Comparison of AI Models for Programming: Claude vs GPT vs DeepSeek vs Gemini — Who Writes the Best Code?

Introduction

Generative neural networks have firmly entered the arsenal of the modern developer. By mid-2026, choosing an AI model for writing code has become not just a trend, but a necessity. But which one truly helps find bugs faster, refactor legacy code, and write from scratch? In this AI model comparison, we tested four leaders: Claude, GPT, DeepSeek, and Gemini. We evaluated them on real-world tasks—from debugging a complex SQL query to generating a microservice in Python. No advertising, only facts, figures, and code examples.

Key Testing Criteria

For an objective assessment, we identified five key parameters critical for a programmer:
- Code generation from scratch — writing functions, classes, API endpoints.
- Refactoring — improving readability, performance, and security of existing code.
- Debugging — finding logical errors, memory leaks, and bugs in multithreading.
- Response speed — time to first token (TTFT).
- Price — cost per million tokens (input/output) for the API.

Test #1: Code Generation from Scratch

We asked each model to write a Python function for parsing web server logs, extracting IP addresses, status codes, and timestamps, followed by aggregation of 4xx/5xx errors.

Results:

• Claude 4 Sonnet — produced clean, documented code using dataclasses and re. Handled edge cases (empty lines, malformed logs).
• GPT-5 Turbo — generated fast but less secure code (no exception handling). Required two clarifying prompts.
• DeepSeek Coder V3 — showed excellent performance: code was compact, with built-in type hints and annotations. The only downside was excessive brevity in comments.
• Gemini 2.0 Pro — produced working code but with redundant checks (e.g., checking for None where impossible).

Verdict: Claude and DeepSeek tie for first place in code cleanliness; GPT is the leader in generation speed.

Test #2: Refactoring Legacy Code

Task: take spaghetti JavaScript code (a 200-line function with nested callbacks, no async/await) and transform it into modern, modular code.

What the models showed:

• Claude — proposed a complete refactoring with breakdown into 5 functions, added comments and unit tests. Best result.
• GPT — split the code into 3 modules but left one critical vulnerability (unhandled promise rejection).
• DeepSeek — completed refactoring in 2 seconds, but the code style was “synthetic” (used outdated patterns).
• Gemini — performed worst: preserved the overall structure, simply replacing callbacks with .then() chains. Did not use async/await.

Test #3: Debugging a Complex SQL Query

We gave each model a query with a JOIN on 5 tables, which had an error: an incorrect LEFT JOIN caused duplicate rows. Additionally, a bug with GROUP BY.

Error detection results:

• GPT — instantly pointed out the incorrect JOIN and suggested a proper INNER JOIN with a subquery. Speed: 1.2 seconds.
• Claude — found both errors but gave two alternative solutions (with CTE and with a temporary table). Time: 2.1 seconds.
• DeepSeek — identified only the GROUP BY issue, missed the JOIN. Time: 0.8 seconds.
• Gemini — explained that “the query could be optimized” but did not specify the exact lines with errors.

Speed and Price Comparison

For practical use, not only code quality but also cost-effectiveness matters. Below is a summary table as of June 2026 (prices are for direct API access, excluding caching):

Key takeaway: DeepS