Is ChatGPT 5.2 All the Hype?

Lately, I've been seeing a flood of AI-slop online—benchmark charts, capability rankings, and bold claims about how much better the newest models are.

So instead of repeating that noise, I wanted to look at something more practical:

How does ChatGPT 5.2 actually feel to use in real workflows?

Because on paper, it's impressive. In practice? It's more nuanced.

If this sounds familiar, I ran into a similar issue while testing Gemini—specifically the lack of project organization and how that impacts real work. (The Worst Thing About Gemini 3 Pro (That No One Talks About))

The Overthinking Problem

One thing I've noticed with the last two iterations of GPT (5.1 and 5.2) is how much they overthink simple tasks.

GPT-5.1 and 5.2 overthinking simple tasks

Instead of giving me a direct answer, the model often:

Over-optimizes responses
Suggests 3–4 different approaches when I only needed 1
Forces extra follow-up prompting just to narrow things down

That friction adds up.

Ironically, GPT-5.0 felt faster in day-to-day use—not because it was "smarter," but because it knew when not to be.

What GPT-5.0 Did Better

What I really liked about GPT-5.0 was its ability to task switch cleanly.

I could:

Plan a full blog post
Then immediately ask for a featured image or thumbnail
And it just… did it

No unnecessary clarifications. No over-engineering. No prompt gymnastics.

It usually understood whether I was writing, planning, or creating visuals without me spelling it out.

This is the same reason I ended up building my own interface when Gemini couldn't surface the right context. (Google Antigravity: The UI I Built After Gemini 3 Kept Showing a 1965 Space Launch)

Where 5.1 and 5.2 Excel

To be fair, GPT-5.1 and 5.2 are clearly more intelligent models.

They shine when:

Writing or debugging code
Handling complex automations
Working with large or multi-step inputs
Analyzing data or analytics in depth

For those tasks, the extra reasoning is warranted—and honestly welcome.

For deeper workflows, especially automations, the extra reasoning actually pays off. (How I Use AI to Organize My Week Inside Notion)

The Hallucination Tradeoff

One thing that's stood out more than I expected:

I'm seeing more hallucinations in 5.1 and 5.2.

Not dramatic ones—but subtle issues like:

Filling in steps I didn't say I took
Guessing outcomes instead of reflecting what actually happened
Presenting assumptions as facts

Because of this, I've had to slow down and carefully read outputs, which again adds friction where speed should exist.

This is why I've become more cautious about trusting AI output without verification. (GPT-5.1 vs Gemini 3: Which AI Model Is Better for Real Creative Workflows?)

Image Generation Friction

This is where the experience really breaks down for me.

With GPT-5.2, if I ask for something like a thumbnail:

It often over-engineers the prompt
Explains what it would do
Or asks for clarification instead of just generating the image

I find myself stuck in prompt-hell, clicking the "+" icon and nudging it forward.

GPT-5.0 was far better at intuitively knowing when I wanted an image versus text.

Why I've Been Using Gemini for Images

Ironically, this friction pushed me toward a better workflow.

Gemini has one limitation that actually helps:

If you're making images, you're locked into image mode
You can't bounce back into long explanations or text

That constraint removes noise.

So now my workflow looks like this:

Use GPT-5.2 to over-engineer the best possible image prompt
Feed that prompt into Gemini
Get clean, consistent image outputs every time

It's funny how setbacks often lead to creative freedom with AI.

Ironically, constraints often lead to better creative systems. (Nano Banana Pro vs GPT-5.1: Which AI Image Model Actually Performs Better?)

If you're experimenting with multiple AI models and feeling friction, you might find this breakdown helpful: Google Antigravity: The UI I Built After Gemini 3 Kept Showing a 1965 Space Launch.

Final Thoughts

I'm still testing GPT-5.2, and I'll keep using it—especially for:

Automations
Coding
Analytics
Complex systems thinking

But for simpler tasks, the model's tendency to overanalyze can actually slow things down.

At this point, I'm seriously considering project-specific system prompts just to keep things focused and reduce unnecessary verbosity.

More intelligence doesn't always mean a better experience.

Sometimes, knowing when not to think is the real upgrade.