What Reasoning Gen AI Models do for you?

What Reasoning Gen AI Models do for you?

Ever wished your AI assistant would stop and think before blurting out an answer? Good news — the latest generation of AI does exactly that, and it's changing everything about how we solve problems.

The tale of two students

Imagine two students in a classroom. When the teacher asks a difficult question, one immediately raises their hand and gives a confident but sometimes wrong answer. The other takes a moment, works through the problem step-by-step on paper, double-checks their work, and then offers a measured response that's almost always correct.

That's the difference between traditional Large Language Models (LLMs) and the new breed of reasoning LLMs reshaping what artificial intelligence can do.

What makes reasoning AI different?

Let's cut through the jargon. Traditional LLMs (like earlier versions of ChatGPT) are pattern-matching machines. They're excellent at producing quick, fluent responses based on what they've learned, but they generate answers by predicting the next likely words—without any deep problem-solving process.

Reasoning LLMs (like OpenAI's GPT-o1 or GPT-o3-mini), on the other hand, do something remarkably different. Before giving you an answer, they engage in what experts call "chain-of-thought reasoning"—essentially, they think through problems step-by-step, just like you and I would when tackling something complex.

This simple difference creates a massive capability gap for tasks requiring logic, multi-step analysis, or drawing insights from multiple data points.

The four big differences that matter

• Problem-solving method
  Traditional LLM: Like a student who memorized the textbook but doesn't truly understand the concepts, traditional LLMs generate answers directly based on learned patterns. They jump straight to what seems like the most likely answer.
  Reasoning LLM: These models work through problems internally in logical steps before answering. They consider details, check multiple angles, and only then deliver a response.
  Real-world example: Imagine calling tech support because your phone can't make outgoing calls, but weirdly, it can receive calls and has internet access.
  A traditional LLM chatbot might suggest: "Try restarting your phone or check if you have call blocking enabled." It's not a bad answer, but it's basically guessing common fixes.
  A reasoning LLM would think differently: "Let's see... outgoing calls fail, but incoming calls work, and the internet works. If internet and incoming services work, the phone is connected to the network and the account is active. So, the issue must be specifically with outgoing services. This points to either an account setting blocking outgoing calls or a billing limit that's been reached." Now that's actual troubleshooting!
• Handling complex tasks
  Traditional LLM: These struggle with multi-step problems. They may skip steps or produce incorrect answers because they don't verify each part of their reasoning.
  Reasoning LLM: These excel at multi-step reasoning and planning, breaking complex tasks into manageable sub-tasks and verifying each step along the way.
  Real-world example: A financial services company needed to calculate complex shareholder dilution—a task that would take a skilled human analyst about 20-30 minutes.
  A traditional LLM produced a superficial answer with calculation errors because it tried to jump straight to the conclusion.
  A reasoning LLM solved it flawlessly, methodically working through each calculation and producing the correct detailed results. For intensive analytical work, the reasoning model gets it right where a traditional model would stumble.
• Depth and thoroughness
  Traditional LLM: Prioritizes fluency and brevity. Answers are typically short and natural-sounding but might miss specific details or nuances.
  Reasoning LLM: Provides detailed, structured answers for complex queries, thinking through every aspect to create comprehensive responses.
  Real-world example: A customer emails a company with multiple issues: they want a refund for a faulty product, they mention a billing error from last month that was supposedly fixed but they're unsure, and they ask about upgrading to a premium service.
  A traditional LLM might draft a polite email that sounds professional but glosses over details: "We've processed your refund. Regarding the billing error, we apologize and have corrected it. You can upgrade through our app... " It sounds fine but might promise a refund without checking eligibility.
  A reasoning LLM would systematically address each concern, verifying refund eligibility, checking notes on the billing error resolution, and providing detailed upgrade instructions. The result is a complete response that doesn't require follow-up emails.
• Error checking and reliability
  Traditional LLM: Has limited self-correction capabilities and rarely double-checks itself mid-response. It often sounds confident even when wrong.
  Reasoning LLM: Self-verifies steps and catches mistakes internally. If something doesn't add up during its reasoning process, it can revise before giving the final answer.
  Real-world example: When asked to create a meeting schedule that accommodates multiple constraints (like available rooms, participant preferences, and upcoming holidays), a traditional LLM might produce a plausible-looking schedule that contains subtle conflicts—like scheduling an important meeting on a public holiday.
  A reasoning LLM would catch such errors during its planning process. It would notice the holiday conflict, adjust the plan, and produce a schedule that actually works for everyone.

Types of reasoning: Brain's different tools

Not all thinking is created equal. Different problems require different thinking approaches, and reasoning LLMs are developing capabilities across several types:

• Deductive reasoning: From general rules to specific conclusions
  This is like Sherlock Holmes-style thinking—applying general rules to specific situations.
  Example: If all smartphones need charging (general rule) and you have a smartphone (specific situation), then your phone will need charging (conclusion).
• Inductive reasoning: From specific observations to general patterns
  This is how we learn from experience—noticing patterns across multiple examples.
  Example: If every time you eat at a certain restaurant you get indigestion (specific observations), you might conclude that something about their food doesn't agree with you (general pattern).
• Abductive reasoning: Finding the most likely explanation
  This is detective work—looking for the most probable explanation for observations.
  Example: If your car won't start and the dashboard lights aren't coming on (observations), you might guess that the battery is dead (most likely explanation) rather than assuming the entire electrical system failed.
• Analogical reasoning: Using similar situations to solve new problems
  This is solving problems by recalling similar situations.
  Example: If you've successfully assembled IKEA furniture before, you can apply that experience when putting together a new piece, even if it's different.

Reasoning LLMs are becoming increasingly proficient at all these types of thinking, though they still perform better at some types (like deductive reasoning) than others (like analogical reasoning with truly novel concepts).

How reasoning gets built into AI

How do developers create AI that can think? Several methods that are being used today:

• Inference-time compute scaling
  This is like giving the AI more time to think before answering. The model runs internal calculations multiple times, refining its answer before responding.
• Pure reinforcement learning
  The AI learns through trial and error with feedback on its reasoning. It's like teaching a child by saying "good job" when they solve a problem correctly.
• Reinforcement learning + supervised fine-tuning
  This combines learning from feedback with learning from examples. It's like a student both practicing problems and studying worked examples.
• Pure supervised fine-tuning + distillation
  This teaches smaller models to mimic the reasoning capabilities of larger models. It's like a master teacher training apprentice teacher.

Don't worry about the technical details—the important thing is that researchers are using multiple approaches to build thinking capabilities into AI.

Prompt engineering: Getting the best from different types of AI

How you ask questions greatly affects the quality of answers you get. Here's how to adapt your approach when working with different types of AI:

Traditional LLM prompting

When using traditional LLMs, you'll often need to be explicit about the steps you want the AI to take:

• Poor prompt: "Evaluate this investment portfolio."
• Better prompt: "Evaluate this investment portfolio step by step. First analyze the asset allocation, then check sector exposure, then identify any concentration risks, and finally suggest three specific improvements."

The traditional LLM needs explicit instructions to follow a structured approach, as it doesn't naturally break problems down into steps.

Reasoning LLM prompting

With reasoning LLMs, you can focus more on the problem itself and less on how to solve it:

• Effective prompt: "I need to understand the risks in this investment portfolio. It contains 60% stocks and 40% bonds, with heavy tech sector exposure. What should I be concerned about?"

The reasoning LLM will naturally:

• Break down the evaluation into logical steps.
• Identify obvious and non-obvious risks.
• Consider correlations between holdings.
• Provide holistic analysis without requiring step-by-step instructions.

The key difference is that you can spend less time structuring the AI's thinking process and more time focusing on clearly stating your problem and constraints.

GPT-o1 vs. GPT-o3-mini: Different approaches to reasoning

OpenAI's reasoning models have made headlines, but they take different approaches to the challenge of "thinking before answering."

GPT-o1: The thorough analyst

GPT-o1 is designed as a heavyweight reasoning model that excels at complex analytical tasks:

• Strengths:
• Extremely thorough verification of its own reasoning.
• Exceptional at multi-step problems like complex calculations and logical puzzles.
• Strong performance on data analysis, coding challenges, and mathematical reasoning.

Example in action: When given a complex tax calculation involving multiple deductions, credits, and income sources, GPT-o1 methodically worked through each component:

"First, I'll calculate the adjusted gross income by combining the salary ($78,500), dividends ($3,200), and subtracting the qualified business income deduction ($4,750) ...
Next, I'll apply the standard deduction of $12,950 for a single filer...
For the child tax credit, we need to check income phaseout thresholds..."

It continued step-by-step through eight distinct calculation phases, double-checking its math at each stage, before providing the final tax liability figure.

GPT-o3-mini: The efficient reasoner

GPT-o3-mini represents a more balanced approach, designed to be faster while still maintaining solid reasoning capabilities:

• Strengths:
• Quicker response times than GPT-o1.
• Good balance between reasoning and efficiency.
• More accessible for everyday use cases where speed still matters.

Example in action: Given the same tax calculation, GPT-o3-mini identified the key components that would most impact the result:

"I'll focus on the major factors for this tax situation. The AGI comes to $76,950 after adjustments. After applying the standard deduction, we have $64,000 of taxable income.
Based on 2022 tax brackets for a single filer, this would put most income in the 22% bracket, with a base tax of approximately $10,850 before credits.
The child tax credit of $2,000 would reduce this to roughly $8,850 in federal tax liability."

While less detailed, GPT-o3-mini still applied sound reasoning to produce an accurate result more efficiently.

The choice between these models reflects the classic tradeoff between thoroughness and speed. For high-stakes analysis like financial modeling or complex diagnosis, the deeper reasoning of GPT-o1 might be worth the wait. For everyday problem-solving where "good enough" reasoning is sufficient, GPT-o3-mini offers a more practical balance.

Real industries being transformed today

Let's explore three different industries to see how reasoning LLMs are creating breakthroughs that traditional LLMs simply cannot match.

Healthcare: Diagnostic assistance that saves lives

The challenge: A patient presents with unusual symptoms that don't neatly fit into common diagnostic patterns: persistent fatigue, intermittent fever, unexplained weight loss, and joint pain that migrates between different joints.

Traditional LLM in the clinic: "Based on these symptoms, possible diagnoses include chronic fatigue syndrome, fibromyalgia, Lyme disease, or early rheumatoid arthritis. I recommend blood tests for inflammatory markers and autoimmune antibodies."

This covers common conditions, but it's essentially pattern-matching symptoms to diseases without truly analyzing their interrelationships.

Reasoning LLM as a medical consultant: The reasoning model approaches this like an experienced diagnostician:

• It systematically analyzes the symptom pattern: "The combination of fatigue, fever, weight loss, and migratory joint pain suggests a systemic inflammatory or infectious process."
• It considers the temporal relationships: "The migratory nature of joint pain is particularly significant as it's characteristic of certain inflammatory conditions."
• It prioritizes possibilities based on clinical significance: "While these symptoms overlap with chronic fatigue syndrome, the presence of objective fever and migratory joint pain points more strongly toward conditions like adult-onset Still's disease or atypical presentations of rheumatic fever."
• It suggests targeted diagnostic steps: "I recommend specific testing for ferritin levels and inflammatory markers, which would be elevated in Still's disease, along with throat cultures to rule out streptococcal infection that could indicate rheumatic fever."

A physician who tested both types of AI assistants reported that the reasoning LLM caught a rare diagnosis that might have been missed or significantly delayed otherwise.

Financial services: Portfolio analysis that uncovers hidden risks

The challenge: A financial advisor needs to review a client's complex investment portfolio to identify risks and opportunities ahead of a quarterly review meeting.

Traditional LLM financial review: "Your portfolio has a 60/40 stock/bond split which is generally considered balanced. Your tech sector exposure is high at 35% of equity holdings, which could be risky in a downturn. Consider diversifying into more defensive sectors."

This analysis sounds reasonable but stays at a surface level. It identifies obvious concentration risk but misses deeper correlations.

Reasoning LLM financial analysis: The reasoning model examines the portfolio like a seasoned financial analyst:

• It identifies hidden correlations: "While your portfolio appears diversified across asset classes, I notice that 65% of your holdings have high exposure to interest rate sensitivity—not just your bonds, but also your real estate investments and certain tech stocks that behave like long-duration assets."
• It simulates scenarios: "In a rising interest rate environment, multiple segments of your portfolio could decline simultaneously. Your cryptocurrency holdings historically show correlation with tech stocks during market stress, potentially amplifying downside risk."
• It uncovers timing risks: "I notice that 30% of your bond holdings mature within 6 months of each other. This creates reinvestment risk if rates are low when these bonds mature."
• It provides actionable recommendations: "Consider laddering your bond maturities more evenly, reducing technology exposure by 12-15%, and adding 5-8% allocation to consumer staples which would reduce your portfolio's overall interest rate sensitivity by approximately 22%."

A wealth management firm testing both approaches found that the reasoning LLM's analysis matched or exceeded what their human analysts typically produced in hours of work.

Manufacturing: Supply chain optimization that anticipates problems

Challenge: A manufacturing company wants to optimize its global supply chain in the face of increasing disruptions from climate events, geopolitical tensions, and transportation bottlenecks.

Traditional LLM Supply chain advice: "To improve supply chain resilience, consider diversifying suppliers, increasing safety stock for critical components, and implementing real-time tracking systems. Develop contingency plans for major disruption scenarios."

While this advice follows supply chain best practices, it's generic and fails to analyze the company's specific vulnerabilities.

Reasoning LLM supply chain strategy: The reasoning model approaches this like a supply chain consultant with systems thinking:

• It analyzes vulnerability patterns: "Looking at your past disruptions, 78% of your major delays stemmed from just three components, all sourced from regions with monsoon seasons that overlap. This creates a predictable annual vulnerability window from June to September."
• It identifies non-obvious dependencies: "Your tier-1 suppliers appear diverse, but 62% of them source key raw materials from the same tier-2 supplier in Malaysia. This creates a hidden single point of failure in your supply network."
• It quantifies trade-offs: "Increasing safety stock for all components would require $4.2M in additional working capital. However, a targeted approach focusing on the 12 most critical components would mitigate 80% of your historical disruption impact for only $1.1M."
• It recommends a staged implementation: "Begin by dual sourcing these specific components, then implement weather pattern monitoring for your key logistics routes. Your shipping data shows that rerouting through Singapore during typhoon season costs more but reduces delays by 74%."

A manufacturing executive noted that the reasoning LLM identified specific vulnerabilities that hadn't been recognized despite years of supply chain management experience.

Current limitations: What reasoning AI still struggles with

While reasoning LLMs represent a major advance, they're not perfect. Understanding their limitations helps set realistic expectations:

• Time and resource intensity
  Reasoning LLMs typically take longer to respond and use more computational resources than traditional LLMs. Think seconds instead of milliseconds—not a problem for complex analysis but potentially frustrating for quick queries.
• Overconfidence in reasoning
  These models can still make errors in their reasoning chains, and when they do, they can be harder to spot because the step-by-step approach gives an impression of thoroughness.
• Novel problem domains
  While reasoning LLMs excel at applying logical thinking to areas they've been trained on, they can struggle with truly novel domains where there are no established patterns to draw from.
• Data and context limitations
  Even the best reasoning is only as good as the data it's working with. These models can still miss important context that would be obvious to human experts in specialized fields.

When to use which type of AI

Not every situation calls for the deep thinking of a reasoning LLM. Sometimes the quick response of a traditional LLM is exactly what you need.

The quick-decision matrix: Traditional vs. reasoning LLMs

The future is thinking before speaking

The shift from traditional to reasoning LLMs represents a fundamental evolution in artificial intelligence. We're entering an era where AI doesn't just answer what it knows, it figures out what it needs to know to give you the best possible answer.

Think of it this way: traditional LLMs are like having a quick conversation with a knowledgeable friend, while reasoning LLMs are like consulting with a thoughtful expert who carefully analyzes your problem before offering advice.

Yes, a reasoning LLM might take a bit longer and produce more detailed answers, but the outcome is a well-reasoned solution that saves time in the long run by reducing errors and follow-up questions. For businesses, this means better decision support, more efficient customer service, and higher trust in AI-driven processes.

Just like in human relationships, sometimes taking a moment to think things through makes all the difference. The future belongs to AI that thinks before it speaks—and that changes everything about how we can use these systems in our daily lives and businesses.