Not exactly. It can also make sense if the cache is hot to switch models. For example, we might currently be on Deepseek and the next task is SO HARD that it doesn't make sense to not use frontier (1 turn to crack versus 100 turns for Deepseek to crack). Likewise, the router may judge that it's likely that the next few set of tool-calls will be ridiculously easy, and so switching to Deepseek will save over time.
Remember that Deepseek can be something like 30x cheaper even cold.
There is a more fine-grained view of caching here, because each model can have a different amount of the prefix already cached as well (since we were switching a bunch) so even if the cache is somewhat warm it may make sense to switch.
I can really only see the utility of things like spawning subagents to a lower tier model from another provider, and that's something harnesses can already handle (ie. give model specs for certain delegation roles).
How does tokenless quantify "so hard"?
> 1 turn to crack versus 100 turns for Deepseek to crack
Interesting definition for a "frontier". What is a "turn" here? Token count? Request count? Context-based?
I've found that MiniMax M3 (a smaller model at 295b) will code up better when DeepSeek v4 Pro (1.6t) will not (and vice versa).
A turn here is a tool call/user input, anything that causes the model to get some new input. We're working on adding Minimax M3 and other models. We think that people have some intuitions about which models are good when--we seek to quantify them scientifically.