ContextCompressor.update_model() recomputed context_length/threshold/budgets
but kept the cross-call calibration state (last_real_prompt_tokens,
last_rough_tokens_when_real_prompt_fit, last_compression_rough_tokens,
awaiting_real_usage_after_compression, _ineffective_compression_count) from the
PREVIOUS model.
Those fields encode 'the provider proved this prompt fit' / 'preflight can be
deferred' decisions valid only for the model that produced them. Carried across
a switch to a smaller-context model, should_defer_preflight_to_real_usage() used
the old model's 'it fit' history to SKIP a preflight compression the new model
actually needed — sending an oversized prompt the provider rejects (#23767).
update_model() now clears that state; the new model's first response repopulates
it via update_from_response(). Verified E2E: after a 200K->65,536 switch, defer
no longer suppresses and should_compress fires on an over-threshold estimate.
kshitijk4poor
1e0b3a2bcc