The AI assistant market resembles an arms race: OpenAI, Google, and Anthropic are constantly releasing new, more powerful models. But when the hype subsides, developers are left with a simple question: which of these tools is actually useful in daily work? Who writes better code, who helps debug more effectively, and who gives the most sensible architectural advice?
We decided to move beyond abstract comparisons and conduct a stress test for the three titans—ChatGPT-4, Google Gemini, and Anthropic's Claude 3—on tasks that every developer faces daily.
Our Contenders
- ChatGPT-4 (OpenAI): The "creative veteran" that set the standard for the entire industry.
- Gemini (Google): The "integrated expert" with real-time access to Google search.
- Claude 3 (Anthropic): The "thoughtful analyst" with a massive context window, capable of "reading" an entire codebase.
Test 1: Green-Field Code Generation
Task: Write a REST controller in Spring Boot for a simple CRUD application (Create, Read, Update, Delete) for a Product entity.
- ChatGPT-4: Performed excellently. It produced clean, idiomatic Java code, immediately added handlers for the main HTTP methods (
@GetMapping,@PostMapping, etc.), and even suggested a basic DTO structure. The code was almost production-ready. - Gemini: Also generated working code but made it more concise. A plus was that it immediately mentioned the current versions of dependencies to add to pom.xml, thanks to its web access.
- Claude 3: Delivered the most detailed result. In addition to the controller itself, it generated the
Product,ProductRepository, andProductServiceclasses, essentially creating a complete functional slice of the application. The code was very well-commented.
Test 2: Debugging Foreign Code
Task: Find the logical error in a code snippet with a method that incorrectly calculates a discount (e.g., applies a double discount).
- ChatGPT-4: Quickly found the error and proposed a correct fix. The explanation was clear but focused solely on the problem itself.
- Gemini: Also found the error but additionally provided context on why such errors often occur and advised on how to improve the code to avoid them in the future (e.g., by moving the discount calculation to a separate private method).
- Claude 3: Gave the most comprehensive answer. It not only found the error and suggested a fix but also detailed its "reasoning" process for finding it and proposed several unit tests to verify the corrected logic.
Test 3: Architectural Advice
Task: Propose an architecture for an asynchronous video processing system. What components and technologies should be used?
- ChatGPT-4: Suggested a classic, time-tested architecture: a message queue (RabbitMQ/Kafka), processing workers, and cloud storage (S3). A good, reliable, but slightly "conservative" option.
- Gemini: Proposed a more modern stack oriented towards Google Cloud infrastructure: Cloud Storage, Pub/Sub for messaging, and Cloud Run for workers. The answer was very practical and included direct links to the necessary documentation.
- Claude 3: Compared several approaches (monolithic vs. microservices), describing the pros and cons of each in the context of the task. Its response was like a consultation with an experienced system architect who doesn't give a ready-made solution but helps you choose the optimal one.
Verdict: Each Has Its Own Strength
After the tests, it became clear: there is no single "best" chatbot. The choice depends on the task.
- ChatGPT-4 is the best "idea generator." Ideal for a quick start, writing boilerplate code, and creative tasks.
- Gemini is the best "pragmatist." Its strength lies in its access to up-to-date information and integration with the Google ecosystem. Indispensable when working with new technologies.
- Claude 3 is the best "code analyst." Thanks to its huge context window, it's the perfect assistant for refactoring, analyzing existing code, and solving complex, multi-layered problems.
Conclusion: From Generalists to Specialists
General-purpose chatbots are powerful assistants, but they are limited by their generalized nature. The future of development lies with specialized AI agents that are deeply integrated into the IDE and understand the full context of your project. Tools like Explyt don't just answer questions. They analyze your code, generate tests based on your logic, and assist in debugging while seeing the whole picture. Generalists are good for getting started, but professional work requires professional tools.
