cascade

A cascade works a lot like the tiered support system at a call center. When a call comes in, a junior rep picks it up first and handles most questions on the spot. Only when the junior rep can't answer, or seems to have gotten it wrong, does the call get escalated to a senior rep. If every call went straight to a veteran from the start, accuracy would be high, but labor costs would balloon.

The same trade-off shows up with AI models. Bigger, more expensive models generally perform better, but they cost more every time you use them. So the workflow is arranged so that a small, cheap model tries the problem first, the result is automatically checked, and only if it's wrong does the task get handed off to the larger, pricier model. This entire sequence is called a cascade.

The point is to cut costs without sacrificing much accuracy. Since the cheap model handles most cases and only the genuinely hard ones get escalated to the expensive model, the average cost across all requests goes down.

The article described Together AI's "DeepSeek-first cascade" approach, where DeepSeek V4 Flash generates an answer first, the result gets verified, and only when necessary does the task get passed to GPT-5.6 Luna. A common misunderstanding is that this isn't a new model merging the two together — it's simply an operational setup that runs two separate models in sequence.