One sentence conclusion: Not all jobs require the most expensive model.
Stratify the tasks according to difficulty, and let different models with different prices - this is a small numberNo need to change the product or data structureOne step to see the effect. But it has a premise, and this premise is often skipped:Downgrade must have acceptance criteria.
Phenomenon
The default form of many systems is "all requests go through the same model", and it is usually the one with the strongest capabilities and the highest unit price.
The reason is understandable: using the best one is the easiest and least error-prone during development. But looking back after a while, you will find that there are a lot of requests mixed inYou don’t need that much ability at all's work. The capability boundaries of
Teardown: Which tasks can be downgraded and which ones should not be touched
Three categories that can be downgraded:
| Type | Why can | Typical scenario |
|---|---|---|
| Format conversion class | Input and output are blocked by rules, there is little room for fault tolerance, and excess capacity | Form to field, fixed template to fill in the blanks |
| Classification and extraction of classes | The judgment dimension is fixed and the answer space is limited. | Classification, marking, and extracting values from a piece of text |
| Short question and answer and repetitive generation | The answer does not require a long chain of reasoning | Standard speaking skills and template copywriting |
Two categories that should not be downgraded:
- Activities that require long chain reasoning - If there are more than one step, the weak model will start to deviate from a certain step in the middle, and the deviation will not be obvious.
- The conclusions will be directly used in the activities delivered externally. - Once you make a mistake, the price is not on the bill, but on the customer.
Conclusion
The correct approach is not to "try a cheaper model", but toAssign an acceptance criterion to each layer：
- Take out the historical calls of this task and form a batchFixed test sample；
- Rerun this batch of samples using a cheaper model;
- Use predetermined standards to judge right and wrong, and get a pass rate;
- The pass rate meets the standard → this layer adopts the cheap model for a long time; if it does not meet the standard → remain the same,Don’t relax standards to save costs。
Set the standard first, then change the model - If the order is reversed, it is equivalent to transferring the risk from the bill to the customer's site.
Boundary (under what circumstances does this article not hold)
- If the capability requirements of the same batch of requests are inherently consistent (for example, all are long chain analyses), then layering is meaningless and this article does not apply.
- How much you save by downgrading depends on the proportion of each layer in your request.Please refer to your revised bill.; This article does not give any estimate of the province’s size.
- This article does not compare or name any manufacturers: which model is suitable for which layer should be determined by your own test results, not by other people's recommendations.
How can you verify it yourself?
No need to connect to the system or change the code: select a batch of real calls from the usage details you exported, rerun the same input with another model, and manually compare the pass rate.
The pass rate is based on comparison, not what I said.
Source and Description
- The layered method is a common practice and does not involve any manufacturer’s evaluation conclusion.
- Source: Tunenomics Tunenomics unified external measurement basis (compare and review date: 2026-09-18).
- This article does not contain original customer data, actual measured figures and savings commitments.
This article is from Tunenomics’ “AI Cost Observation” series. The description of methods and the measurement basis of conclusions in the article can be checked; the numbers are marked with the source, measurement basis and date. Sample data does not represent actual results for any customer.