Evaluating LLM Coding Capabilities: A Guide to 3D and Frontend Visual Testing

I. Why Hands-On Testing Matters Beyond Leaderboards

Public benchmark leaderboards are an essential starting point for understanding model capabilities, but relying solely on them for model selection introduces three systematic biases:

1. Score Saturation and Reduced Discrimination: Mainstream models are scoring near the ceiling on classic coding benchmarks. A fraction of a point difference between top-tier models rarely reflects the actual user experience.
2. Data Contamination Risks: Public evaluation questions likely leak into training corpora. Models might “memorize the answers,” leading to inflated scores.
3. Task Format Misalignment: Leaderboards mostly feature single-turn, atomic tasks. Real-world development is a complex scenario involving long contexts, multiple constraints, and iterative refinement.

Therefore, a more reliable approach is to build a private, hands-on evaluation process tailored to your business. By using test cases close to real tasks, reproducible execution environments, and clear scoring criteria, you can measure the “usable code output per unit cost.” This is especially critical for frontend visuals and 3D graphics—domains that are highly subjective and demand comprehensive model capabilities.

II. Evaluation Dimensions: Breaking “Usability” into Measurable Metrics

“Whether the code is good” is a vague judgment. Before testing, it must be broken down into independently scorable dimensions.

1. Code Correctness
The most fundamental dimension: Can the code run directly, and is the logic correct? This can be refined into objective metrics like compilation/execution pass rates, test case pass rates, and boundary condition handling.

2. Engineering Quality
Running doesn’t mean usable. You need to inspect: Is module division logical? Are naming and comments clear? Are exception paths handled? Are unnecessary dependencies introduced? This dimension typically requires manual review or static analysis rules.

3. Frontend Visual Fidelity
Given a design description or reference image, evaluate the fidelity of page layout, spacing, color schemes, and fonts, as well as the completeness of interaction logic (form validation, state toggling, animations). The visual dimension is highly subjective; a multi-person blind review averaging scores is recommended.

4. 3D and Graphics Capabilities
This is a comprehensive test of the model’s “spatial imagination + math proficiency + graphics API familiarity,” which can be broken down into:

• Scene Construction: Correctness of coordinate systems, cameras, lighting, and material configurations.
• Mathematical Correctness: Rigor of rotation matrices, interpolations, and trajectory calculations.
• Interaction and Animation: Rationality of event binding, animation loops, and frame rate control.
• Performance Awareness: Avoidance of common anti-patterns like recreating objects every frame.

5. Instruction Following and Iteration
Does it strictly adhere to constraints like “only use standard libraries,” “single-file delivery,” or “code only, no explanations”? In multi-turn defect fixing, can it accurately locate the problem without introducing regressions?

III. Test Case Construction: A Three-Tier Task Pyramid

It is recommended to organize test cases into three tiers: Atomic Tasks → Composite Tasks → Project-Level Tasks.

• L1 Atomic Tasks: Implementing a single function or component, such as “write a debounce function” or “implement a sortable table.” Used for quick baseline testing; low cost and batch-executable.
• L2 Composite Tasks: Stateful complete pages, such as “Login page: form validation + loading state + error prompts” or “Data dashboard: chart rendering + filter linkage.” This tier offers the highest discrimination.
• L3 Project-Level Tasks: Multi-file, multi-turn iterations simulating requirement changes and bug fixes in a real repository, focusing on long context and engineering capabilities.

For 3D and frontend visuals, test case design has two key requirements:

First, explicit constraints. For example, requiring “a single HTML file, relying only on native browser capabilities (e.g., WebGL), no external network resources.” This facilitates sandbox execution and eliminates network interference.

Second, verifiable acceptance criteria. Translate “looks good” into checkable items, such as: Does the cube continuously rotate around two axes? Does the particle count meet the requirement? Is the layout responsive after window resizing?

A standardized prompt template can significantly improve result comparability:

Please implement a webpage: {requirement_description}
Hard constraints:
1. Output only one complete HTML file with inline styles and scripts;
2. Do not reference any external resources;
3. Do not include explanatory text in the code.
Acceptance criteria:
{List verifiable acceptance criteria line by line}

IV. Automated Evaluation Pipeline: Making Results Reproducible

Manual testing is slow and irreproducible. It is recommended to build a minimum viable pipeline: Batch Calls → Code Extraction → Sandbox Execution → Result Collection → Scoring and Archiving.

import pathlib
PROMPT = """Please implement a webpage: {requirement}
Hard constraints: Single HTML file, inline styles and scripts, no external resources.
Output only the complete code, no explanations."""
def extract_code(text: str) -> str:
    """Extract code block from model output"""
    ...
def run_in_sandbox(html: str) -> dict:
    """Render the page in an isolated environment,
    collect console errors, runtime duration, and page screenshots"""
    ...
def evaluate(case: dict, model: str) -> dict:
    resp = call_model(model, PROMPT.format(**case))
    result = run_in_sandbox(extract_code(resp))
    result["auto_checks"] = check_acceptance(result, case["checks"])
    result["human_scores"] = blind_review(result["screenshot"])
    return result

Scoring should adopt a dual-track system: “Objective Metrics + Blind Review”:

• Objective Part: Does the page render without errors? Pass rate for each acceptance criterion? Can the code pass static analysis?
• Subjective Part: Screenshots are anonymized regarding model source and scored independently by multiple reviewers (e.g., 1-5 for fidelity and aesthetics). The average is taken to avoid bias.

All intermediate artifacts (raw output, screenshots, error logs) should be uniformly archived, such as in object storage, with evaluation metadata logged for post-hoc tracing and version comparison. This pipeline has no special platform requirements; it can be built using general capabilities like function computing, containers, and object storage on any cloud platform. For instance, using Baidu AI Cloud’s general computing, storage, and model service components, the entire process can be completed at a low cost.

V. Cost-Benefit Analysis: Spending Wisely

The other half of hands-on testing’s value lies in answering “is it worth it?” Two core metrics are recommended:

• Single Task Cost ≈ Input token price × Input volume + Output token price × Output volume (including failed retries);
• Usable Deliverables per Dollar = Number of tasks passing acceptance / Total cost.

It is common in the industry to see call prices differ several-fold within the same capability tier. This means that when capabilities are similar, cost differences will be amplified by business scale. A tiered selection strategy is recommended in practice:

1. Prototyping Phase: Use cost-effective models for rapid trial and error, accepting a higher rework rate.
2. Critical Paths (Core business logic, complex 3D scenes): Use the most capable models to reduce rework and integration costs.
3. Routine Tasks (Simple components, style adjustments, comment completion): Delegate to the cost-effectiveness tier.

Additionally, cost-reduction measures are equally important: trimming irrelevant context, constraining output length, caching fixed prefixes, and offline batch processing evaluation tasks can typically yield substantial savings.

VI. Common Pitfalls and Avoidance Checklist

1. Drawing conclusions from a single sample: Generation is random; results from the same prompt can vary greatly across runs. Sample at least 3-5 times per task and calculate the pass rate rather than looking at only the best result.
2. Test cases too simple or too obscure: Too simple yields no discrimination; too obscure detaches from business. Cases should be adapted from real requirements.
3. Testing only the first turn, ignoring iteration costs: In real engineering, “how many rounds it takes to fix” is as important as “right on the first try.” Fix rounds should be factored into the evaluation conclusion.
4. Ignoring engineering factors: Long context, multi-file modifications, tool calling, and streaming output stability all impact the real experience and should be included in the assessment.
5. Using public question banks to evaluate latest models: Data contamination risks exist. Conclusions drawn from private, self-built test cases are more credible.

VII. Conclusion

Model selection should not be “checking leaderboards + going with a gut feeling,” but a data-driven engineering practice: use tiered test cases close to real business to cover code, frontend visual, and 3D graphics capabilities; use an automated pipeline to ensure reproducible results; and use cost-benefit metrics to convert “capability” into “output per dollar.” Teams should treat evaluation sets as continuously accumulating assets—add a test case after every pitfall, and run regular regression tests on new models. This way, whether a new model is released or prices adjust, the team can produce its own conclusions within days rather than being led by external noise.