How to Evaluate the Overall Capabilities of Next-Generation AI Coding Models

When evaluating next-generation AI coding models, it is not enough to ask whether they can generate runnable code. Nor should their capabilities be judged solely by the visual quality of the first output. In real development tasks, the ability to understand requirements accurately, gather information, break down complex problems, continuously verify results, and iterate based on feedback is often more important than whether the first version simply works.

This article examines a representative web animation recreation task. Given a webpage featuring particles, aggregation, dispersion, scroll-linked transitions, and mouse interaction, the model is asked to build a standalone demo that reproduces the original visual experience as closely as possible. Although this appears to be a frontend development task, it also tests visual analysis, interaction design, browser debugging, animation algorithms, and software organization.

Why Web Animation Recreation Is a Useful Evaluation Task

Traditional coding problems usually have clearly defined inputs, outputs, and validation criteria. Web animation recreation is more complex because it involves both understanding and implementation.

First, the model must identify the page’s key visual elements. For example, the initial viewport may display a shape with a specific outline. As the user scrolls, the shape may gradually break apart into many particles. With further scrolling, the particles may regroup into another form. Mouse movement may also change the particles’ repulsion range, direction, opacity, or speed. These behaviors cannot be reduced to “add a particle animation.” They must be decomposed into multiple implementable states.

The model must also understand the timing relationships behind the animation. A complete interactive animation often includes an initial state, a transition state, a target state, and a user-interaction state. Scrolling does not simply trigger an animation once; it maps the page position to the animation progress. As a result, the particles’ positions, opacity, scale, and arrangement should change continuously as the user scrolls.

Visual recreation also involves subjective evaluation. A page may run correctly, but if its particle density, shape outline, motion rhythm, background depth, or mouse response differs significantly from the original, users will still consider the recreation unsuccessful. This makes the task effective for distinguishing between a model that can generate code and one that can reproduce a design and its underlying interaction logic.

What Capabilities Does a Complete Task Require?

1. Requirement Understanding

A high-quality model should not merely repeat the user’s description. It should actively extract the task’s constraints.

For example, the requirements can be divided into the following areas:

• The page should run independently without a complex backend environment.
• The animation should be connected to page scrolling.
• Particles should transition continuously between aggregated and dispersed states.
• Particles should form recognizable text, shapes, or logo-like outlines.
• Mouse movement should influence nearby particles.
• The page should have visual depth rather than displaying a flat field of single-color dots.
• The code should be easy to tune and should support replacing the target shape.

If the model fails to decompose these requirements, it will often generate a generic particle background. Such a result may look animated, but it does not implement the page mechanism the user actually requested.

2. Page Analysis

In real development work, a model may inspect a page through a browser, or analyze screenshots, video, or a written description. The available information directly affects the final result.

When the page is accessible, the model should examine:

• Which elements change as the page scrolls;
• Whether the animation maps linearly to the scroll position;
• Whether the number and distribution of particles remain fixed;
• Whether particles are sampled from an image, text, or geometric shape;
• Whether mouse interaction affects position, velocity, or opacity;
• Whether the page contains multiple animation stages;
• How the layout changes across screen sizes.

When analysis is based only on screenshots, the model can usually recreate the static composition but may struggle to infer the motion rules accurately. An effective AI coding tool should clearly distinguish between observed facts and experience-based assumptions instead of treating guesses as the page’s actual implementation.

3. Animation Algorithm Design

The core of a particle animation is usually not a particular framework, but its data structures and state transitions.

A general implementation can store the following information for each particle:

Current position: x, y
Target position: targetX, targetY
Origin position: originX, originY
Velocity: velocityX, velocityY
Size: radius
Opacity: alpha
Random offset: noiseX, noiseY

When the scroll progress changes from 0 to 1, the particle’s current position can be calculated through interpolation:

currentX = originX + (targetX - originX) * progress
currentY = originY + (targetY - originY) * progress

To avoid a mechanical-looking animation, an easing function can be added. For example, a smooth-step function makes the beginning and end of the transition more natural:

smoothProgress = progress * progress * (3 - 2 * progress)

Mouse interaction can be based on distance. When a particle is within a defined radius of the pointer, a repulsion or attraction force can be applied:

distance = sqrt(dx * dx + dy * dy)
if distance < interactionRadius:
    force = (interactionRadius - distance) / interactionRadius
    particle.x += dx * force * strength
    particle.y += dy * force * strength

These algorithms are not particularly complex, but the model must understand the relationships between the variables. A common problem in low-quality implementations is that particle position, scroll progress, and mouse influence are handled independently, causing the animation states to conflict.

Why First-Generation Outputs Are Often Unsatisfactory

The first version generated by an AI coding model can usually establish the page structure quickly, but it may differ considerably from the target experience for several reasons.

1. Overreliance on Generic Templates

Many models choose familiar implementation patterns first, such as drawing random particles on a canvas and adding simple mouse repulsion. This approach is fast to start, but it only solves the problem of creating “a particle animation.” It does not solve the problem of making particles form a specific shape.

If the target is text or a complex outline, particles should be sampled from the target shape’s pixel data rather than generated randomly. A more suitable workflow is:

1. Draw hidden text or a hidden shape.
2. Read its pixel data.
3. Extract non-transparent pixels at a fixed interval.
4. Convert those pixel coordinates into target particle positions.
5. Move particles from dispersed positions toward the target during the animation.

2. Insufficient Visual Calibration

Even when the algorithm is correct, inappropriate particle counts, colors, sizes, or distributions can create a significant visual mismatch. At a minimum, the following parameters should be calibrated:

• The proportion of the main shape within the viewport;
• Overall particle density;
• The particle size range;
• Background brightness and gradient direction;
• The duration of the aggregation phase;
• The maximum range of dispersion;
• The mouse interaction radius;
• The amount of randomness and damping in the motion.

Without this calibration, the model will often produce an implementation that is functionally correct but visually ordinary.

3. No Feedback Loop

High-quality development is not a one-shot generation process. It requires observing the result, making changes, and observing it again. The model should compare screenshots or runtime behavior with the target and identify differences such as:

• The shape outline is too blurry;
• There are too few particles;
• The aggregation process lacks visual depth;
• Scroll response is not smooth enough;
• The mouse interaction area is too large;
• The page is cropped on mobile devices;
• The frame rate drops during animation.

If the model only generates code without validating the result, its role is closer to code completion than to that of a complete development agent.

How to Build More Objective Evaluation Criteria

Complex web tasks can be evaluated across five dimensions.

1. Functional Completeness

Can the page load correctly? Does the animation start? Do scrolling and mouse interaction work? Does the page satisfy the requirement to run independently? These are the most basic criteria.

2. Visual Similarity

The evaluation should consider composition, color, particle density, target outlines, animation rhythm, and spatial depth. Visual similarity should not be judged from only one frame; multiple animation stages should be examined.

3. Interaction Quality

Is scrolling continuous? Does mouse movement cause abrupt jumps? Can particles smoothly return to their target positions after being disturbed? Does the page remain stable when users interact quickly?

4. Engineering Quality

Is the code modular? Are parameters managed centrally? Is there duplicated logic? Does the implementation account for high-resolution displays, window resizing, and mobile devices? Good code should not only produce a visual effect but also support future maintenance.

5. Iteration Efficiency

Can the model quickly locate problems based on feedback and improve the result in relatively few iterations? In practical development, iteration efficiency is often more valuable than the ability to produce an impressive first draft.

How Companies and Developers Should Use These Tools

For real projects, it is not advisable to hand over a complex page to a model and ask it to generate everything in one step. A staged collaboration process is more reliable.

In the first stage, ask the model to produce a requirement breakdown and technical plan instead of writing the complete implementation immediately. Confirm the animation states, data structures, and interaction logic before moving to development.

In the second stage, build the static page and basic particle system. Verify that canvas dimensions, device pixel ratio, and the rendering loop are correct.

In the third stage, add shape sampling and scroll mapping so that particles can form a clear target.

In the fourth stage, add mouse interaction, easing, damping, random perturbations, and performance optimization.

In the fifth stage, conduct acceptance testing through screenshots, screen recordings, or hands-on evaluation. Ask the model to address specific problems rather than giving a vague instruction to “optimize it further.”

Teams can also include this type of task in an internal evaluation system. By using consistent pages, interaction requirements, and acceptance criteria, they can track different AI coding solutions over time. When necessary, the evaluation can also incorporate code repositories, test environments, logging systems, and monitoring capabilities to observe how models perform within real engineering workflows.

Conclusion

Evaluating next-generation AI coding models should not focus only on whether they can write code that appears complex. The more important questions are whether they can turn ambiguous visual requirements into a clear technical plan, observe and validate results through a browser, identify the differences between an initial implementation and the target, and continuously improve the output through multiple iterations.

Web animation recreation is only one example. The same evaluation approach can be applied to data visualization, interactive administration systems, mobile interfaces, 3D presentations, and complex business pages. The value of an AI coding model does not lie in replacing developers entirely. It lies in reducing exploration costs, accelerating validation, and allowing developers to focus more on architecture, user experience, and engineering decisions.

Therefore, the most useful evaluation of an AI coding tool should go beyond asking whether it can write code. It should also ask whether the tool understands the problem, can verify the problem, can continuously correct the problem, and can ultimately deliver a usable, maintainable result that meets expectations.