AI Reasoning Models: What They Actually Do in 2026

I gave GPT-4 a logic puzzle in February. It got the wrong answer. Confidently wrong.

Gave the same puzzle to O1 reasoning mode. It took 23 seconds instead of 3 seconds. But it got it right, and it showed me every step of its thinking.

That's the difference between regular AI and reasoning AI. Speed versus correctness.

And when you're solving problems that actually matter, correctness wins.

What Reasoning Models Actually Do

Normal AI models generate answers fast. They pattern match based on training. Think of it like intuition. Quick, usually right, sometimes spectacularly wrong.

Reasoning models slow down and think. They break problems into steps, check their work, backtrack when something doesn't make sense.

O1 from OpenAI does this. So does Claude with extended thinking mode. Gemini Pro Deep has a version of it.

The trade is always the same. Slower answers but better logic.

The Math Test That Convinced Me

I tested this properly in March. Created 50 problems. Math, logic, coding challenges, strategic scenarios. Things that require multi-step thinking.

Ran them through GPT-4, Claude 5, and then their reasoning versions.

GPT-4 got 31 out of 50 right. Claude 5 got 34. Good, not great.

O1 got 47. Claude with extended thinking got 46.

The problems they solved were the hard ones. The ones where you need to think through implications, consider edge cases, check your assumptions.

On April 17th, I gave them this problem: "You have 12 balls, one is lighter. You have a balance scale and can use it three times. How do you find the light ball?"

GPT-4 suggested a method that only works if you get lucky. Claude 5 gave a strategy that takes four weighings.

O1 found the optimal three-weighing solution and explained why it works. Broke down each step, showed the decision tree, proved you can't do it in fewer weighings.

That's reasoning. Not just knowing the answer, but being able to construct it from first principles.

When Normal Models Break Down

Regular AI models struggle with things that require precise logic.

I asked GPT-4 to plan a road trip with 12 constraints. Budget limits, time windows, specific stops, dietary restrictions, the whole mess.

It gave me a plan that violated three of the constraints. It didn't notice because it was generating an answer that sounded good, not checking if it actually satisfied the rules.

O1 took that same problem, listed all the constraints, worked through them systematically, and produced a plan that worked. Took 41 seconds instead of 5, but I didn't need to fix it after.

This pattern keeps showing up. Tasks with hard constraints, tasks requiring math, tasks where the logic has to be perfect. Reasoning models shine.

The Cost Problem

O1 is expensive. Like, really expensive compared to regular GPT-4.

As of May 2026, O1 costs about 4x more per token. A query that costs $0.10 on GPT-4 might cost $0.40 on O1.

If you're doing high volume, that adds up fast.

I tracked my usage in April. I use AI for probably 200 queries a day. If I ran everything through O1, my bill would be around $2,400/month. Running most stuff through GPT-4 and only using O1 for complex problems costs me about $680/month.

So you need to be selective. Don't use reasoning models for simple stuff. Use them when correctness matters more than speed or cost.

Summarizing an email? Regular model. Planning a complex database migration? Reasoning model.

Learn to pick the right tool for the job.

The Transparency Thing

One advantage of reasoning models is they show their work.

When O1 solves a problem, you can see its thought process. Each step of logic. Where it considered alternatives. Where it corrected itself.

On May 3rd, I asked it to analyze a business decision. Should we build feature A or feature B first?

It laid out the factors. Development time, user value, technical dependencies, revenue impact. Weighted them. Explained the tradeoffs. Showed why it ranked feature B higher.

I actually disagreed with its conclusion. But seeing the reasoning helped me understand what I was disagreeing with. Made my own thinking clearer.

That's valuable even when you don't follow the AI's recommendation. It's like having a really smart colleague who writes out their entire thought process.

Regular models just give you answers. Reasoning models give you arguments. Arguments you can challenge and improve.

Where They Still Fail

Reasoning models aren't perfect. They make mistakes.

They overthink simple problems. I asked O1 what 2+2 equals and it spent 8 seconds considering whether there might be trick aspects to the question. Sometimes fast and simple is better.

They can reason toward wrong conclusions if the premise is wrong. Garbage in, garbage out. If you give them false information, they'll build logical arguments on top of it.

And they're slower. Way slower. There are situations where you need an answer right now and waiting 30 seconds for perfect logic isn't worth it.

I learned this the hard way in a live demo. Asked O1 a question in front of a client, waited awkwardly for 25 seconds while it thought. Looked silly. Should've used regular GPT-4 for a quick answer.

Know the context. Fast and good enough beats slow and perfect when timing matters.

The Code Debugging Use Case

I write a lot of code. Reasoning models changed how I debug.

Regular AI is great at writing code. But when code has a subtle bug, it often misses it or suggests fixes that don't address the root cause.

O1 is better at this. I showed it a function with a race condition. GPT-4 suggested adding a delay, which is treating the symptom. O1 identified the actual race condition, explained why it happens, and proposed a proper fix with locks.

On April 29th, I had a bug I'd been chasing for three days. Performance issue that only showed up under specific load patterns. Gave O1 the code and the symptoms.

It reasoned through what could cause intermittent slowness, identified a database query that was missing an index, explained why the query planner was choosing a slow path, and suggested the fix.

Took 2 minutes. Saved me probably another day of debugging.

For code that's already written and needs analysis, reasoning models are significantly better than standard ones.

Strategic Planning Scenarios

I helped a friend with a business plan in March. He was trying to decide between two market approaches.

I fed the details to Claude with extended thinking. Revenue projections, cost structures, competitive landscape, all the context.

It analyzed both paths, identified risks in each, showed how different assumptions changed the outcome, and explained which factors were most sensitive to change.

Better than spreadsheet modeling alone because it considered qualitative factors too. Market timing, team strengths, strategic positioning. Stuff that's hard to quantify but matters.

My friend ended up going with the option Claude ranked lower. But the analysis helped him see what he was betting on by choosing that path. Made the decision deliberate instead of instinctive.

That's the value. Not making the decision for you, but helping you think through it more rigorously.

Legal and Compliance Stuff

Companies are using reasoning models for compliance review. Going through policies, regulations, contracts.

A lawyer I know uses O1 to check contracts for issues. It reads the whole document, identifies clauses that might conflict with regulations, flags vague language that could cause problems later.

She still reviews everything herself. But the AI does the first pass and catches things she might miss in a 80-page agreement.

On May 9th, she showed me an example. Contract had a clause that seemed fine but conflicted with a regulation from 2024. O1 caught it, cited the specific regulation, explained why it was a problem.

Would she have caught it? Probably, eventually. But the AI found it in 40 seconds instead of after she'd spent two hours reading.

For work where missing details has real consequences, that's worth the extra cost and time.

Scientific Research Applications

Researchers are using reasoning models to analyze experiments and plan studies.

A biologist I know describes experiments to Claude with extended thinking. The AI helps identify confounding variables, suggests controls, points out assumptions that need testing.

It's not doing the science. But it's like having a colleague who's really good at spotting holes in methodology.

On May 14th, she was designing a study on gene expression. Claude identified three variables she hadn't controlled for. Two of them turned out to be important when she ran pilot tests.

Saved her from running a full study with a flawed design. That's months of wasted work avoided.

Science is about rigorous thinking. Reasoning AI is pretty good at rigorous thinking. Natural fit.

Education and Learning

I've been using reasoning models to understand complex topics.

When I'm learning something new, I ask O1 to explain it and show its reasoning. Not just what, but why.

Last month I was trying to understand Byzantine fault tolerance in distributed systems. Dense topic, lots of papers written in academic language.

I asked O1 to explain it. It broke down the problem, showed why simpler solutions don't work, walked through how the algorithm handles different failure modes.

Then I asked follow-up questions. It answered while building on what it had already explained. By the end, I understood the core concept and could explain it myself.

Better than reading papers because it was interactive. Better than normal AI because the explanations were logically constructed, not just pattern-matched summaries.

For anyone learning complex subjects, reasoning models are surprisingly good teachers.

The Competition Between Models

O1, Claude extended thinking, and Gemini Pro Deep all work slightly differently.

O1 is strongest on pure logic and math. If your problem is mostly quantitative reasoning, O1 tends to be best.

Claude with extended thinking is better at nuanced analysis. It handles ambiguity well, considers multiple perspectives, explains tradeoffs clearly.

Gemini Pro Deep is good at research-heavy reasoning. When the problem requires pulling in facts and connecting them logically, Gemini shines.

I use all three depending on the task. Haven't found one that's universally better.

The field is moving fast. By the time you read this, there are probably newer models with better reasoning capabilities.

But the core concept stays the same. Slower, more careful thinking for problems where correctness matters.

When Not to Use Them

Don't use reasoning models for creative work. They're logical, not creative.

I asked O1 to write a story. It produced something technically correct but completely boring. No personality, no risk, no spark.

Regular GPT-4 writes better creative content. It's less rigorous but more interesting.

Don't use them for brainstorming. You want wide exploration, not deep logical analysis.

Don't use them for simple lookups or summaries. Overkill. You're paying for logic you don't need.

And don't use them when you need instant responses. The thinking time is a real tradeoff.

My Current System

I default to regular models for 80% of my AI usage. Fast, cheap, good enough.

For the 20% where logic matters, I switch to reasoning models.

Complex coding problems. Strategic decisions. Anything involving math or hard constraints. Reviewing important documents. Planning complicated projects.

I also use reasoning models when I'm stuck. If I've been working on a problem for an hour and not making progress, I'll throw it at O1. Sometimes a different approach to thinking about it helps.

The key is being deliberate. Don't just always use the fanciest model. Match the tool to the task.

What This Means for the Future

Reasoning capabilities will keep improving. Models will get faster at it, cheaper to run, better at more types of problems.

I think in two years, reasoning modes will be the default for complex tasks. The cost will come down, the speed will improve, and we'll wonder how we managed without them.

But the fundamental trade will stay the same. Fast intuitive answers versus slow logical ones.

Sometimes you need to think fast. Sometimes you need to think carefully.

Now we have AI that can do both. We just need to learn when to use which.

For me, that's the skill worth building. Not how to use any specific model, but how to choose the right type of thinking for the problem in front of me.

Fast when speed matters. Careful when correctness matters. That's the judgment call.

And honestly, that's a pretty human skill. Knowing when to trust your gut and when to slow down and reason it through.

Now we just have AI that can match either mode. Use it well.