I've found disabling reasoning entirely but adding a "reason" to the JSON response from the LLM to work significantly faster and consume many fewer tokens for narrowly scoped prompts.

At least for Claude family models.

e.g. {

  "reason": "<Describe why you picked this result>",

  "selection": "<The number of the value you selected>"

I'm sure native reasoning produces more accurate results, but for my use case the quality was about the same, and the model would reason for thousands of tokens in native reasoning vs just 1-200 with response level reasoning.

Again, to be clear, this is for deterministic/pipeline style workflows, not agentic/coding use.