Introduction
June 2026. The market for AI assistants for developers is booming: new versions are released every month, context windows are improving, and code generation speed is approaching instant. We tested four top models — Claude, GPT, DeepSeek, and Gemini — on real tasks: refactoring legacy code, debugging complex errors, and generating microservices from scratch. In this comparison of AI models, you'll find out which tool saves hours of work and which is a waste of tokens.
Testing Methodology
We evaluated the models based on four criteria:
- Refactoring — improving readability and performance of existing code.
- Debugging — finding and fixing errors in multithreaded applications.
- Generation from scratch — writing a REST API in Python + FastAPI.
- Price and speed — cost per 1K tokens and response time.
Each test was conducted with identical prompts at a temperature of 0.3 for objectivity.
1. Claude (Anthropic) — King of Refactoring
Claude 4 Opus showed the best results in refactoring tasks. It doesn't just rewrite code; it explains the logic behind changes. For example, when optimizing an SQL query with an N+1 problem, the model suggested using select_related and added indexes — the solution was ready in 12 seconds.
Pros:
- Deep context understanding (200K tokens).
- Natural comments in Russian.
- Excellent handling of TypeScript and Rust.
Cons:
- Sometimes generates excessive abstractions.
- High cost: $15 per 1M input tokens.
2. GPT-5 (OpenAI) — Universal Soldier
GPT-5 Turbo is the golden mean. In the debugging test, it found a race condition in a Go program in 8 seconds, suggesting using mutexes instead of atomics. When generating from scratch, the code was valid on the first run, but the style sometimes resembled "spaghetti."
Pros:
- Largest ecosystem of plugins and tools.
- Fast generation (5-7 seconds for 200 lines).
- Good support for Python and JavaScript.
Cons:
- Increased token consumption for long prompts.
- Price: $10 per 1M input tokens (average).
3. DeepSeek-V3 — Budget Genius
The Chinese model DeepSeek-V3 surprised with its performance for the price. In the REST API generation task, it produced working code with JWT authorization and Swagger documentation in 6 seconds. However, when refactoring legacy PHP code, the model got confused with variable names.
Pros:
- Price: $0.5 per 1M input tokens (30 times cheaper than GPT).
- High speed (3-5 seconds).
- Support for Chinese language in comments.
Cons:
- Weak context (128K tokens).
- Errors in complex algorithms (e.g., merge sort).
4. Gemini 2.0 Ultra (Google) — Multimodal Assistant
Gemini 2.0 Ultra handled debugging visual bugs best — it found a CSS Grid issue in a second. But in pure Python code, it falls short: generating a class for working with an API contained method duplication.
Pros:
- Integration with Google Cloud and Android Studio.
- Works with images and diagrams.
- Free limit of up to 100 requests/day.
Cons:
- Slow response (10-15 seconds).
- Requires explicit specification of the programming language.
Comparison Table
| Model | Refactoring | Debugging | Generation | Price (input) | Speed |
|---|---|---|---|---|---|
| Claude 4 Opus | 9.5/10 | 9/10 | 8.5/10 | $15/1M tokens | 12 sec |
| GPT-5 Turbo | 8/10 | 9.5/10 | 9/10 | $10/1M tokens | 7 sec |
| DeepSeek-V3 | 6/10 | 7/10 | 8/10 | $0.5/1M tokens | 5 sec |
| Gemini 2.0 Ultra | 7.5/10 | 8/10 | 7/10 | $7/1M tokens | 13 sec |
Practical Tips for Choosing
- For refactoring legacy code (Java, C#) — go with Claude. It understands architecture better and suggests micro-optimizations.
- For rapid prototyping (Python, Node.js) — GPT-5. It's reliable and supports popular frameworks.
- For startups on a budget — DeepSeek. Saves up to 90% on AI assistant costs.
- For UI/UX and frontend — Gemini. Its multimodality allows immediate testing of visual components.
Comments