Best LLM for JavaScript Coding With Examples

As large language models (LLMs) continue evolving, developers are increasingly turning to them to assist with complex coding tasks, including building JavaScript apps from scratch. Whether you’re building a simple Todo App in React or an interactive Snake Game in Three.js, choosing the right LLM can make a significant difference in code quality, performance, and ease of use.

Why Use LLMs for JavaScript Development?

JavaScript is the most widely used programming language today, powering both front-end and back-end applications. LLMs trained on vast codebases can:

• Autogenerate boilerplate code

• Refactor and debug complex logic

• Explain errors and suggest improvements

• Speed up the learning process for frameworks like React and Three.js

But which LLM actually performs best in hands-on coding scenarios?

Example 1: React Todo App

Objective: Build a functional Todo app using React, with features like state management, adding/removing tasks, and data persistence (e.g., localStorage).

What We Tested:

• JSX generation accuracy

• React hooks usage (e.g., useState, useEffect)

• Component modularization

• LocalStorage integration

• Real-time code correction

Example 2: Snake Game Using Three.js

Objective: Create an interactive 3D Snake Game using Three.js with basic controls, collision detection, and scoring.

What We Tested:

• Scene setup with THREE.Scene()

• Mesh manipulation

• Game loop logic using requestAnimationFrame

• Keyboard control handling

• Code readability and frame rate optimization

GPT-4o (OpenAI): The Most Reliable for Full-Stack JS

GPT-4o is OpenAI’s latest multimodal model, and it’s an absolute powerhouse for developers. It excels at both frontend and backend JavaScript workflows.

Key Strengths:

• Perfect handling of React lifecycle and hook logic

• Accurate Three.js rendering suggestions

• Smooth debugging and async logic handling

• Super responsive in multi-turn conversations

Verdict: Best all-around LLM for JavaScript development. Great for full-stack apps and game logic.

Claude 3.5 (Anthropic): Human-Like Code Reasoning

Claude 3.5 Sonnet brings near-human reasoning to code, especially strong in maintaining coding style and structure.

Key Strengths:

• Clean, modular code output

• Excellent at breaking down component logic

• Intuitive React explanations

• Slightly weaker on 3D logic with Three.js but improving

Verdict: Best for clean, readable React codebases. Not ideal for 3D-heavy tasks.

Gemini 1.5 Pro (Google): Best for Collaborative Coding

Gemini 1.5 Pro integrates beautifully with Google’s ecosystem and supports long-context coding with strong JavaScript comprehension.

Key Strengths:

• Long context handling—great for large projects

• Strong React documentation support

• Solid with vector-based graphics in Three.js

Verdict: Ideal for larger codebases and team collaboration, though its 3D logic is slightly behind GPT-4o.

DeepSeek R3: Fast but Inconsistent

DeepSeek R3 is a lean, code-specialized model that offers fast performance but lacks some of the nuance in complex JavaScript tasks.

Key Strengths:

• Quick at boilerplate code

• Handles basic React apps decently

• Struggles with complex 3D logic or async operations

Verdict: Decent for junior developers or boilerplate React work, not ideal for advanced game logic.

Grok (xAI): Needs Work for JavaScript

xAI’s Grok is Elon Musk’s answer to LLMs, optimized for humor, creativity, and brevity. While Grok is promising in conversations, it’s still catching up in software development tasks.

Key Strengths:

• Basic JavaScript output

• Passable for simple UI logic

• Weak Three.js support and sparse debugging insights

Verdict: Fun for creative prompts, not reliable for serious JavaScript development yet.

Which LLM Should You Use?

GPT-4o: The All-Around Performer

GPT-4o represents OpenAI’s most advanced multimodal model, offering exceptional capabilities for JavaScript developers working with React and Three.js.

Strengths:

• Superior code generation quality across both React component architecture and Three.js scene setup
• Excellent understanding of 3D mathematics and shader programming
• Strong ability to debug complex rendering issues and performance bottlenecks
• Comprehensive knowledge of modern JavaScript frameworks and libraries
• Multimodal capabilities allow for analyzing screenshots, diagrams, and visual references

Best for:

• Complex Three.js implementations requiring custom shaders
• React applications with sophisticated state management
• Projects requiring both UI development and 3D visualization
• Developers who need reliable, production-ready code suggestions

Limitations:

• Higher API costs compared to alternatives
• May generate overly complex solutions for simple problems

Claude 3.5: The Frontend Specialist

Claude 3.5 excels at producing clean, readable, and well-structured frontend code, making it particularly valuable for React developers.

Strengths:

• Exceptional at generating modular, maintainable React components
• Superior documentation within code comments
• Strong understanding of React best practices and patterns
• Excellent at explaining complex concepts in accessible language
• Produces highly readable code with consistent styling

Best for:

• Frontend-focused projects prioritizing code quality and maintainability
• Teams with varying experience levels who need well-documented code
• Projects requiring extensive component libraries
• Developers who value clean architecture and separation of concerns

Limitations:

• Less proficient with complex Three.js implementations
• May struggle with advanced WebGL concepts compared to GPT-4o

Gemini 1.5 Pro: The Collaboration Expert

Google’s Gemini 1.5 Pro offers unique advantages for team-based development and large-scale projects.

Strengths:

• Excellent integration with Google’s development ecosystem
• Superior handling of large codebases and project context
• Strong understanding of build systems and deployment workflows
• Good at generating code that follows established project patterns
• Effective at suggesting refactoring approaches for legacy code

Best for:

• Large teams working on collaborative projects
• Codebases requiring integration with Google Cloud services
• Projects with complex build and deployment requirements
• Developers working within Google’s ecosystem

Limitations:

• Less specialized knowledge of Three.js compared to GPT-4o
• May produce less elegant solutions for complex UI challenges

DeepSeek R3: The Rapid Prototyper

DeepSeek R3 offers a balance of speed and capability that makes it ideal for quick prototyping and learning.

Strengths:

• Faster response times than larger models
• Good understanding of fundamental JavaScript concepts
• Capable of generating functional code examples quickly
• Cost-effective for high-volume usage
• Suitable for educational purposes and skill development

Best for:

• Rapid prototyping and proof-of-concept work
• Developers learning JavaScript, React, or Three.js
• Projects with limited budget constraints
• Situations requiring quick iterations and feedback

Limitations:

• Less sophisticated understanding of advanced patterns
• May produce code requiring more manual refinement
• Not ideal for production-ready implementations

Grok: The Experimental Option

Grok represents an interesting but less mature option in the LLM landscape for JavaScript development.

Strengths:

• Novel approaches to problem-solving
• Can generate unconventional solutions
• Faster response times for simple queries
• May offer unique perspectives on coding challenges

Best for:

• Experimental projects and tinkering
• Generating alternative approaches to solved problems
• Developers interested in exploring different coding styles
• Non-critical applications where reliability is less important

Limitations:

• Less consistent code quality
• Limited understanding of complex JavaScript ecosystems
• May produce solutions that don’t follow best practices
• Not recommended for production environments

Decision Framework

When selecting an LLM for your JavaScript development needs, consider:

1. Project complexity: More complex projects benefit from GPT-4o or Claude 3.5
2. Team size and collaboration needs: Larger teams may prefer Gemini 1.5 Pro
3. Budget constraints: DeepSeek R3 offers good value for limited budgets
4. Learning vs. production: Learning environments can use simpler models like DeepSeek R3
5. Specific technical requirements: Three.js projects benefit most from GPT-4o’s capabilities

The ideal approach may involve using different models at different stages of development, leveraging each for its particular strengths.

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