Escalating to a bigger model recovers under half the gap

Escalating to a stronger model recovers less than half the quality gap in stuck coding agents, per a new study on mid-task model handoffs. The tests ran across cheap-to-expensive pairs in both the Claude and GPT families. Escalating cost a real premium in tokens and compute that the paper calls the handoff tax.

Downshifting reverses the math. Moving from an expensive model to a cheap one, after the hard reasoning is done, beat escalating on every measure the paper tracked.

The right handoff payload flips with direction too. Escalation works better when the stronger model gets less of the weak model's trajectory instead of more of it. Downshifting works the other way: stripping the strong model's own trajectory before it reaches the cheap model hurts, so context should stay intact.

Anyone running a cheap-then-expensive cascade should test the payload for each direction, trimmed when escalating, full when downshifting, rather than assume more history helps.