Claude Sonnet 4 vs GPT-4o for Coding: Which AI Assistant Writes Better Python?

When GitHub’s 2024 Octoverse report revealed that Python had overtaken JavaScript as the most-used language on the platform, it confirmed what many developers already suspected: Python is now the lingua franca of software development. But as AI coding assistants become increasingly embedded in daily workflows, a new question has emerged—not just what you code, but which AI you code with.

Two models dominate the conversation: Anthropic’s Claude Sonnet 4 and OpenAI’s GPT-4o. Both are multimodal, both handle context windows in the hundreds of thousands of tokens, and both claim to be exceptional at code generation. But when you strip away the marketing and run them through real Python tasks, clear differences emerge.

I spent two weeks testing both models across a range of Python scenarios—from algorithmic challenges to refactoring legacy code, from writing unit tests to debugging subtle concurrency bugs. Here’s what I found.

The Setup: A Fair Fight

To keep the comparison honest, I used the same prompts for both models through their respective APIs (Claude Sonnet 4 via Anthropic’s API, GPT-4o via OpenAI’s API) with temperature set to 0.2 for reproducibility. I tested five categories:

  1. Algorithm implementation (data structures, sorting, search)
  2. Refactoring (improving existing, messy code)
  3. Test generation (pytest and unittest)
  4. Debugging (finding and fixing bugs in provided code)
  5. Architecture (designing a small application from a high-level description)

Each task was scored on correctness, code quality, readability, and efficiency. I also timed how long each model took to produce a response.

Algorithm Implementation: Close Race, Different Styles

I started with a classic: implementing a thread-safe LRU cache from scratch, then a more involved task—writing a function to find the median of two sorted arrays in O(log(min(n, m))) time.

Both models produced correct, working solutions. But their approaches revealed distinct philosophies.

GPT-4o leaned toward concise, idiomatic code. Its LRU cache used OrderedDict from collections, which is the standard Pythonic approach, and it handled the edge cases (cache misses, capacity overflow) cleanly. The median-of-two-arrays solution was textbook binary search, with clear variable names and a solid explanation of the logic.

Claude Sonnet 4 took a slightly different tack. Its LRU cache also used OrderedDict, but it added a __repr__ method and type hints on every function signature. The median solution was equally correct, but Claude included a step-by-step walkthrough of the algorithm before showing the code—useful for learning, but slower if you just want the answer.

Verdict: Both are excellent. GPT-4o is slightly more direct; Claude Sonnet 4 is more pedagogical. For production code, either works. For learning, Claude has the edge.

Refactoring: Where Claude Sonnet 4 Shines

This was the most striking difference in the entire test.

I provided both models with a 200-line Python script that processed CSV files—complete with nested conditionals, duplicated logic, inconsistent naming conventions, and a few subtle bugs. The prompt: “Refactor this code to be cleaner, more maintainable, and add docstrings.”

Claude Sonnet 4 delivered a masterclass in refactoring. It:

  • Extracted the CSV processing logic into a dedicated class with clear responsibilities
  • Replaced a deeply nested if/elif chain with a lookup dictionary
  • Added comprehensive docstrings and type hints throughout
  • Introduced pathlib.Path instead of string concatenation for file paths
  • Caught a subtle bug where the original code mishandled empty rows

The refactored code was shorter, faster, and significantly more readable. It felt like something a senior engineer would write after a code review.

GPT-4o produced decent refactored code, but it was more conservative. It cleaned up the naming, added some docstrings, and flattened a few conditionals—but it missed the structural issues. The nested logic remained nested, the class abstraction wasn’t introduced, and the empty-row bug went unnoticed.

Verdict: Claude Sonnet 4 is the clear winner for refactoring. It demonstrated a deeper understanding of code structure and best practices, going beyond surface-level cleanup to address underlying design issues.

Test Generation: A Tie with Different Strengths

Writing unit tests is where AI assistants earn their keep—it’s tedious, repetitive, and easy to delegate.

I asked both models to write pytest tests for a utility module that handled date parsing, string manipulation, and JSON validation. The module had known edge cases that good tests should catch.

GPT-4o generated 45 test cases covering a wide range of scenarios. Its tests were well-organized, used fixtures appropriately, and included parametrized tests for the date parsing function. It also correctly identified that the JSON validation function had a bug when handling nested dictionaries.

Claude Sonnet 4 generated 38 test cases, but they were more focused. It spent more time on the edge cases—empty strings, invalid dates, malformed JSON—and less on happy-path testing. Its tests were slightly more verbose, but each one had a clear purpose. Notably, Claude also caught the same nested-dictionary bug that GPT-4o found.

Verdict: Statistically, GPT-4o produced more tests. Qualitatively, Claude’s were more targeted. For most teams, either would be a significant upgrade over writing tests by hand.

Debugging: The Decisive Test

Debugging is where AI assistants either prove their worth or expose their limitations. I gave both models a Python script with five intentional bugs:

  1. A race condition in a multithreaded counter
  2. An off-by-one error in a loop boundary
  3. A mutable default argument in a function signature
  4. A memory leak due to an unclosed file handle
  5. An incorrect regex pattern that failed on valid input

Claude Sonnet 4 found all five bugs, but more impressively, it explained why each was a problem and provided context on the underlying Python semantics. For the race condition, it suggested using threading.Lock and explained why the Global Interpreter Lock (GIL) doesn’t protect compound operations. For the mutable default argument, it cited the classic Python gotcha and showed the idiomatic fix using None as the default.

GPT-4o found four of the five bugs—it missed the memory leak—and its explanations were more surface-level. It correctly identified the race condition and the off-by-one error, but its explanation of the regex issue was less precise, and it didn’t catch the unclosed file handle even when I explicitly asked it to look for resource leaks.

Verdict: Claude Sonnet 4 wins decisively on debugging. Its ability to not just find bugs but explain the underlying concepts is a significant advantage for developers who want to learn from the process.

Architecture: The Long Game

For the final test, I asked both models to design a small REST API for a task management system, with requirements for authentication, database persistence, and rate limiting. The prompt specified using FastAPI and SQLAlchemy.

GPT-4o produced a complete, functional application structure. It set up the FastAPI app, defined SQLAlchemy models, created the necessary endpoints, and included JWT-based authentication. The code was clean and followed standard patterns. However, the rate limiting was implemented as a simple in-memory counter, which wouldn’t scale beyond a single process.

Claude Sonnet 4 also produced a complete application, but its architecture was more thoughtful. It used slowapi for rate limiting, which is the standard FastAPI-compatible solution, and it structured the project with separate modules for models, schemas, routes, and services—following a clean architecture pattern. It also included a docker-compose.yml for the database, which was beyond the scope of the prompt but demonstrates forward thinking.

Verdict: Claude Sonnet 4 edges out GPT-4o on architecture. Its solutions are more production-ready and show a better understanding of the broader ecosystem.

Performance and Practical Considerations

Speed-wise, both models were comparable—typically responding within 2-4 seconds for most tasks. GPT-4o was slightly faster on shorter prompts, while Claude Sonnet 4 was marginally faster on longer ones.

In terms of token usage, GPT-4o was slightly more economical, producing about 10-15% fewer tokens on average. This matters if you’re paying per token, but the difference is negligible for most use cases.

One notable difference: Claude Sonnet 4’s responses felt more “conversational.” It often included brief explanations before and after code blocks, which can be helpful but also adds noise if you just want the code. GPT-4o was more direct—code first, explanation second.

The Bottom Line

After two weeks of testing, a clear picture emerged.

Choose Claude Sonnet 4 if you want:

  • Deeper code understanding and better refactoring
  • Superior debugging with educational explanations
  • More production-ready architecture suggestions
  • A mentor-like experience that helps you learn

Choose GPT-4o if you want:

  • Faster, more concise responses
  • Slightly better test coverage volume
  • Lower token usage
  • A no-nonsense, code-first approach

For most professional Python developers, Claude Sonnet 4 is the better all-around coding assistant. Its edge in refactoring and debugging—the two areas where AI assistance provides the most value—is significant. The gap isn’t massive, and GPT-4o is still a capable coding tool, but Claude’s deeper understanding of Python semantics and best practices gives it a real advantage.

That said, the AI landscape changes quickly. What’s true today may not be true in six months. The best advice is to test both models on your own codebase and see which one aligns with your workflow. The right choice depends on whether you value concise output or comprehensive understanding—and that’s a decision only you can make.