What is the fastest way to cut an LLM bill?

Find the call type that costs the most, then check whether a cheaper model gives the same answers on it. Classification and extraction calls are usually the best candidates, because a small model can match a large one on narrow, repeated tasks.

Will a cheaper model lower quality?

On some calls, yes. That is why routing keeps the cheap answer only when the cheap model is confident, and sends the rest to your current model. Measure agreement on your own data before switching.

Guide

Reduce LLM costs without changing your answers

Most LLM bills are dominated by a few call types that run thousands of times a day. Fix those first, in this order.

1. Measure where the money goes

Group calls by purpose (ticket triage, lead scoring, invoice extraction…) and multiply volume by tokens by price. Reasoning tokens count as output tokens on OpenAI, Anthropic and Google, and they are often the largest line. At list prices checked 2026-11-03, GPT-6.1 Sol costs $2 per million input and $10 per million output tokens: 200,000 calls a month at 500 input and 120 output tokens is about $440 a month.

2. Trim tokens

Shorter system prompts, fewer few-shot examples, and structured output instead of prose answers. A lower reasoning effort setting on simple tasks can remove much of the output bill (estimates by model).

3. Cache and batch

Use the provider’s cached-input pricing for long, repeated prompts, and the batch API for work that can wait hours. Both are discounts on the same model, so answers don’t change.

4. Route easy calls to a cheaper model

For narrow tasks (yes/no decisions, labels, field extraction) a small model often gives the same answer as a large one. Run the small model first; keep its answer when its confidence is above a threshold, and send the rest to your current model. You pay for the small model on every call and for the large one only on the hard share.

Example: Levanto Sage 2 costs $0.05 per million input tokens and returns a calibrated probability with each yes/no answer, which is what makes the threshold possible.

More: choosing the threshold, a step-by-step switch and cost estimates by model.

5. Verify before you switch

The saving is only real if the answers your product depends on stay the same. Take 25 real inputs your current model already answered, run the cheaper model on them, and count agreement at each confidence threshold. That is exactly what the free cutover audit does. Try the numbers first in the routing calculator.