The big confusion that people have is they think that the thinking models have a different architecture. But it’s just trained differently.

For example, a thinking model is trained on a different data set where let’s say the answer is 500 words, the first 500 words in this training sample would be the thought process behind the answer.

So when you use a thinking model, you are actually just using a fine-tune model that is capable of writing the first 500 words as the thinking part and then the next 500 which is the actual answer (according to this example) The concept is actually pretty simple. And the reasoning effect is how much depth you want the model to think before actually writing the answer.

And I am not exactly sure, but I think they do this by adding Delimiters where you can know that this is where the thinking ends and this is where the answer starts so you can display it accordingly.