Claude vs GPT-4 for Coding: Which AI Writes Better Code?

Developers have strong opinions about which AI writes better code. Rather than debate theory, we tested Claude 3.5 Sonnet and GPT-4 on real programming tasks to see how they actually compare.

The Test Setup

We tested both models on:

• Code generation from natural language
• Debugging existing code
• Code explanation
• Refactoring suggestions
• Complex algorithmic problems

Languages tested: Python, JavaScript/TypeScript, Go, Rust, SQL

Code Generation

Test: Build a REST API endpoint

Prompt: "Create a REST API endpoint in Python/FastAPI that handles user registration with email validation, password hashing, and returns appropriate error messages."

Claude's output: Clean, production-ready code with proper error handling, Pydantic models, and bcrypt for password hashing. Included helpful comments explaining security considerations.

GPT-4's output: Also correct and production-ready. Included more extensive inline comments but used a slightly different structure. Added rate limiting suggestion unprompted.

Debugging

Test: Find the bug in async code

We provided buggy JavaScript async/await code with a race condition.

Claude's approach: Immediately identified the race condition, explained why it occurred, and provided three different solutions with tradeoffs explained.

GPT-4's approach: Also identified the race condition but initially suggested a solution that would still have edge case issues. When prompted to reconsider, provided a correct fix.

Code Explanation

Test: Explain complex Rust code

We provided a complex Rust function using lifetimes, generics, and trait bounds.

Claude's explanation: Broke it down section by section, explained the "why" behind each lifetime annotation, and related it to memory safety guarantees.

GPT-4's explanation: More thorough overall documentation, included analogies to other languages, but was slightly more verbose.

Algorithm Implementation

Test: Implement a complex algorithm

Prompt: "Implement a least recently used (LRU) cache in Python that's O(1) for both get and put operations."

Claude's implementation: Used OrderedDict for a clean solution. Code was correct and optimal.

GPT-4's implementation: Built a custom doubly-linked list solution, which is technically more "from scratch" but more complex. Also correct and optimal.

Real-World Comparison Table

IDE Integration Matters

Both models are available in coding assistants:

Claude-Based

• Cursor: Full IDE with Claude integration, excellent for refactoring
• Sourcegraph Cody: Good for large codebases

GPT-4-Based

• GitHub Copilot: Best autocomplete, integrated with GitHub
• Cursor: Also supports GPT-4

When to Use Each

Choose Claude when:

• Working with large codebases (200K context window)
• You need clean, production-ready code
• Debugging complex async or concurrent code
• You want concise, to-the-point responses
• Following specific coding standards matters

Choose GPT-4 when:

• Learning new concepts or languages
• Need thorough explanations with analogies
• Working on novel algorithmic problems
• Using GitHub Copilot's ecosystem
• Need detailed documentation generation

The Practical Answer

Here's what most professional developers actually do: use both.

Claude tends to be better for:

• Day-to-day coding tasks
• Refactoring
• Code review assistance

GPT-4 tends to be better for:

• Learning new technologies
• Exploring solution approaches
• Documentation writing

The differences are often subtle enough that the best tool is whichever you have access to and have learned to prompt effectively. Both are genuinely excellent for coding.