Reasoning Models: How AI Thinks Step by Step

Here’s something that used to frustrate me about early AI assistants: you’d ask a complex question, and the model would just… blurt out an answer. No hesitation, no working through the problem, just instant (and often wrong) confidence. It was like asking someone to solve a calculus problem and having them immediately shout a number without showing any work.

That changed with the rise of reasoning models—AI systems specifically designed to think step by step before giving you an answer. These models don’t just pattern-match to likely outputs. They deliberately work through problems, breaking them into manageable pieces and checking their logic along the way.

In 2026, reasoning has become a defining feature of frontier AI. Models like GPT-5.2 Thinking, Claude 4’s extended thinking mode, and specialized systems like DeepSeek-R1 can tackle problems that would stump traditional LLMs. But they’re not always the right tool, and understanding when to use them—and how they actually work—can make a real difference in your results.

What Are Reasoning Models?

At their core, reasoning models are large language models designed to engage in explicit, step-by-step deliberation before producing an answer. Instead of mapping input directly to output, they generate intermediate “thoughts” that work through the problem.

Traditional LLMs work like this:

Read the input
Predict the most likely next tokens
Output an answer

Reasoning models add a crucial step:

Read the input
Think through the problem step by step (often in a hidden “scratchpad”)
Verify logic and check for errors
Output a refined answer

This might sound obvious—“of course you should think before answering.” But for neural networks, it’s not automatic. Standard LLMs are trained to predict the next token given the previous ones, which pushes them toward plausible-sounding responses even when careful reasoning would produce different (and better) answers.

Reasoning models explicitly allocate compute to thinking. They’re willing to take longer to produce better answers, and they’re designed to catch the kinds of errors that slip through when you just go with your first instinct.

The Key Difference: Deliberation

The technical term for this is deliberative alignment. Instead of generating the most statistically likely response, reasoning models deliberately consider multiple approaches, evaluate their validity, and select the best path forward.

You can think of it like the difference between:

System 1 thinking: Fast, intuitive, automatic (standard LLMs)
System 2 thinking: Slow, deliberate, logical (reasoning models)

Most of us use System 1 for daily tasks—we don’t carefully analyze every decision. But for hard problems—math, logic, multi-step planning—System 2 is essential. Reasoning models bring System 2 capabilities to AI.

How Chain-of-Thought Reasoning Works

The foundational technique behind reasoning models is chain-of-thought (CoT) prompting, first popularized in research around 2022 and now built into model architectures themselves.

The Basic Mechanism

Chain-of-thought works by having the model generate intermediate reasoning steps before the final answer. Let’s see a simple example:

Without chain-of-thought:

Question: If a store has 15 apples and sells 7 in the morning and 4 in the afternoon, how many are left?

Standard LLM: 4 apples.

With chain-of-thought:

Question: If a store has 15 apples and sells 7 in the morning and 4 in the afternoon, how many are left?

Reasoning Model: Let me work through this step by step.

Start: 15 apples

Morning sales: 15 - 7 = 8 apples remaining

Afternoon sales: 8 - 4 = 4 apples remaining

Answer: 4 apples

In this simple case, both get the right answer. But for complex problems, chain-of-thought dramatically improves accuracy because:

Errors surface visibly — If the model makes a mistake at step 2, it’s clear
Complex problems decompose — Multi-step problems become sequences of simple steps
Self-correction happens — The model can catch its own errors mid-stream
Working memory expands — Each step extends the effective memory for the problem

From Prompting to Architecture

Originally, chain-of-thought was something you triggered through prompting—you’d write “Let’s think step by step” and hope the model would comply. Modern reasoning models bake this into the architecture:

Dedicated reasoning tokens: Special tokens that indicate “thinking mode”
Extended internal computation: More processing time allocated to complex queries
Interleaved reasoning: Thinking happens before every tool call or response
Retention-based reasoning: Reasoning chains persist across conversation turns

The result is that you don’t need to prompt for reasoning anymore—the model automatically engages its deliberative capabilities when encountering complex problems.

Thinking Tokens and Hidden Scratchpads

Many reasoning models use “hidden” thinking that you don’t see in the output. GPT-5.2 Thinking, for example, generates an internal chain-of-thought that influences the final answer but isn’t shown to the user unless you specifically request it.

This addresses a practical concern: reasoning chains can be verbose. For a simple question, you don’t want paragraphs of deliberation before a one-word answer. The model reasons internally, then provides a concise response—best of both worlds.

Major Reasoning Models in 2026

The reasoning model landscape has evolved significantly. Here are the key players:

GPT-5.2 Thinking (OpenAI)

OpenAI’s latest reasoning variant builds on the o1 model architecture introduced in 2024. Key features:

Extended thinking time: Can process for seconds or even minutes on hard problems
Internal chain-of-thought: Reasoning happens in a hidden scratchpad
Strong math/coding performance: Excels at STEM problems
Adjustable reasoning depth: API allows tuning how much to “think”

GPT-5.2 Thinking particularly shines on competition mathematics, complex coding challenges, and multi-step logical problems. The tradeoff? It’s slower and costs more than standard GPT-5.

Claude 4 Extended Thinking (Anthropic)

Anthropic’s approach to reasoning emphasizes transparency and reliability:

Visible reasoning option: You can see Claude’s thought process
Constitutional limits: Reasoning stays within ethical guidelines
Strong at nuanced problems: Particularly good at tasks requiring context sensitivity
200K+ context with reasoning: Long documents + complex analysis

I’ve found Claude’s reasoning mode particularly useful for tasks where I want to understand how it reached a conclusion, not just what the conclusion is.

Gemini 3 Pro with Reasoning

Google’s multimodal approach means Gemini can reason across text, images, and code:

Multimodal reasoning: Can think about visual and textual data together
2M token context: Reason over entire codebases or document collections
Integration with Google tools: Search-augmented reasoning

Gemini’s strength is applying reasoning to multimodal problems—analyzing a complex diagram while also reasoning through textual requirements, for instance.

DeepSeek-R1 and Open Source Options

The open-source reasoning ecosystem has matured:

DeepSeek-R1: Chinese lab’s strong reasoning model, open weights
Qwen-3 with CoT modes: Multiple reasoning strategies available
Kimi K2 Thinking: Trillion-parameter MoE with reasoning focus
Mistral’s reasoning variants: Smaller but efficient reasoning models

For local deployment or fine-tuning, these options let you run reasoning models without API dependencies.

When Reasoning Models Outperform Standard LLMs

Reasoning models aren’t universally better—they’re specifically better at certain problem types. Understanding when to use them saves time and money.

Where Reasoning Excels

Complex mathematics and logic: Problems requiring multiple steps, variable tracking, or formal proof structures. Standard LLMs often get these wrong through simple errors that compound; reasoning models catch and correct such errors.

Code debugging and analysis: When diagnosing bugs, reasoning models can trace through code execution step by step, maintain state, and identify root causes more reliably than quick-response models.

Strategic planning: Multi-step tasks with dependencies, trade-offs, and constraints benefit from deliberate reasoning. Project planning, game strategy, resource allocation.

Scientific reasoning: Hypothesis formulation, experimental design, and causal analysis require the kind of systematic thinking reasoning models provide.

Legal and policy analysis: Applying multiple rules to specific situations, identifying conflicts, and synthesizing precedents—tasks where logical structure matters.

Where Standard LLMs Are Fine

Simple queries: “What’s the capital of France?” doesn’t need chain-of-thought deliberation.

Creative writing: Reasoning can actually harm creative output by making it feel mechanical. Standard LLMs often produce more flowing, natural prose.

Casual conversation: Overthinking casual chat makes responses feel stilted. Standard models tend to be more natural.

Speed-critical applications: When response time matters more than marginal accuracy improvements, standard LLMs are often the right choice.

The Cost-Benefit Tradeoff

Reasoning models typically cost 2-5x more than standard models in API pricing and take 2-10x longer to respond. For high-stakes problems where accuracy matters, that’s worth it. For bulk processing of simple tasks, it’s wasteful.

Limitations of Reasoning AI

Despite the hype, reasoning models have real limitations that you should understand.

Reasoning Doesn’t Equal Understanding

Here’s a subtle but important point: generating step-by-step text that looks like reasoning isn’t the same as actually understanding a problem. These models are still statistical systems that predict likely token sequences.

A reasoning model might produce a perfectly-formatted chain-of-thought that leads to a wrong answer. The reasoning can be plausible without being correct. Just because the model “shows its work” doesn’t mean you can trust that work.

Slow and Expensive

Extended thinking takes time—sometimes significant time. For real-time applications, this matters. And you’re paying for all those extra tokens, even the ones in hidden scratchpads.

For cost-sensitive applications processing millions of requests, reasoning models might be prohibitively expensive.

Overthinking Simple Problems

Reasoning models can actually make simple problems worse by overcomplicating them. When the intuitive answer is right, deliberating extensively adds noise without improving accuracy.

This is why production systems often route queries: simple questions go to fast models, complex ones go to reasoning models.

Training Data Limits Still Apply

Reasoning doesn’t help with knowledge gaps. If the model doesn’t have domain expertise—say, specialized medical or legal knowledge—reasoning about it won’t magically create correct answers. You can have perfectly valid reasoning built on incorrect premises.

Hallucinated Reasoning Steps

Perhaps most concerning: models can hallucinate reasoning steps that sound plausible but are factually wrong. A model might “remember” a theorem that doesn’t exist, or cite a fake precedent in legal reasoning. The chain-of-thought format makes these errors more convincing, not less.

Practical Applications of Reasoning Models

Let’s look at where reasoning models are actually being deployed effectively:

Software Development

Code debugging: Reasoning models can trace through execution paths, identify where logic goes wrong, and suggest fixes. They’re particularly good at bugs that require understanding control flow and state.

Architecture decisions: Evaluating trade-offs between design approaches, considering scalability, maintainability, and performance factors systematically.

Code review: Rather than just pattern-matching to common issues, reasoning models can think through edge cases and potential failure modes.

Education and Tutoring

Step-by-step explanations: Obviously valuable for showing students how to solve problems, not just giving answers.

Identifying misconceptions: Reasoning through a student’s work to find where their thinking went wrong.

Adaptive difficulty: Analyzing performance patterns to adjust challenge levels.

Research and Analysis

Literature synthesis: Working through multiple sources, identifying connections and contradictions.

Hypothesis generation: Systematically considering what might explain given observations.

Data interpretation: Reasoning about what results mean and what conclusions are warranted.

Autonomous Agents

Multi-step task execution: AI agents using reasoning can plan sequences of actions, handle errors, and adapt to unexpected situations.

Tool selection: Reasoning helps agents determine which tools to use and in what order.

Self-debugging: Agents can reason about why an approach failed and try alternatives.

How to Use Reasoning Models Effectively

Some practical advice from working with these systems:

When to Invoke Reasoning

Don’t use reasoning models for everything. Save them for:

Problems with multiple steps
Tasks where accuracy matters more than speed
Situations where you need to understand the AI’s thinking
Problems that require comparing alternatives

Prompting for Better Reasoning

Even with built-in reasoning, your prompts matter:

Be explicit about complexity: “This is a multi-step problem. Take your time and work through it carefully.”

Encourage verification: “After reaching an answer, double-check your work for errors.”

Request visible reasoning: “Show your reasoning step by step before giving the final answer.”

Verify Reasoning Chains

Don’t blindly trust chain-of-thought outputs. Check that:

Each step follows logically from the previous
The model isn’t making up facts or “remembering” things incorrectly
The final answer actually follows from the reasoning shown

Combine with Tools

Reasoning models work best when combined with tools that provide ground truth:

Calculators for arithmetic (don’t trust models to add reliably)
Code execution for programming problems
Search for factual verification

The reasoning guides the process; the tools ensure accuracy.

Frequently Asked Questions

What’s the difference between reasoning models and regular LLMs?

Reasoning models explicitly allocate computation to thinking through problems step by step before producing answers. Standard LLMs generate the most statistically likely response more directly. Reasoning models are slower and more expensive but significantly more accurate on complex, multi-step problems like math, logic, and code debugging.

Can I make any LLM reason by prompting?

Partially. Adding “Let’s think step by step” to prompts triggers chain-of-thought behavior in most models. However, purpose-built reasoning models have architectural features that make reasoning more reliable and effective. Prompting helps, but dedicated reasoning models consistently outperform prompted standard models on complex tasks.

Are reasoning models always more accurate?

No. For simple questions with obvious answers, reasoning models can overthink and introduce errors. They also remain susceptible to knowledge gaps—reasoning perfectly about incorrect facts still produces wrong answers. Reasoning models excel at problems requiring multi-step logic; for everything else, standard models may be just as good or better.

Why can’t I always see the reasoning?

Many reasoning models use hidden “scratchpad” computation for efficiency. Showing all reasoning would make responses extremely long for complex problems. Most APIs offer options to expose reasoning when you want it. The hidden approach lets models think extensively without overwhelming users with verbose outputs.

How do reasoning models handle tasks they’re not trained for?

Better than standard models, but still imperfectly. The ability to break problems into steps helps with novel situations—reasoning about unfamiliar problems can work if the component steps are within the model’s capability. However, true domain expertise requires training data, not just reasoning ability. Reasoning isn’t magic.

Conclusion

Reasoning models represent a genuine advancement in AI capability—the shift from “first instinct” responses to deliberate, step-by-step thinking. For complex problems in math, coding, planning, and analysis, they significantly outperform traditional LLMs.

The key insight is knowing when to use them. Not every query needs extended deliberation. But when you encounter problems that require careful thinking—multi-step logic, debugging tricky code, synthesizing complex information—reasoning models are the right tool.

In 2026, reasoning capability has moved from research novelty to production reality. GPT-5.2 Thinking, Claude 4’s extended thinking mode, and open-source options like DeepSeek-R1 all bring System 2 thinking to AI. The result is AI that can tackle problems that stumped earlier models—not by brute force, but by actually thinking through them.

Interested in prompting techniques that encourage better reasoning? Check out our guide to chain-of-thought prompting to learn how to get the most out of any LLM’s reasoning capabilities.