What exactly is a reasoning model? Let's try to understand. In a simple way. Here is how a normal LLM answers.
You ask, and it starts writing immediately. First word, right away. No pause, no scratch work. One single pass.
That works for easy questions. But try a tricky logic puzzle, or a multi step math problem. Guessing the answer in one shot? That usually goes wrong.
Think about yourself. What is seventeen times twenty four? You don't just blurt it out. You grab scratch paper, work in steps, and check as you go.
A reasoning model does exactly that. Before giving you the answer, it works the problem through, step by step. Deliberate first, answer second. That's the whole definition.
So the output has two phases. First a working phase: the model writes out a chain of thought, its intermediate steps. Then a short final answer built on that work. And here is the powerful part.
In that working phase, the model can catch its own mistakes. Wait, that's wrong. Backtrack. Try a different path.
Is it a new kind of brain? No. Underneath, it's the same transformer, predicting one token at a time. What changed is the behavior: it deliberates before it commits.
You've probably met these models. OpenAI's o series, DeepSeek R1, Claude in extended thinking mode. They all pause, work it through, then speak. So, the gist.
A standard LLM answers in one pass. A reasoning model earns its answer: it works step by step, checks itself, and only then replies. Quick check now. One question is coming up.
Let's see if it clicked.
What exactly are thinking tokens? Let's try to understand. In a simple way. Here is the thing.
A language model makes one token at a time, and every token gets the same fixed slice of computation. So an instant answer gets exactly one shot. Now try this. Multiply thirty seven by twenty four in your head.
Hard, right? On paper, easy. Reasoning models pull the same trick. They write out steps first.
Those written steps are thinking tokens. Ordinary tokens, generated the usual way. But they are scratch work, not the answer. The literature calls this chain of thought.
First this, so that, therefore this. Each step lands on the page, and the next one builds on it. Why does writing help? Every token the model writes gets fed back in as input.
Intermediate results are stored in the text itself. The text becomes working memory. And a second effect. Each new token triggers a full pass through the network.
Ten steps of thinking means ten more chances to compute. In products you often never see this. The thinking streams into a hidden block and gets stripped from the reply. That pause when a model says thinking?
It's writing tokens you never read. One catch. The model trusts its own thinking like any other text. A wrong step gets built on, not questioned.
So it helps, but it is not a truth guarantee. So, thinking tokens are ordinary tokens used as scratch work. Write the steps, feed them back, then answer. Reasoning out loud, literally.
Quick check now. One question is coming up. Let's see if it clicked.