Claude vs GPT vs DeepSeek vs Gemini: Comparison of AI Models for Programming in 2025 — Which AI Writes Code Better?

Introduction: The Battle of Neural Networks for Your Code

The market for AI assistants for developers is overheated. Every month, a new model emerges promising a "revolution in coding." But when a real task arises—refactoring legacy code, hunting a bug at 3 AM, or generating a microservice from scratch—which AI truly won't let you down? We conducted a series of tests on real-world tasks to provide an objective comparison of AI models: Claude (Anthropic), GPT-4o (OpenAI), DeepSeek-Coder, and Gemini 2.0 (Google).

We evaluated not only code quality but also generation speed, token cost, and the ability for multi-step reasoning. If you're looking for the perfect AI colleague for writing code, this article is your guide.

1. Task #1: Refactoring Spaghetti Code

Participants: Claude 3.5 Sonnet vs GPT-4o vs DeepSeek-Coder V2 vs Gemini 2.0 Flash

We gave each model the same Python block—150 lines with duplication, magic numbers, and no typing. Results:

• Claude 3.5 Sonnet — suggested splitting the code into 4 classes, added type hints, and wrote docstrings. The only one that fixed a potential memory leak. Quality: 10/10
• GPT-4o — reduced the code by 40% but left several implicit bugs (e.g., an unhandled exception). Quality: 7/10
• DeepSeek-Coder V2 — did a good refactoring but didn't remove commented-out junk. Quality: 8/10
• Gemini 2.0 Flash — simply renamed variables. Essentially failed the task. Quality: 4/10

Conclusion: For cleaning and organizing code, Claude is the best—it sees architecture, not just syntax.

2. Task #2: Debugging a Hidden Bug

Test: Find a race condition in asynchronous code

We designed a complex bug: a data race in asyncio with partially incorrect logical operators. Models had to not only find the error but also explain the cause.

• GPT-4o — found the bug in 2 iterations, gave a correct explanation with a traceback. Time: 45 sec
• Claude 3.5 — found the bug instantly, but its explanation was too general. Time: 30 sec
• DeepSeek — pointed to a wrong import (which didn't exist) but missed the actual bug. Time: 60 sec
• Gemini — got stuck on one method and didn't see the problem.

Conclusion: For debugging sessions where speed and accuracy are needed, GPT-4o remains the leader. Claude is good but can get lost in abstractions.

3. Task #3: Generating Code from Scratch

Technical Specification: Write a REST API in FastAPI for a to-do list with authorization

This was a test of understanding the full development cycle. Criteria: security (JWT), code cleanliness, documentation.

• Claude 3.5 — generated fully working code from scratch, including Alembic migrations and tests. The only one to use dependency injection correctly. Winner
• GPT-4o — code worked but was redundant (200 lines vs. Claude's 150).
• DeepSeek — produced a minimal working solution but without error handling.
• Gemini — output code that didn't run due to a syntax error in Pydantic.

Conclusion: If you need to write code from scratch for a complex architecture, choose Claude. For simple scripts, any will do, but Claude delivers production-ready solutions.

4. Price and Speed: What's More Profitable?

We compared the cost of 1 million tokens (input/output) and the generation time for an average response (500 tokens):

Analysis:
- Gemini — the cheapest and fastest, but code quality suffers.
- DeepSeek — excellent balance of price and quality for routine tasks. Ideal for writing simple functions.
- Claude and GPT — premium segment. Expensive but pay off on complex projects.

5. Verdict: Which Model to Choose?

Final comparison of AI models by scenario:

• For refactoring legacy code