be directly accessed via a local server. This “one-click runnable project” approach significantly lowers the barrier to entry for developers.

2. Core Feature Scenario Analysis

Login Page: Solid Basic Layout and Engineering Usability

For the login page scenario, the requirements specified form inputs, state toggles, and a background carousel. Domestic Model A performed satisfactorily:

• Page Structure: Accurately constructed the form and carousel containers with clear DOM hierarchies.
• Interaction Implementation: The carousel component auto-played correctly, and basic form validation (e.g., non-empty checks) was logically complete.
• Evaluation: The page’s completion level was high, reaching a “ready for production with minor polishing” standard. This indicates that Domestic Model A possesses a solid underlying code reserve for handling standardized, highly templated basic pages.

List Page: Degradation in Following Complex Instructions

Issues emerged during the product list page test. The document detailed multi-dimensional filtering conditions (e.g., dropdowns, cascading selectors) and their linked interaction logic, but the model’s implementation showed obvious omissions:

• Correct Structure but Incomplete Functionality: The model correctly rendered the list’s table or card structure, but the filtering UI components were not instantiated, and event bindings were missing.
• Logic Disconnect Analysis: This phenomenon repeated across multiple similar list pages, indicating it was not an occasional probabilistic error but a degradation of the model’s attention mechanism when processing long contexts and complex conditional branches. Faced with multiple constraints, the model tended to prioritize the integrity of the main visual structure at the expense of peripheral yet core business logic.

Form Page: Shortcomings in Deep Business Logic Understanding

The form page is one of the most complex scenarios in enterprise management systems, involving field linkage, asynchronous validation, and state management. Domestic Model A performed as follows:

• Basic Usability: The form could render and submit, passing the basic workflow.
• Low Detail Fidelity: The field linkage logic explicitly required in the document (e.g., displaying Field B if Type A is selected) was not implemented; complex asynchronous validation (e.g., duplicate-check API simulation) was simplified to frontend regex validation.
• Comparative Analysis: In contrast, Benchmark Model B accurately restored the implicit dependencies between fields based on the same document and generated validation code optimized with debounce and throttle mechanisms. This shows that Domestic Model A still falls short in the “depth” of semantic understanding, acting more like mechanical pattern matching rather than true business logic parsing.

3. UI Style and Engineering Adaptability Evaluation

Regarding overall visual style, although Domestic Model A’s generated code did not utilize a specific mature commercial component library, its autonomously generated CSS/style code presented a “conventional engineering aesthetic”:

• Pros: Uniform spacing, color schemes that meet basic design specifications, and a more substantial feel than standard wireframe prototypes.
• Cons: Lacks the dynamism of modern design languages. It still falls short compared to directly using mature UI templates, leaning more towards “engineering usability” than “product wow factor.”
  For internal enterprise tools pursuing rapid delivery, this style is acceptable. However, for consumer-facing (C-end) products, it still requires significant visual refactoring by frontend engineers.

4. Aligning Domestic LLMs with Top-Tier Models: Gaps and Breakthroughs

Based on the tests, Domestic Model A has achieved initial scale in basic code generation. However, in its contest with Benchmark Model B, the core gaps are concentrated in two dimensions: “Understanding Depth” and “Execution Efficiency”:

1. Understanding Depth (Semantic to Logic Mapping): Top-tier models can read requirement documents like senior architects, extracting implicit logic. Current domestic models act more like junior programmers, methodically translating explicit instructions. The core to solving this lies in enhancing the model’s logical reasoning capabilities during the code pre-training phase, rather than simply increasing the volume of code.
2. Execution Efficiency (Compute and Architecture Optimization): The prolonged generation time exposes bottlenecks in underlying inference compute scheduling or model architecture. In actual enterprise-level R&D, developers are highly sensitive to the response latency of AI tools integrated into their IDEs.

5. The Profound Impact of Open-Source Attributes and Tech Development

Despite the gaps, the fact that domestic models are continuously approaching top-tier international standards holds immeasurable value for the industry ecosystem:

1. Breaking the “Black Box Monopoly” of Compute and Technology
When domestic models can rival or even replace closed-source top-tier models in certain scenarios, they provide developers with an alternative that does not rely on specific external ecosystems. The open-source nature means enterprises can deploy models privately and fine-tune them with their internal code repositories, building dedicated intelligent R&D pipelines while ensuring data privacy. This is particularly crucial for sensitive industries like finance and government that involve core business code.

2. Driving Application Layer Ecosystem Prosperity and Feedback Loops
An excellent open-source foundational model is the soil for upper-layer application innovation. When LLMs can generate complete engineering code, it will spawn a new batch of “AI Full-Stack Development Tool” and “Automated Software Factory” startups. The massive amounts of high-quality interaction data generated by these applications in real-world business can, in turn, feed back into the open-source models, creating a flywheel effect of “model capability improvement - application ecosystem prosperity - data feedback iteration.”

3. Promoting the Evolution of Underlying Cloud Service Architectures
The extreme demand for compute power by LLM code generation is forcing the upgrade of underlying cloud infrastructure. To support millions of developers simultaneously calling LLMs for code generation, cloud service providers (such as Baidu AI Cloud) are accelerating the development of high-performance container platforms, large-scale GPU compute scheduling systems, and intelligent monitoring and alerting systems. This compute demand, triggered by AI applications, is propelling cloud computing to transition from a mere resource provider to an “intelligent computing service provider.”

Conclusion

The test performance of domestic LLMs in code generation shows that while significant progress has been made in the “breadth” of engineering implementation, there is still a hill to climb regarding the “depth” of complex logic restoration. Acknowledging the gaps, continuously optimizing the model’s ability to follow complex instructions, and building a developer moat relying on the open-source ecosystem will be the necessary path for domestic AI to achieve comprehensive self-strengthening. For developers, rationally viewing the boundaries of model capabilities and treating AI as a “super assistant” rather than a “complete replacement” is the optimal solution for improving R&D efficiency today.