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): | Model | Time to First Token (TTFT) | Price per 1M input tokens | Price per 1M output tokens | Code quality (score) | |---|---|---|---|---| | Claude 4 Sonnet | ~1.8 s | $12 | $60 | 9.5/10 | | GPT-5 Turbo | ~0.5 s | $15 | $75 | 8.5/10 | | DeepSeek Coder V3 | ~0.9 s | $3 | $15 | 9.0/10 | | Gemini 2.0 Pro | ~1.2 s | $10 | $50 | 7.0/10 | **Key takeaway:** DeepS

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

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