GPT-5's Reasoning Capabilities: What Developers Are Actually Seeing in Practice

I've been using GPT-5 daily since it launched earlier this year, and after hundreds of hours of testing, I have a pretty clear picture of where it shines and where it still struggles.

The headline numbers are impressive — 40% improvement over GPT-4 on complex math, 35% better code generation accuracy, significantly reduced hallucination rates. But here's what those numbers don't tell you.

What GPT-5 Actually Does Well

The most noticeable improvement is multi-step reasoning. With GPT-4, asking it to design a complex system architecture often resulted in a shallow, textbook-style answer. GPT-5 actually works through the problem. I asked it to design a real-time data pipeline handling 10K events per second with exactly-once semantics, and it produced a genuinely thoughtful architecture with trade-off analysis for different approaches.

The code generation is also meaningfully better. Not just "writes more code" — it writes more idiomatic code. The variable names make sense. The error handling is actually there. The edge cases are considered. It feels less like a smart autocomplete and more like a junior developer who's been reading good code.

Where It Falls Short

GPT-5 still struggles with very large codebases. The context window management is better than GPT-4, but it still loses coherence on projects with more than about 5,000 lines across multiple files. I've started using a technique of feeding it summarized context instead of full files, which helps considerably.

It also has a tendency to over-engineer solutions. I asked it to build a simple CRUD API, and it produced a full event-sourced architecture with CQRS pattern. Technically impressive, but not what anyone needs for a basic todo app.

The Developer Consensus

Talking to other developers who use GPT-5 extensively, the consensus is clear: it's a genuine step forward, not just a bigger model. The reasoning improvements translate to real productivity gains. But it's not magic. You still need to know what you're doing — GPT-5 is better at executing on a good specification, but it won't build your app for you.

The pricing increase from GPT-4 was modest (about 20% higher per token), which most developers I've talked to consider reasonable given the quality improvement. For API-based tools like Codex Desktop, the cost difference is barely noticeable in practice.