Claude 4 vs GPT-5 as Coding Assistants: A Hands-On Comparison
After two months of switching between both models, here's what actually matters in practice.
My "Pick One and Stick With It" Resolution Lasted Two Days
One day in November, someone dropped a Claude 4 link in the team Slack. I'd been happily using GPT-5 and was firmly in the "just go deep with one tool" camp. But curiosity is a dangerous thing -- that night I ran the same task through Claude 4. That was the beginning. Two months later, I'm using both. (So much for resolutions.)
The TypeScript Difference Is Real
This is where the gap is most noticeable. When I ask for a TypeScript React component, Claude 4 produces working code on the first try maybe eight or nine times out of ten. GPT-5 lands around seven. The difference widens especially when generics get complex.
But GPT-5's explanations alongside the code are way more helpful. It articulates "why I wrote it this way," which makes modifying the code later much easier. Claude 4 sometimes just throws code at you and moves on.
They React to Errors Differently
Paste an error message and ask "what's going on?" -- the two models have clearly different styles.
Claude 4 tends to pinpoint the root cause right away. When I pasted a Next.js hydration mismatch error, it correctly identified which component had the server/client inconsistency on the first attempt. Genuinely impressive.
GPT-5 tends to list three or four possible causes. Not wrong, but I end up having to narrow things down through elimination myself. A bit frustrating.
GPT-5 Had the Edge on Refactoring
I handed a 450-line utility file to both and said "clean this up." GPT-5 suggested design patterns and cleanly separated concerns. Claude 4 produced working code, but its structural suggestions were more conservative. Though honestly, this varies a lot depending on how you prompt. (Might be my fault.)
Who Forgets Context in Long Conversations?
After 30+ exchanges, GPT-5 starts forgetting types defined early on. Claude 4 holds onto context comparatively well throughout. This makes a real difference in practice.
Once I showed three 500-line files in sequence and asked for cross-file refactoring. Only Claude 4 correctly identified the dependencies between all three files. GPT-5 got confused about the second file's contents partway through.
So How Did I Settle?
A natural division of labor emerged. New feature implementation and complex types: Claude 4. Architecture discussions and code review: GPT-5. Quick utility functions and regex: whichever tab happens to be open.
"Which AI is better?" turned out to be the wrong question. It depends on the task.
One Regret, Honestly
Subscribing to both costs 40,000 won a month. It was 20,000 with just one. I'd like to cut one, but each is good at things the other isn't, so I can't bring myself to cancel either. And sometimes I catch myself mindlessly accepting AI-generated code without really thinking about it. When I later can't explain what that code does, it becomes debt.
The convenience is real. But getting too comfortable with it is a little scary too.