Well, it’s here. GPT-5. The model that was supposed to change everything. Again. The hype leading up to its release was, to put it mildly, INTENSE. Sam Altman, OpenAI's CEO, was out there saying GPT-4 was "mildly embarrassing at best" & that GPT-5 would be so much smarter. We heard whispers of it being a "doctoral expert" & a coding prodigy that would practically write entire applications on its own. For developers, this was supposed to be the moment our jobs got exponentially easier. We pictured a seamless coding partner, a brilliant assistant that could untangle our most complex bugs & help us build better, faster.

But now that GPT-5 is out in the wild, the reality for many developers is… complicated. To be honest, it's been a bit of a mess. While some are singing its praises, a significant & vocal group of developers are finding their new "coding partner" to be more of a buggy, frustrating, & downright unhelpful mess. It turns out, the upgrade we were all waiting for feels more like a downgrade for a lot of us.

Let's start with what was promised. On paper, GPT-5 looks like a coding powerhouse. OpenAI’s official announcement was packed with impressive stats & glowing testimonials. They told us it's "state-of-the-art" on key coding benchmarks, scoring a whopping 74.9% on SWE-bench Verified. This isn't just about solving simple coding puzzles; SWE-bench tests an AI's ability to solve real-world GitHub issues, the kind of stuff developers actually grapple with every day.

They also touted its prowess in front-end development, claiming it beats their previous models significantly in creating clean, production-ready code for things like HTML, CSS, & React. We saw companies like Cursor calling it "the smartest coding model we've used" & praising its intelligence & steerability.

One of the most hyped features was the new "Thinking Mode." The idea is that GPT-5 has two gears: a fast one for quick, simple answers, & a deep, multi-step reasoning engine for when you're stuck on a really gnarly problem. For junior developers, this sounded like a game-changer – a mentor in a box that could not only give you the answer but also explain the logic behind it.

And let's not forget the promise of fewer hallucinations. OpenAI claimed an 80% reduction in factual errors compared to previous models, which for any developer who's been sent on a wild goose chase by an AI-generated bug, sounded like a dream. So, better code, smarter reasoning, fewer mistakes… what’s not to love?

This is where things get messy. For every glowing review, there seems to be a developer on a forum somewhere pulling their hair out. A thread on the OpenAI Developer Community pretty much sums it up with the title: "ChatGPT 5 is worse at coding, overly-complicates, rewrites code, takes too long & does what it was not asked." Yikes.

One of the biggest complaints is that GPT-5 has become a verbose, jargon-obsessed mess. A developer described its writing style as a "1950s boomer technician trying to sound smart using endless jargon." Instead of clean, simple code, it's spitting out overly-engineered solutions with cryptic variable names & bizarre comments. For example, a simple message gets turned into a complex function with hypothetical "ui toasts" & referred to as an "error pipe." It’s not just unhelpful; it's actively making the codebase worse.

Then there's the issue of it going rogue. Developers are finding that GPT-5 just doesn't listen. You ask it to fix a small bug or write a simple helper method, & instead, it rewrites your entire class. It will change variable names, alter method signatures, & introduce a ton of unnecessary code you never asked for. It's like hiring an assistant who, instead of just grabbing you a coffee, decides to renovate your entire kitchen without asking.

What’s even more maddening is that it seems to be hallucinating entire file structures. One developer reported that GPT-5 was giving them instructions to insert code at specific line numbers in files that didn't even exist. This is a MAJOR step back. While older models might have hallucinated a faulty line of code, this version is inventing a whole fictional project to put it in.

Honestly, the frustration is palpable. One developer put it bluntly: "ChatGPT 5 is one of the worst coding models I have EVER in my life used." That's a pretty damning review for a tool that was supposed to be a massive leap forward.

So, what gives? How can a model be both "state-of-the-art" & a "disaster"? A lot of the blame seems to be pointing to a new feature that OpenAI introduced: an "automatic switcher."

Here’s the thing: GPT-5 isn't just one giant model. It's a family of models with different capabilities & speeds. The automatic switcher is supposed to intelligently choose which model to use based on the complexity of your prompt. Simple question? Get the fast, lightweight model. Complex coding problem? It should fire up the big guns.

The problem is, it seems to be getting it wrong. A LOT. Developers are finding that for what they consider complex tasks, the system is using the weaker, "non-thinking" models, leading to subpar, lazy, & sometimes just plain wrong answers. When prompted to "think again," it can sometimes produce the correct answer, but that lack of transparency is a huge source of frustration. We're flying blind, not knowing if we're getting the "smart" GPT-5 or its less-capable sibling.

This also explains why some users feel like they're getting "shrinkflation" – less value hidden behind a big announcement. It feels like we're paying for a premium steak but only getting served a cheap burger. And to make matters worse, OpenAI removed access to the older, more reliable models like GPT-4o overnight, forcing everyone onto this new, unpredictable system. It’s not an upgrade; it’s a forced adoption of a buggy product.

Now, it's not ALL bad. It would be unfair to paint the entire GPT-5 experience with the same brush. There are developers, particularly those on the junior end of the spectrum or those working heavily in front-end development, who are having a genuinely positive experience.

For a junior developer, the "Thinking Mode" can be a powerful learning tool. The ability to ask the model to "explain this like I'm new to JavaScript closures" & get a tailored, mentor-like response is incredibly valuable. The improved code generation for front-end tasks is also a big win, with the model producing cleaner, more semantically correct HTML & CSS that requires less manual rework.

There's also the "vibe coding" feature, where you can describe an app you want – "a minimal to-do app with dark mode & animations" – & watch it come to life in a live preview. This is a pretty cool way to prototype & learn by seeing best practices in action.

And some have even found that while the model does produce bugs, it's better at fixing them. One user noted that while older models would get stuck in a "doom loop" of fixing one error only to create another, GPT-5 seems to be able to recover more gracefully.

So, we have this weird split. Experienced developers working on existing, complex codebases are tearing their hair out, while junior developers or those starting new projects from scratch are finding it to be a helpful, if imperfect, tool. It seems GPT-5 is great at creating things from whole cloth but struggles when it has to play nicely with existing code & follow specific instructions.

This whole situation brings up a bigger conversation that's been bubbling in the AI community for a while now. There's a growing fatigue with the relentless hype cycle & the push toward AGI (Artificial General Intelligence). Many developers don't want or need an AI that can "think like a human" or write a novel. What they want are reliable, predictable, & controllable tools that help them do their jobs better.

The GPT-5 launch, with all its inconsistencies, has really highlighted this need. When you're on a deadline, the last thing you want is an AI assistant that decides to go on a creative tangent & rewrite your entire application. You need a tool that does what you tell it to do, every single time.

This is where the idea of more specialized, custom AI solutions comes into play. For businesses, the unpredictability of a model like GPT-5 is a non-starter. Imagine trying to build a customer service chatbot with GPT-5. One minute it’s providing helpful, accurate answers, & the next it's spouting jargon & trying to rewrite your company's FAQ page. It’s just not feasible.

That’s why many businesses are turning to platforms like Arsturn. The whole idea behind Arsturn is to help businesses create custom AI chatbots trained on their own data. This means you get a predictable, reliable assistant that knows your business inside & out. You can build a no-code AI chatbot that provides instant customer support, answers questions accurately, & engages with website visitors 24/7, all without the fear that it's going to go off-script. It's about building a meaningful connection with your audience through personalized, controlled AI, not just throwing a powerful but unpredictable model at the problem. For developers inside a company, having a reliable internal tool built on a platform like Arsturn to answer questions about a codebase would be infinitely more useful than the current GPT-5 experience.

Honestly, the launch of GPT-5 has been a rollercoaster. It’s a classic case of over-promising & under-delivering, at least for a large chunk of the developer community. While it clearly has some impressive capabilities & is helping some people, the frustrations are real & significant. The buggy performance, the rogue behavior, & the lack of control have turned a tool that was supposed to be a dream partner into a source of frustration for many.

It feels like OpenAI got so caught up in the race for bigger & better benchmark scores that they lost sight of what many developers actually need: a reliable, predictable, & helpful tool. The "magic" of AI quickly wears off when it's actively making your job harder.

Hopefully, OpenAI will listen to the feedback & work out the kinks. But for now, it seems like the relationship between developers & their AI coding partners is on the rocks.

Hope this was helpful & gave you a good overview of what's going on. Let me know what you think – have you had a good or bad experience with GPT-5?