A Practical Evaluation Framework for LLM Coding Ability: Model Selection for 3D and Frontend Workloads

In recent years, public evaluation scores for large language models in code generation have continued to reach new highs. Yet many teams that select models based on leaderboard rankings find that their real-world project experience falls far short of expectations. This is particularly true in scenarios such as 3D visualization and frontend page generation, where both logical correctness and visual quality matter. In such cases, benchmark scores alone make it hard to judge whether a model is truly suitable for production environments. Starting from engineering practice, this article presents a quantifiable, reproducible methodology for empirically evaluating LLM coding ability.

1. Why Leaderboard Scores Cannot Directly Guide Model Selection

Before building an evaluation system, it is important to understand the three mismatches between public benchmarks and real engineering work:

1. Task distribution mismatch: Public benchmarks mostly consist of algorithm problems and function-level completions, whereas real frontend development involves composite tasks covering page structure, styling, interaction, state management, and error handling. The capability dimensions these two assess do not overlap.
2. Visual and interaction quality is hard to capture with automated metrics: Between a page that “runs” and a page that “looks good and works well” lies a large amount of detail that requires human judgment, such as layout hierarchy, color harmony, and animation smoothness.
3. Hidden costs are ignored: A model may produce high-quality output in a single pass, but if it requires multiple rounds of correction to converge, the accumulated token consumption and human waiting time may make the overall cost higher than a superficially “more expensive” alternative.

Therefore, an empirical methodology close to real scenarios has more decision-making value than relying solely on third-party benchmark scores.

2. A Five-Dimension Evaluation System: Breaking “Usability” into Measurable Indicators

It is recommended to break down coding ability evaluation into five dimensions, each scored and archived independently:

1. Code correctness: Whether the generated result can directly compile or parse, whether runtime errors occur, and whether the core logic matches the requirement description. This is a baseline indicator that must be satisfied first in any scenario.

2. Engineering quality and maintainability: Whether the code structure is clear, naming is standardized, necessary comments are present, and exception handling is in place. Readability can be tested by having another engineer read the code without looking at the prompt.

3. Visual fidelity: For frontend tasks, compare the generated page against the design mockup or text description item by item, examining the fidelity of layout, spacing, color, fonts, and other details. A 1–5 scoring scale is recommended, with reviewers required to write down reasons for deductions.

4. 3D and interaction complexity: Examine the model’s use of browser-native rendering capabilities such as WebGL, including whether lighting, shadows, camera controls, drag-to-rotate, scroll-to-zoom, and other interactions are complete and usable, and whether frame rates are stable.

5. Instruction following and output stability: Run the same test case multiple times (e.g., 3–5 times) and calculate the satisfaction rate for each requirement. Models with poor stability will “improvise” at critical moments, bringing risks to engineering integration.

3. Building Test Cases in Layers: Atomic, Composite, and Project Levels

How test sets are constructed matters more than their quantity. It is recommended to organize them at three levels:

• Atomic level: Single feature points, such as “implement a debounced search box” or “draw a bar chart with a legend,” used to locate the boundaries of a model’s basic capabilities.
• Composite level: Multiple feature points stacked together, such as “a data table page with filtering, sorting, and pagination,” examining the model’s ability to manage complex requirements.
• Project level: Tasks close to real deliverables, such as a complete 3D showcase page. Below is a prompt template that can be reused directly:

Using only a single HTML file (with inline CSS and JavaScript), implement a 3D data showcase page:
1. The scene contains a cube that can be rotated by dragging and zoomed by scrolling;
2. Each face of the cube displays simulated real-time data, refreshed once per second;
3. Include basic lighting and shadows, a dark theme, and a data legend at the bottom of the page;
4. Do not depend on any external build tools or network resources; it should run when opened directly in a browser;
5. The code must include necessary comments, and key functions must handle invalid input.

Requiring “single file, zero dependencies, opens and runs” naturally eliminates environmental interference, making the outputs of different models comparable. For execution, it is recommended to fix the browser version and resolution, and record a screen capture of each run for archival purposes.

4. Combining Quantitative Metrics with Human Scorecards

Automated metrics are responsible for objective facts, and human review is responsible for subjective experience; neither can be omitted. For the automated part, at minimum track: compile pass rate, number of runtime errors, number of console warnings, and requirement satisfaction rate.

For the human part, use a structured scorecard and store each review result in a database for long-term tracking:

{
  "task_id": "fe-3d-cube-001",
  "runs": 3,
  "compile_pass_rate": 1.0,
  "runtime_error_count": 0,
  "visual_fidelity": 4.5,
  "interaction_score": 4.0,
  "code_quality": 4.0,
  "avg_revision_rounds": 1.3,
  "avg_token_cost": 18200
}

To reduce subjective bias, it is recommended to use two reviewers working back-to-back, and to hold a calibration meeting to align standards when the score difference exceeds 1 point. If the team has no dedicated reviewer, an alternative is to have one model under test generate the code, then have another model act as the “reviewer” to give structured feedback, followed by manual spot-check calibration.

5. Cost-Effectiveness Analysis: Bringing Price into the Evaluation Formula

No matter how strong a model’s coding ability is, it must ultimately answer the question of whether it is worth the cost. Two concepts are recommended:

• Total cost per task: First-generation cost + accumulated cost of all correction rounds;
• Value-for-money score: Weighted total score divided by total cost per task.

def value_for_money(records):
    """
    records: all task records for a model under test,
    each containing weighted_score and total_cost (including correction rounds)
    """
    total_score = sum(r["weighted_score"] for r in records)
    total_cost = sum(r["total_cost"] for r in records)
    return total_score / total_cost  # score obtained per unit cost; higher is better

In practice, it is often found that some models lag slightly on flagship capabilities but, because they are an order of magnitude cheaper and require fewer correction rounds, offer significantly better overall value for money. For most non-extreme-performance scenarios, such high-value models are often the more reasonable default choice, while flagship models are reserved for critical tasks on core paths.

6. Implementation Process and Engineering Recommendations

1. Unified access and scheduling: Large model service platforms, such as Baidu AI Cloud, typically provide unified multi-model access and call observability capabilities. The same test set can be distributed in parallel to multiple models under test, ensuring identical prompts and parameters.
2. Fix randomness: Fix sampling parameters such as temperature during evaluation to prevent result jitter from masking real differences.
3. Establish a regression mechanism: Model versions iterate frequently. It is recommended to run the same test set as a regression once a month, forming a capability curve rather than a single-point snapshot.
4. Logging and alerting: Connect evaluation call latency, token consumption, and failure rates to logging services and monitoring alerts, and intervene promptly when anomalies occur.

7. Common Pitfalls

• Judging by a single question: One impressive result or one failure does not represent true capability; always look at the statistical distribution across multiple runs.
• Looking only at first-pass success rate: Ignoring correction rounds severely underestimates the real cost of use.
• Ignoring prompt sensitivity: A model that is overly sensitive to prompt wording will cause substantial hidden friction in team collaboration.
• Mistaking demo quality for engineering ability: A polished demo does not equal maintainable code; engineering quality must be scored separately.

8. Conclusion

Evaluating LLM coding ability is essentially a testing engineering effort: use layered test cases to simulate real task distributions, use automated metrics to anchor correctness, use human scorecards to capture visual and interaction experience, and use a cost model to translate “capability” into “value for money.” After building an evaluation pipeline according to the five-dimension system and three-tier test cases described in this article, teams can quickly complete objective re-testing each time a new model is released, basing model selection decisions on reproducible data rather than marketing benchmark scores.