In my main GPT-5.2 review, I called Pro "a fucking monster when it comes to code" and promised a dedicated deep dive. This is that post.

If you haven’t read the main review, the short version is this: GPT-5.2 is a meaningful step up in instruction-following, reasoning, style, and code generation, but the standard model is too slow for daily use. Pro mode is where things get genuinely interesting... it’s willing to think for as long as a problem requires, and the results are often remarkable.

Let’s get into the details.

What Pro Actually Is

First, some clarification for people who haven’t used it. GPT-5.2 Pro is a separate system within ChatGPT. It’s not just "GPT-5.2 but it thinks longer"... it’s a distinct agent that (likely) uses some form of parallel/additional compute to increase reliability and drive a step-change in the difficulty of problems it can tackle.

Pro is only available inside ChatGPT. Not in Codex CLI. Not in the API. Not in Cursor or Cline. Just the ChatGPT interface. This continues to frustrate me. I’d love to use Pro in agentic coding workflows... but for now, that’s the reality.

The Time Investment

Let me set expectations: Pro is slow. I’ve never seen it think for less than five minutes on anything I’ve asked it. For complex problems like hard coding tasks, idea generation with tight constraints, research tasks, analysis, etc., I’ve watched it think for 45 minutes, sometimes over an hour. Tasks with difficult constraints in particular can lead to extremely long thinking times.

This changes how you use it. When I send something to Pro, I don’t sit and wait. I build my prompt (super) carefully, paste it in, and then I go do something else. Run an errand. Make lunch. When I come back, it’s done.

This sounds absurd if you’re used to the instant responses of regular models. But the results justify the wait. More often than not, Pro delivers something I couldn’t have gotten any other way.

I almost never use the Instant mode in ChatGPT. Thinking is much better, and Pro is insanely better. Instant feels quite dumb compared to both, as well as almost every other frontier model I’ve tried.

When Pro Is Worth It

I use Codex CLI with standard GPT-5.2 for day-to-day coding tasks. The more I use it, the more impressed I am... it’s the closest I’ve felt to using a Pro model in a CLI, and it gets things right on the first shot way more often than anything else I’ve tried. The frustrating part is speed: in the extra-high reasoning mode I have access to, it can take forever, sometimes longer than Pro. But it’s gotten good enough that I often don’t even check the output, especially if my prompt is prescriptive and clear. It’s almost always correct. Long-context is also one of its biggest strengths, which makes it great for huge codebases. For certain categories of work, though, I reach for Pro specifically.

The first category is problems that are genuinely hard. Tasks where most models fall flat because they require balancing many constraints simultaneously, or reasoning through something that doesn’t have an obvious solution.

The second is work where I can’t afford mistakes, like production code or important decisions. For anything where getting it wrong has real consequences, Pro’s reliability makes the time investment worthwhile.

And even if something isn’t super hard, but I want a thorough answer and I don’t need it right away, I’ll reach for Pro.

Here’s what’s impressive about GPT-5.2 Pro on hard problems: it’s good at intuiting things I didn’t even include in the prompt. I’ll forget to mention a constraint, or not think to add certain context, and Pro will somehow account for it anyway. It models the problem more completely than I described it.

A Concrete Example

I’m working on a new app (more on this soon, maybe?) that requires balancing a ton of different constraints... engineering time, maturity of AI tech available today, very strict user experience considerations, cost, and more. Getting all of these right simultaneously is extremely difficult.

Most models fell flat on their face when I described what I was trying to build and asked for ideas that fit those constraints. They’d give up, usually by optimizing for one constraint while ignoring others, or suggesting solutions that weren’t actually feasible.

I gave the problem to Pro. It thought for almost an hour. When it finished, it had come up with a fantastic idea that I’m actually using. The solution accounted for constraints I hadn’t even explicitly mentioned... it understood the shape of the problem well enough to fill in gaps I’d left.

There’s just nothing like GPT-5.2 Pro.

How Pro Thinks Differently (Well... Maybe)

One thing I’ve noticed from watching Pro’s reasoning summaries is that it uses code a lot more than I expected. Not just for coding tasks. For everything.

When I asked it to write a book, it used code to keep track of chapter names, chapter lengths, and the overall outline. It planned the entire structure programmatically before writing, then used code to build the final PDF.

For idea generation tasks, when it’s juggling a bunch of possibilities, it’ll put them into lists and data structures. It’s using code to organize its working memory... keeping track of what it’s considering, what constraints each option satisfies, what tradeoffs exist.

I don’t know if previous models were doing this internally and we just couldn’t see it, but the reasoning summaries definitely show much more code than I’ve seen before. Maybe OpenAI is just increasing transparency a bit. But it’s definitely a noticeable difference, at least for me.

When Pro Fails

Pro isn’t perfect. When it fails after thinking for a long time, it’s usually because it made a wrong assumption somewhere or misunderstood part of the problem. The output looks reasonable but doesn’t actually solve what you asked for, or solves a slightly different problem than you intended.

This is annoying specifically because of the time investment. Every so often, Pro will think for 45 minutes and then fail, and it wastes a ton of time. But it fails less often than previous models, and when you’re working on hard problems, some failure rate is unavoidable. Even people make wrong assumptions sometimes.

Overall, Pro gets it right more often than not, and more often than anything else I’ve used.

Prompting Pro Effectively

Because Pro thinks for so long, you really don’t want to get your prompt wrong. A mistake that costs you 30 seconds with a regular model costs you 30 minutes with Pro. So I approach Pro prompting differently.

First: be extremely clear. Spend time actually thinking about your prompt before you send it. What are you trying to achieve? What constraints matter? What does success look like? Cover everything you need to cover, because you don’t want to realize you forgot something important after Pro has been thinking for 20 minutes.

Second: add constraints. This is true for all reasoning models, but especially Pro. The more specific you are about what constitutes success... and what doesn’t... the better Pro can focus its thinking. Vague prompts get vague results. Constrained prompts get precise solutions.

Here’s a trick I use when I’m not sure if my prompt is complete: I give my original prompt to Claude Opus 4.5 first and ask, "Do you have any follow-up questions you’d need answered to actually complete this task?" Opus asks its questions, I answer them, then I say "Can you update my original prompt with these as context?" The refined prompt then goes to Pro.

You could also use GPT-5.2 Thinking for this prompt-refinement step, but it’s slower. Opus is faster for quick back-and-forth.

If you want some quick help with this, I have a GPT-5.2 Pro prompt builder available at shumerprompt.com that assembles these constraints for you.

Pro vs. Everything Else

I haven’t found a single task where standard GPT-5.2 Thinking beats Pro. That doesn’t mean Thinking is bad. It’s a good model, but if you have Pro access and time isn’t a constraint, Pro is just better.

Claude Opus 4.5 sometimes beats Pro, but it’s a matter of different strengths rather than one being universally better. I think Opus handles some creative writing tasks better. There’s a stylistic quality to its prose that I prefer. For quick, well-defined code changes where I know exactly what I want, I slightly prefer the code Opus 4.5 writes. It’s a small stylistic thing.

For quick research, obviously I’m not going to Pro, as I don’t want to wait 20 minutes for something I could get in 20 seconds. But for extensive research, where I need something researched and thought through deeply and carefully, Pro is where I go.

Pro is also definitely a better writer than GPT-5.2 Thinking. The thoughtfulness that goes into Pro’s reasoning translates into more nuanced, better-structured and more info-dense writing.

Pure prose quality still trails Claude Opus 4.5, but I often choose 5.2 Pro for writing anyway because it reasons more carefully; even if the wording is a touch less polished, the arguments are clearer and better supported.

Improvements Over 5.1 Pro

It’s not like GPT-5.2 Pro is a different breed of model from 5.1 Pro. I can’t point to any one thing that’s dramatically better. It just is overall somewhat better, and a bit more reliable across the board.

Part of this is that we’re getting to the edge of what we as humans can often evaluate outside of our own domains. In coding, I can see that it’s better. But if I’m asking a medical question, I’m not qualified to judge if 5.2 Pro is better than 5.1 Pro... they’re both so much smarter than me in that domain.

I’d say it’s probably 15% better across the board (which, for less than a month of progress, is pretty amazing). And it’s willing to think longer when it needs to, which is a huge win. It would be annoying if it thought longer on things where it didn’t need to, but I often find it’s around the same speed as 5.1 Pro on most tasks... it’s just on things that are extremely hard that it’s willing to go longer.

Is It Worth $200/Month?

The ChatGPT Pro plan costs $200/month and gives you essentially unlimited Pro queries. Whether that’s worth it depends entirely on how you work.

For me, it’s not even a question. I can’t live without GPT-5.2 Pro. I pay $200/month without thinking about it. I rely on this for my daily work in ways that would be hard to replicate with other tools.

But I’m not the average user. I’ve been using these models intensively for a long time. I know how to prompt them well. I’ve integrated AI into my workflows deeply enough that I constantly see opportunities to use it. I have friends who are dealing with something in their life that AI can help with, and it hasn’t even occurred to them to use it. For them, Pro probably isn’t worth $200/month. They wouldn’t get enough value out of it.

If you’re someone who uses AI seriously, who works on hard problems, who has learned to prompt effectively, and who would benefit from having access to the most capable reasoning available, Pro is worth it. If you’re still figuring out how to integrate AI into your work, you might want to get more comfortable with the standard (and much, much cheaper) tiers first.

Final Thoughts

GPT-5.2 Pro is roughly 15% better than 5.1 Pro... not a different breed of model, but a meaningful improvement on something that was already my favorite system. That 15% matters when you’re working at the edge of what these models can do.

What makes Pro special isn’t any single capability. It’s the willingness to think for as long as a problem requires, combined with reliability that lets me trust the output. When I send something to Pro and go make lunch, I’m genuinely confident that what I come back to will be good.

If you’ve been hesitant about the Pro plan, and you do the kind of work where getting hard things right matters more than getting quick things fast, I’d encourage you to try it.