When OpenAI announced the arrival of GPT-5 in early August 2025, the AI community expected big, big leaps forward. A few weeks later? We’ve all had to reset our expectations.
Yes, the new model has merit, but it certainly wasn’t the tech marvel we were led to expect. In fact, users raised enough of a commotion that OpenAI had to return the old models as options.
4 Ways GPT-5 Improves on GPT-4o
- Improved ability to handle intricate, multi-step reasoning tasks.
- Stays present longer on a conversation thread to handle sophisticated queries.
- Model routing promises greater efficiency and results.
- Agent Mode offers the potential for GPT-5 to break down larger tasks into steps and execute on them.
The stumble was a lesson in promotion. GPT-5 may be better than its predecessors, but it fell short of the quantum leap many expected. For most day-to-day users, the upgrade felt more like a side-step than a bold leap.
Why GPT-5 Fell Short of Launch Expectations
Hype is a mixed blessing when it comes to technology. It can accelerate adoption and funding. It can also build an expectation gap that’s hard to close.
In GPT-5’s case? The marketing and community buzz set the bar extraordinarily high. People envisioned a transformative AI companion that would outsmart, out-reason, and out-dream its predecessors in dramatically new ways.
Reality hit hard. GPT-5 delivered incremental improvements, but not an outright revolution. This isn’t just a criticism of the model’s capabilities — GPT-5 is indeed smarter and more context-aware — but the past two weeks have exposed the difficulty of managing expectations around AI progress, which often unfolds step-by-step instead of in giant strides.
Is GPT-5 Better Than 4o?
You wouldn’t know it from scrolling news feeds, but there’s a lot to like about GPT-5, especially when you peek under the hood. One of GPT-4’s weaknesses was handling intricate, multi-step reasoning tasks. GPT-5 makes important gains here. It’s better at analyzing layered problems and piecing together logic across multiple turns in conversation.
Conversations with GPT-5 feel more coherent over longer stretches. The new model stays “present” and on-topic better, so you’re much more likely to get dialogues that flow naturally without losing the thread.
A potentially groundbreaking feature is the model routing system. Instead of using a single model for all queries, GPT-5 can dynamically route tasks to different specialized models.
For example, quick lookups might use a smaller, faster model to save time and resources, while detailed analysis relies on the full GPT-5 model. This architecture promises greater efficiency and smarter resource use if you trust the system to pick the right model on your behalf.
These improvements are real. And they represent a solid foundation for the future of AI.
Where Does GPT-5 Fall Short?
Still, not everything about the much-anticipated release went as planned. Within a day of launching GPT-5, OpenAI had to restore access to older models due to community pushback, which stemmed from several reasons.
For the majority of routine tasks, such as writing emails, summarizing documents, and answering straightforward questions, the difference between GPT-4 and GPT-5 is subtle. It left most users without a strong reason to switch.
After all, switching AI models isn’t like updating an app. It affects trusted workflows. When an AI’s familiar behavior shifts or slows down, it chips away at user trust.
On top of the changes for users, the new Agent Mode lags. In this new mode, GPT-5 acts more like a digital worker by breaking tasks into steps and calling on external tools. It’s exciting on paper, but right now, it feels more like a beta experiment than a ready-to-roll feature.
Bottom line: The biggest challenge may have simply been expectation management. The buzz promised a generational leap, and while GPT-5 made gains, it was not the miracle many hoped for. This mismatch created an expectation gap that was hard to overcome once the reality set in.
What to Expect From GPT-5 in the Future
Initial reactions to GPT-5 may be mixed, but AI is still moving forward. In this respect, two features stand out: model routing and agent mode. Let’s take a look at what both have to offer:
Model Routing Represents a Clear Shift
In the near future, users won’t have to know or choose which AI model to use for a task. The system will intelligently decide the best tool for the job. Think of it like a digital call center that instantly routes you to the right expert.
The key obstacle now is trust. Users, and even organizations, need confidence that the AI will choose wisely. Only transparency and consistent performance will build that trust over time. For ChatGPT’s part, they’ve added more transparency in where responses come from and added additional learning models. These are the types of steps they’ll need to continue to take to earn that trust.
Agent Mode
Agent Mode is one of the most exciting and most frustrating parts of GPT-5. While it’s currently as slow as molasses, that’s because it’s doing something fundamentally different from just generating text. In Agent Mode, the model isn’t just answering a question; it plans, breaks tasks into steps, calls on external tools or APIs, and then stitches the results together into something coherent.
This mode is where the real upgrade is: GPT-5 is acting like a mini digital worker. Right now, though, it’s not there in terms of speed, and the outputs sometimes feel like overkill for simpler requests. But once performance catches up and integrations deepen, Agent Mode could be the factor that makes GPT-5 a true leap forward over previous versions.
GPT-5 Represents a Step Forward, Not a Giant Leap
As someone who works with AI daily, I appreciate the advances GPT-5 brings. I’m also grounded in how real users experience technology.
GPT-5’s strengths in reasoning and context retention are meaningful. Its routing system is innovative and its Agent Mode is ambitious, but these gains feel evolutionary rather than revolutionary.
OpenAI’s rapid rollback to older models acknowledges that users still find great value in what came before. We can take it as a reminder that AI adoption is as much about user trust and workflow fit as it is about technical horsepower.
If you ask me whether I “like” GPT-5, the honest answer is “yes and no.” I like the direction as a necessary step forward, but unfortunately, it didn’t justify the hype.
The lesson? Temper marketing promises with realism. Let’s focus on moving forward with steady progress, not overnight miracles. And let’s explore future goals that revolve around building trust and delivering tangible value.
