Model Cascading
An architectural pattern in which models of differing capability and cost are combined within a single workflow, so that high-capability models handle high-value decisions while low-cost models perform bulk execution.
Timeline (last 90 days)
The model cascade (Cascade) approach is to first use a cheap model to handle all requests and output a confidence score or structured marker, and forward only difficult cases below the threshold to a stronger model, typically with about 80% of simple requests resolved by the cheap model
In the cost rehearsal step, the messy fixture is run once before launch to check two numbers: the escalation rate (more than one third indicates a problem with the prompt, and the prompt should be fixed rather than loosening the budget) and the per-meeting cost ceiling (total token count × worst-case price, which must fall within the escalation budget, otherwise the workflow is not allowed to go live).
Model Cascade is a routing strategy that trades off cost and quality: first use a cheap small model (such as the mini tier) to process all input, and only escalate that item to an expensive flagship model when it outputs low confidence, fails validation, or triggers specific conditions; the escalation rate directly determines total cost.
Using a model cascade pattern, escalating messages where Jev's confidence is below 0.7 to GPT-5.5 raised accuracy to 83.7%, with cost increasing from $0.07 to approximately $2.58, which is lower than the 84.9% accuracy and $4.65 cost of using GPT-5.5 throughout
All Facts (4)
The model cascade (Cascade) approach is to first use a cheap model to handle all requests and output a confidence score or structured marker, and forward only difficult cases below the threshold to a stronger model, typically with about 80% of simple requests resolved by the cheap model
50%UnverifiedIn the cost rehearsal step, the messy fixture is run once before launch to check two numbers: the escalation rate (more than one third indicates a problem with the prompt, and the prompt should be fixed rather than loosening the budget) and the per-meeting cost ceiling (total token count × worst-case price, which must fall within the escalation budget, otherwise the workflow is not allowed to go live).
50%UnverifiedModel Cascade is a routing strategy that trades off cost and quality: first use a cheap small model (such as the mini tier) to process all input, and only escalate that item to an expensive flagship model when it outputs low confidence, fails validation, or triggers specific conditions; the escalation rate directly determines total cost.
50%UnverifiedUsing a model cascade pattern, escalating messages where Jev's confidence is below 0.7 to GPT-5.5 raised accuracy to 83.7%, with cost increasing from $0.07 to approximately $2.58, which is lower than the 84.9% accuracy and $4.65 cost of using GPT-5.5 throughout
50%