The model felt like GPT-4.2 at best… faster, definitely sharper than 4.1, but not some huge leap. I tried to use it for my day-to-day work (which, IMO, is the best way to evaluate any new model), and while it handled the tasks I was giving it very well, I wasn’t noticing anything dramatically better than GPT-4.1, Claude 4 Opus, or any of the other models I’ve been using.
Masters-Certificate-in-Risk-Management-and-Business-Performance-Leadership-scaled-1

Where GPT-5 Falls Short

For explicit search tasks, I still prefer o3. Why? GPT-5 stops digging sooner. For example, I was trying to have GPT-5 find the hometown of a public figure. It only found the city, and stopped there. I needed to prompt it multiple times to get it to actually look deeper and find the specific town. o3, on the other hand, will just keep digging until it finds what you need. This isn’t a deal-breaker for me, but it’s something to keep in mind if you rely heavily on models for research.
On the other hand, when it comes to implicit research, like mid-task documentation lookups or quick library checks during coding, GPT-5 clearly outperforms o3.
I’ve also noticed that GPT-5 does struggle a bit with instruction following. It’s not terrible, but you still need to be very careful with how you phrase and structure your prompts if you want the best results.

Long-Context Handling

Here’s something unexpected, especially given my suspicions around the model’s size: GPT-5 is incredibly good at maintaining consistency over very, very long coding sessions. I’ve worked with prompts likely spanning hundreds of thousands of tokens. It consistently maintains context insanely well. This feels far better than Gemini 2.5 Pro at long-context handling (though, I was accessing the model through the ChatGPT interface, so there’s a chance OpenAI is doing something on top of the model). I didn’t realize how valuable that was until I experienced it directly. It is a true step up for deep, long-term coding sessions.
GPT-5, even when pushed into big, messy codebases, maintained a clear understanding of the architecture, file organization, and project context, which previous models often struggled to do without constant reminders. It didn’t seem to get “dumber” as the context window grew… often, it even seemed to improve, becoming more aware of the project’s overall structure and how the pieces fit together.

Measurement & Continuous Improvement

Building resilience doesn’t end with strategy execution — it requires consistent evaluation and refinement. Regular measurement ensures your business stays aligned, efficient, and ready for change.

1. Track Interview Responses

Check how often you get shortlisted.

2. Improve Based on Feedback

Refine resume and communication.

3. Stay Updated

Follow industry trends and expectations.

4. Adapt Continuously

Keep improving your profile and skills.
All of this comes with a bigger-picture implication. GPT-5 is a true leap. I genuinely think the rest of the industry is going to have to sprint now. Labs releasing other models or coding platforms need to pay attention: developers are going to shift to GPT-5 quickly. The combination of autonomy and speed is a major unlock. Teams using GPT-5 will out-ship teams that don’t.

If you’re building around these models, this is your opportunity to 10x your product. If you’re a VC, pay close attention:
adoption curves of GPT-5-powered teams will be visible in how quickly they build and ship products. Expect a noticeable shift in market dynamics.

And most importantly, as with every jump in model intelligence, new use-cases will become possible, and new companies will emerge to capitalize on them. You can bet that I’ve already found a couple of these use-cases and will be keeping them close to my chest for now, with the aim of building something new around them. It’s exciting to say the least.

Bottom line, GPT-5 isn’t just going to improve vibe coding, it will fundamentally change the kinds of projects I consider doable without serious human intervention and steering. This past week, it turned what I confidently thought was a multi-month engineering challenge into a casual one-hour sprint.