OpenAI o1 Model Thinks for 30 Seconds Before Answering. Is It Worth the Wait?

I asked OpenAI’s new o1 model to solve a logic puzzle. It thought for 34 seconds before responding. Thirty-four seconds of just… waiting. Watching a loading spinner while an AI “thinks.”

Then it gave me a perfect answer, complete with step-by-step reasoning that actually made sense.

This is the new frontier: AI that pauses before it speaks. OpenAI calls it “reasoning.” Basically, instead of instantly spitting out tokens like GPT-4, o1 runs internal chains of thought—testing hypotheses, checking its work, reconsidering conclusions.

The result? It destroys GPT-4 on complex tasks. Math Olympiad problems, coding challenges, scientific reasoning—o1 scores in the 89th percentile among competitive programmers. GPT-4? 11th percentile.

But here’s the thing: it’s slow as hell.

I’m testing it for a client who wanted help debugging production code. The bug was subtle—a race condition that only appeared under specific load. I gave o1 the codebase context and asked for analysis.

Three minutes later—yes, three actual minutes—it identified the exact issue, explained why traditional debugging wouldn’t catch it, and proposed a fix. The fix worked. First try.

For comparison, I’d spent six hours on this bug. o1 solved it in three minutes. Technically slower than GPT-4 would respond, but infinitely faster than me figuring it out.

The trade-off is brutal though. For simple questions, o1 is overkill. I asked it “What’s the capital of France?” It thought for 12 seconds before confidently stating “Paris.” That’s embarrassing. GPT-4 answers instantly.

OpenAI’s pitch is that reasoning models excel at tasks requiring deep thought: scientific research, complex coding, advanced mathematics. They’re right. A Stanford researcher told me o1 helped design a protein structure that their team had been stuck on for months. The AI proposed a configuration they hadn’t considered.

But the cost. Oh, the cost. o1 is expensive—like, an order of magnitude more expensive than GPT-4. Each query burns through tokens during its “thinking” process, even though you don’t see that output. You’re basically paying for the AI to have internal monologues.

I ran a test: 100 customer support tickets through o1 versus GPT-4. o1 was more accurate—fewer hallucinations, better reasoning. But it cost $47 versus $3.50 for GPT-4. Unless your customer support tickets require PhD-level reasoning, that math doesn’t work.

DeepMind released a competing model called Gemini 1.5 Pro with “extended thinking.” Google’s version is faster but less accurate on really hard problems. Everyone’s racing to build better reasoning engines.

The paradox is fascinating: we spent years making AI faster, and now we’re deliberately slowing it down to make it smarter. It’s like the difference between blurting out an answer in class versus actually thinking through the problem first.

An AI researcher at Berkeley told me the real breakthrough isn’t speed—it’s reliability. “GPT-4 sometimes gives you genius insights and sometimes complete nonsense. You never know which. o1 is more consistently correct on hard problems. That reliability is worth paying for.”

Maybe. But I’m not paying $47 per query to ask about Paris.

The sweet spot seems to be using o1 for genuinely complex tasks—code reviews, research analysis, strategic planning—and keeping GPT-4 for everything else. It’s like having a specialist consultant you only call for the hard stuff.

One thing’s certain: the era of instant AI responses is evolving. We’re entering the age of AI that actually thinks before it talks. Whether that’s worth the wait depends entirely on what you’re asking.