I tested GPT-5’s coding skills, and it was so bad that I’m sticking with GPT-4o (for now)
If you’ve been following the world of AI-assisted coding, you’ve probably heard the buzz around GPT-5 and its promises of cutting-edge code generation capabilities. As an avid developer and AI enthusiast, I couldn’t resist putting GPT-5 to the test-especially compared to the reliable GPT-4o tool I’ve been using for months. Spoiler alert: GPT-5 didn’t quite live up to its hype, and I’m sticking with GPT-4o for now. In this article, I’ll dive into my first-hand experiences, key differences, practical tips, and why GPT-4o remains my go-to AI coding assistant.
Why AI Coding Assistants Matter
AI-powered coding assistants have revolutionized programming by streamlining workflows, reducing errors, and boosting productivity. Whether you’re a beginner debugging your first script or a seasoned developer automating complex tasks, AI tools help you write, understand, and improve code faster and better.
- Faster code generation based on natural language prompts.
- Intelligent debugging and error detection.
- Code suggestions tailored to your project and style.
- Multi-language support for diverse tech stacks.
My First-Hand Experience Testing GPT-5’s Coding Skills
When GPT-5 was officially announced with claims of exponential improvement in coding assistance, I was eager to test its capabilities side by side with GPT-4o. Here’s what I did:
- Task Complexity: I ran a series of coding tasks from simple scripts to complex algorithms.
- Language Variety: I tested HTML, Python, JavaScript, and SQL queries.
- Code Accuracy: I evaluated generated code for correctness, efficiency, and readability.
- Bug Handling: I prompted both models to debug pre-written erroneous code snippets.
Results Summary
| Criteria | GPT-5 | GPT-4o |
|---|---|---|
| Code accuracy | 70% (many syntax errors) | 90% (mostly correct and clean) |
| Debugging ability | Poor – missed logical flaws | Good – identified key issues |
| Understanding complex prompts | Inconsistent interpretation | Consistent and context-aware |
| Speed of response | Fast but lower quality | Moderate speed with high-quality output |
What Went Wrong With GPT-5?
While I expected GPT-5 to outperform its predecessor, several factors led to disappointing results:
- Overambitious Complexity: GPT-5’s code was often convoluted or overly complex to the point of being impractical.
- Error-Prone Code: Frequent syntax mistakes and buggy snippets required manual corrections, defeating the purpose of AI assistance.
- Poor Context Retention: GPT-5 sometimes misunderstood multi-step or conditional instructions, leading to irrelevant or unusable code.
- Lack of Consistent Debugging: Instead of fixing bugs, it introduced new ones or overlooked existing errors.
Why GPT-4o Remains My Preferred AI Coding Assistant
Despite being older, GPT-4o consistently delivered reliable, readable, and useful code snippets. Here’s why I’m sticking with it for the foreseeable future:
- Stable and Predictable: GPT-4o generates code that works more often than not, reducing frustration.
- Better Debugging: It helps identify and resolve errors more accurately.
- Context-Aware Responses: GPT-4o understands complex instructions and multi-turn conversations better.
- Wide Language Support: Supports a broad range of programming languages with appropriate syntax.
- Strong Community and Documentation: GPT-4o benefits from extensive usage, enabling more resources and tutorial content.
Quick Comparison: GPT-5 vs GPT-4o
| Feature | GPT-5 | GPT-4o |
|---|---|---|
| Code Quality | Variable, often buggy | Consistently good |
| AI Understanding | Less consistent | High consistency |
| Debugging Support | Limited | Robust |
| Community Support | Emerging | Established |
| Speed | Faster but less accurate | Balanced speed and accuracy |
Practical Tips for Choosing an AI Coding Assistant
Whether you’re considering GPT-5, GPT-4o, or another AI model, keep these tips in mind:
- Test Before Fully Adopting: Run sample projects and tasks to assess code quality and debugging ability.
- Consider Stability Over Hype: Cutting-edge models aren’t always better; consistency and reliability matter.
- Use AI as a Helper, Not a Replacement: Always review AI-generated code before deployment.
- Stay Updated: Keep an eye out for software updates, plugins, and community feedback.
- Customize Your Prompts: Clear and detailed instructions increase AI output accuracy.
Conclusion
Testing GPT-5’s coding skills was an eye-opener – while the new model brought speed and ambition, its practical coding abilities fell short of expectations. In contrast, GPT-4o demonstrated dependable and high-quality assistance consistently. Until GPT-5 undergoes significant refinement, GPT-4o remains my preferred AI coding assistant. If you’re looking for a reliable tool that understands your coding needs, sticking with proven options like GPT-4o is the smart choice for now.
Have you tested GPT-5 or other AI coding assistants yet? Share your experience in the comments below!