Evaluating LLM Code Generation: Task Design and Model Selection Practices

I. Why Use “Directly Executable Tasks” for Evaluation

When debating “which code model is better,” the two most common mistakes are relying on subjective impressions and blindly trusting marketing rhetoric. Code generation has a natural advantage: its results can be objectively verified. It either runs or throws an error; it either meets requirements or misses them. This makes “evaluation” genuinely feasible.

Among various coding tasks, frontend tasks that are “single-file, openable directly in a browser, and visually intuitive” are the best fit for rapid benchmarking for four reasons:

1. Extremely low verification cost: Save the output as an HTML file and open it. You can judge success or failure within seconds without configuring environments or installing dependencies.
2. Rich evaluation dimensions: A complex frontend animation task simultaneously tests requirement comprehension, geometric calculation, programmatic generation, animation performance optimization, interaction design, and code organization.
3. High comparability: Feeding the same prompt to different models produces outputs that can be compared side-by-side, making superiority immediately obvious.
4. Adjustable difficulty: From “a fire-breathing dinosaur” to “a mechanical astronomical clock,” task complexity can be finely controlled to form a clear difficulty gradient.

II. Tiered Design of Evaluation Tasks

Evaluation should start with simple tasks and gradually scale up, focusing on observing at which complexity level a model starts to “fall behind.”

Tier 1: Single Visual Effect

Typical task: Implement a “fire-breathing dinosaur” effect using HTML/CSS/JavaScript. This type of task only tests basic particle and graphic animation capabilities. Mainstream models can generally complete it, making it suitable as a “smoke test” to quickly eliminate obviously unqualified candidates. The conclusion at this tier is often “everyone can do it, and the gap is minimal.”

Tier 2: Complex Structured Animation

Typical task: Implement a “mechanical astronomical clock” using pure SVG, with the constraints of no Canvas, no external images, and final delivery as a single, directly runnable complete HTML file. The prompt for such a task is itself an excellent evaluation template, worth breaking down:

• Structural constraints: Must contain at least 5 concentric circle layers — outer dial ring, star trail ring, gear layer, moon phase layer, and central pointer layer.
• Geometric details: At least 6 gears. Adjacent gears must rotate in opposite directions at different speeds, and must have real tooth shapes; plain circles are not allowed as a workaround.
• Programmatic generation: The 60 minute ticks and 12 hour ticks on the outer ring must be generated via code loops. Hand-writing 72 elements is prohibited.
• Motion and cycles: Light points on the star trails must move smoothly along elliptical orbits; the moon phase dial must transition periodically between new moon, half moon, and full moon; the three central hands must rotate at different speeds.
• Interaction layer: Hovering over different rings should highlight that layer and display a brief label; the control panel must support pause/resume, speed adjustment, and day/night theme switching.
• Engineering and visual constraints: Gradients, shadows, and glows are allowed, but readability must not be sacrificed; code structure must be clear with no repetition; animation performance must be stable.

Each constraint acts as a verifiable checkpoint, effectively eliminating any room for “faking” the result.

III. Scoring Dimensions: Breaking Down “Better” into Quantifiable Metrics

After obtaining the output, it is recommended to score it based on fixed dimensions to avoid “looks cool” impression-based scoring:

Dimension	Key Checks
Executability	Does it throw errors or show a blank screen upon opening? This is the baseline threshold.
Requirement Coverage	Check item-by-item against the prompt; deduct points for any missed requirement.
Code Structure	Is there massive repetition? Are function splits logical? Is naming clear?
Animation Performance	Does it use requestAnimationFrame? Is the frame rate stable? Are there layout reflow risks?
Visual Completeness	Are details refined? Are filters overused to the detriment of readability?
Interaction Integrity	Do hover highlights and the control panel actually work?

A particularly effective “differentiator” is the programmatic generation of ticks. A model that knows how to write loops will generally produce something like this:

const SVG_NS = /* Standard SVG namespace constant */;
for (let i = 0; i < 60; i++) {
  const isHour = i % 5 === 0;              // One hour tick every 5 positions, 12 in total
  const angle = (i / 60) * Math.PI * 2;
  const tick = document.createElementNS(SVG_NS, 'line');
  const r1 = RADIUS_OUT;
  const r2 = isHour ? RADIUS_IN - 6 : RADIUS_IN;   // Hour ticks are longer
  tick.setAttribute('x1', (cx + Math.cos(angle) * r1).toFixed(2));
  tick.setAttribute('y1', (cy + Math.sin(angle) * r1).toFixed(2));
  tick.setAttribute('x2', (cx + Math.cos(angle) * r2).toFixed(2));
  tick.setAttribute('y2', (cy + Math.sin(angle) * r2).toFixed(2));
  tick.setAttribute('class', isHour ? 'tick hour' : 'tick');
  dial.appendChild(tick);
}

In contrast, a lazy model will simply paste dozens of nearly identical lines of tag code. This detail simultaneously exposes programming mindset and engineering habits, reflecting real-world daily coding proficiency far better than any abstract “logic puzzle.”

A common observation in practice: Top-tier models show almost no gap on Tier 1; on Tier 2 tasks involving “stacked multi-constraints,” the stratification truly emerges. Some models can pass all verification points in one go, while others will miss gear rotation logic, moon phase transitions, or interaction details.

IV. Understanding Capability Profiles: Three Easily Overlooked Dimensions

Benchmark scores are just the result. Understanding the underlying capability composition allows you to predict a model’s performance in your actual scenarios.

1. Trade-off between reasoning depth and response speed. Many models currently employ an “adaptive reasoning” strategy: thinking less or not at all for simple tasks and answering directly, while expanding into deep reasoning only for complex tasks. This brings obvious speed benefits but requires users to adapt to changes in output pacing. During evaluation, both “first-token latency” and “answer quality” should be recorded simultaneously, as they often exhibit a trade-off.

2. Context window and “context anxiety.” A million-token long context might seem redundant for daily minor requirements, but its actual value lies in eliminating “context anxiety” — no longer needing to repeatedly agonize over which files to include in the prompt or which history to trim. Models with windows only in the tens of thousands require much finer context management strategies during multi-file refactoring and long-session iterations. Note: the nominal window size does not equal the effective window size. It is recommended to test attenuation empirically using “mid-text detail recall” tasks from long documents.

3. Throughput and accelerated versions. The same model may offer versions with different inference speeds. For interactive coding scenarios, the user experience difference brought by throughput often impacts daily perception more than benchmark score gaps.

V. From Evaluation to Selection: Different Problems for Individuals and Enterprises

A notable real-world phenomenon: Two models may be neck-and-neck in pure capability, but differ massively in “availability and accessibility.”

• Enterprise perspective: Prioritize whether enterprise-grade procurement channels are open, if invoices and corporate billing are supported, if there are Service Level Agreements (SLAs) and data compliance solutions, and if more controllable deployment forms are supported. Some models only offer procurement to enterprises, with virtually no individual channels.
• Individual perspective: Focus on whether personal subscription tiers exist and if quota designs are reasonable. A common situation is that lower-priced tiers have tight quotas, requiring an upgrade to a higher tier for practical cost-effectiveness. There are also models with decent capabilities but no sales channels aimed at individuals whatsoever.
• Reimbursement paths: Even for personal subscriptions, whether an invoice can be issued often determines if the “personal purchase, corporate reimbursement” path is viable — an aspect easily overlooked during selection.

Therefore, when capabilities are comparable, accessibility, billing structure, and compliance become the deciding factors. Enterprise selection is fundamentally a procurement problem; individual selection is fundamentally a cost-effectiveness problem. The answers for the two may be completely different.

VI. A Repeatable Evaluation Workflow

Finally, condensing the above method into an actionable checklist:

1. Prepare three tiers of tasks: Single visual effect -> Complex structured animation -> A task drawn from your real business (e.g., a real page from an internal component library).
2. Fix the prompts: Write a prompt with a checklist of acceptance points for each task. All candidate models must receive the exact same input.
3. Blind scoring: Hide model names and score based on the dimension table to avoid brand bias.
4. Record non-functional metrics: First-token latency, total output time, and empirical long-context performance.
5. Finish with real workflows: Integrate the top two scoring models into your daily development workflow for a week, and let real-world experience make the final call.

In summary: The gap among top-tier models in daily coding is converging. Differences emerge more in complex structured tasks, as well as in engineering配套设施 like context, speed, and procurement channels. Rather than asking “which is the best,” spend an hour building your own benchmark suite — this will always provide a more reliable answer than any leaderboard.