Evaluating LLM Coding Capabilities: 3D Graphics and Front-End Generation

1. Background: Why Coding Evaluation Needs New Dimensions

With the rapid evolution of Large Language Models (LLMs) in code generation, traditional programming benchmarks—such as solving algorithmic problems, function completion, and unit test pass rates—can no longer comprehensively reflect a model’s capabilities in real-world engineering scenarios. Developers are increasingly finding that a model might perform exceptionally well on standard algorithmic challenges but show significant divergence when faced with composite tasks requiring a simultaneous understanding of visual intent, spatial geometry, and front-end rendering logic.

The core characteristic of these composite tasks is that they require the model not only to “write runnable code” but also to “write visually correct code.” Typical scenarios include:

• Generating 3D scene rendering code based on natural language descriptions;
• Generating well-structured front-end pages based on design intent;
• Handling geometric transformations and style layouts simultaneously in interactive applications.

Therefore, establishing an evaluation framework that covers “visual correctness + code quality + engineering usability” provides practical reference value for developers and enterprise technology selection.

2. Evaluation Framework Design

2.1 Task Categorization

We divided the evaluation tasks into two main categories, each with several sub-items:

3D Visual Generation Tasks

• Basic geometry combination and transformation (rotation, scaling, translation)
• Lighting and material effect description
• Camera perspective and projection configuration
• Animation and interaction logic

Front-End Visual Generation Tasks

• Layout structure and responsive design
• Component styling and theme consistency
• Dynamic interaction and state management
• Accessibility and semantic markup

2.2 Evaluation Dimensions

Each task is scored based on the following four dimensions:

Dimension	Description	Weight
Visual Fidelity	The degree of match between the generated result and the described intent	30%
Code Correctness	Whether the code runs directly, and the presence of syntax or logic errors	25%
Structural Maintainability	Naming conventions, module separation, comment quality	25%
Performance and Resource Usage	Rendering efficiency, bundle size, runtime overhead	20%

2.3 Testing Methodology

To ensure reproducibility, all tasks utilized a unified prompt template and were executed in the same runtime environment. Code correctness was verified through automated execution, while visual fidelity was assessed through a combination of screenshot comparison and manual review.

3. 3D Visual Generation Analysis

3.1 Basic Geometry and Transformation

During testing, we required the model to generate 3D scene code containing multiple geometries with different transformation matrices applied, based on a description. A typical prompt was:

Create a scene containing a red cube rotated 45 degrees around the Y axis,
and a blue sphere positioned to its right and above. The camera should
observe from a front, slightly elevated angle.

From the generated results, better-performing models could correctly use standard 3D rendering APIs and reasonably set transformation matrices and camera parameters. The code structure was clear, separating geometry creation from transformation logic, making it easy to modify later.

Notably, some models confused rotation sequences (Euler angles vs. quaternions), causing the visual results to mismatch expectations. This highlights the need to pay special attention to spatial mathematical correctness in evaluations.

3.2 Lighting and Materials

Lighting is the part of 3D graphics most prone to “looking wrong.” We tested combined scenes of ambient light, directional light, and point lights to evaluate the model’s understanding of lighting parameters.

Models with excellent performance could:

• Correctly distinguish the applicable scenarios for different light types;
• Reasonably set light intensity and color;
• Configure appropriate roughness and metalness parameters for materials.

Conversely, average-performing models often mixed all light sources indiscriminately or ignored the impact of materials on lighting responses, resulting in flat or overexposed rendering results.

3.3 Animation and Interaction

For animation tasks, we required generating an interaction logic where “a cube smoothly rotates 90 degrees after a button click.” This involves three elements: event binding, easing functions, and state updates.

Testing revealed that models generally performed well in event binding and state updates, but differences were significant in easing function selection and time parameter configuration. Some models directly used linear interpolation, resulting in stiff animations; better-performing models proactively selected appropriate easing curves and explained the visual differences.

4. Front-End Visual Generation Analysis

4.1 Layout and Responsiveness

In front-end tasks, we required generating a page structure based on a text description: “a three-column layout, left navigation with fixed width, adaptive middle content, right sidebar hidden on narrow screens.”

From the generated code, excellent models could:

• Correctly use modern layout solutions (such as Flexbox or Grid);
• Reasonably set breakpoints and media queries;
• Maintain the standard use of semantic tags.

Common issues included over-reliance on absolute positioning, unreasonable breakpoint settings, and ignoring touch target sizes for mobile devices.

4.2 Styling and Theme Consistency

We further tested theme consistency: requiring the generation of style variables including primary color, secondary color, border radius, and shadows, and reusing them across multiple components.

Better-performing models proactively defined and centrally managed style variables, with component styles referencing variables rather than hard-coded values. This not only improved visual consistency but also significantly reduced subsequent maintenance costs.

4.3 Interaction and State Management

In interaction tasks, we required implementing a “tab switching + content lazy loading” component. This requires the model to handle state management and asynchronous loading logic simultaneously.

During testing, some models generated well-structured component code, separating state definitions, event handling, and rendering logic. Others piled all logic into a single function; while functional, the maintainability was poor.

5. Cost-Benefit Analysis

In real-world engineering, model capability is not the only consideration; cost is equally critical. We analyzed the cost-benefit from two perspectives:

5.1 Unit Task Cost

By multiplying the average token consumption per code generation task by the corresponding price, we obtain the unit task cost. On the same task set, the cost difference between different solutions can be several-fold.

5.2 Rework Cost

A more significant hidden cost is the “rework rate”: if the generated code requires extensive modifications to reach a usable state, the overall cost is actually higher despite a low per-generation cost. Therefore, in our evaluation, we specifically recorded the proportion of “first-generation code that passed execution verification directly.”

Overall, for visual programming tasks, choosing a solution with reasonable cost-performance can effectively control costs while ensuring output quality. For budget-sensitive teams, it is recommended to prioritize “first-time pass rate” rather than simply looking at the unit price.

6. Practical Recommendations

Based on the evaluation analysis above, we offer the following recommendations for developers and technical teams:

1. Establish Internal Benchmarks: Build an evaluation set containing both visual and logic tasks based on your specific business scenarios, and conduct regular regression testing.
2. Focus on Visual Correctness: Do not just check if the code runs; verify whether the rendering results meet expectations.
3. Value Code Structure: The maintainability of generated code directly impacts subsequent iteration efficiency and should be given sufficient weight in evaluations.
4. Calculate Comprehensive Costs: Factor in rework costs to avoid being misled by low unit prices.
5. Maintain Technological Neutrality: Base selection on task performance rather than blindly following a single metric.

7. Conclusion

The application of LLMs in programming is shifting from “assisted completion” to “visual-level generation.” 3D graphics and front-end generation tasks impose higher demands on a model’s spatial understanding, code organization, and engineering awareness. By establishing a multidimensional evaluation framework covering visual fidelity, code correctness, structural maintainability, and performance, developers and enterprises can more rationally assess technical solutions and find a balance between quality and cost.

As model capabilities continue to evolve, evaluation frameworks will also need continuous updates to cover more complex engineering scenarios and more refined visual requirements.