Yep totally -- think of this as "maximum effort". If a task doesn't need a lot of thinking tokens, then the model will choose a lower effort level for the task.
replyTechnically speaking, models inherently do this - CoT is just output tokens that aren't included in the final response because they're enclosed in <think> tags, and it's the model that decides when to close the tag. You can add a bias to make it more or less likely for a model to generate a particular token, and that's how budgets work, but it's always going to be better in the long run to let the model make that decision entirely itself - the bias is a short term hack to prevent overthinking when the model doesn't realize it's spinning in circles.
reply> You can add a bias to make it more or less likely for a model to generate a particular token, and that's how budgets work
replyDo you have a source for this? I am interested in learning more about how this works.
It's how temperature/top_p/top_k work. Anthropic also just put out a paper where they were doing a much more advanced version of this, mapping out functional states within the modern and steering with that.
replyHuh, I wonder if that's why you cannot change the temperature when thinking is enabled. Do you have a link for the paper?
replyhttps://transformer-circuits.pub/2026/emotions/index.html
replyAt the actual inference level temperature can be applied at any time - generation is token by token - but that doesn't mean the API necessarily exposes it.
Thanks. I was referring to the fact that Anthropic, in their API, prohibits setting temperature when thinking is enabled.
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