Concept Beginner to Intermediate · 3 min read

What are reasoning models

Quick answer

Reasoning models are specialized large language models (LLMs) or AI systems designed to perform logical inference and step-by-step problem solving beyond simple text generation. They improve accuracy on tasks requiring multi-step reasoning, such as math, code debugging, or complex question answering.

Reasoning models are AI systems that enhance language models with the ability to perform logical, stepwise inference to solve complex problems.

How it works

Reasoning models work by breaking down complex problems into smaller logical steps, similar to how a human solves puzzles by thinking through each part sequentially. Instead of generating answers in one shot, they generate intermediate reasoning chains or use specialized training to improve logical consistency. This can be compared to a detective gathering clues and making deductions before concluding.

Concrete example

Here is a Python example using the OpenAI SDK with a reasoning-capable model like gpt-4o to solve a math word problem step-by-step:

python

from openai import OpenAI
import os

client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

messages = [
    {"role": "system", "content": "You are a reasoning assistant that explains each step."},
    {"role": "user", "content": "If a train travels 60 miles in 1.5 hours, what is its average speed? Show your work."}
]

response = client.chat.completions.create(
    model="gpt-4o",
    messages=messages
)

print(response.choices[0].message.content)

output

The average speed is calculated by dividing the distance by the time.
Step 1: Distance = 60 miles
Step 2: Time = 1.5 hours
Step 3: Average speed = Distance / Time = 60 / 1.5 = 40 miles per hour.

When to use it

Use reasoning models when your AI tasks require multi-step logical thinking, such as math problem solving, code debugging, legal reasoning, or scientific analysis. Avoid them for simple text generation or tasks that do not benefit from explicit stepwise inference, as reasoning models may be slower or more costly.

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Key Takeaways

Reasoning models improve AI accuracy by generating intermediate logical steps.
They excel at tasks requiring multi-step problem solving like math and code.
Use reasoning models when explicit inference chains improve task reliability.

Verified 2026-04 · gpt-4o

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