Chain-of-thought (CoT) monitoring is a promising tool for detecting misbehaviors

and understanding the motivations of modern reasoning models. However, if

models can control what they verbalize in their CoT, it could undermine CoT

monitorability. To measure this undesirable capability — CoT controllability —

we introduce the CoT-Control evaluation suite, which includes tasks that require

models to solve problems while adhering to CoT instructions, e.g., reasoning about

a genetics question without using the word “chromosome”. We show that reasoning

models possess significantly lower CoT controllability than output controllability;

for instance, Claude Sonnet 4.5 can control its CoT only 2.7% of the time but 61.9%

when controlling its final output. We also find that CoT controllability is higher

for larger models and decreases with more RL training, test-time compute, and

increased problem difficulty. CoT controllability failures extend even to situations

in which models are given incentives (as opposed to direct requests) to evade CoT

monitors, although models exhibit slightly higher controllability when they are

told they are being monitored. Similarly, eliciting controllability by adversarially

optimizing prompts does not meaningfully increase controllability. Our results

leave us cautiously optimistic that CoT controllability is currently unlikely to

be a failure mode of CoT monitorability. However, the mechanism behind low

controllability is not well understood. Given its importance for maintaining CoT

monitorability, we recommend that frontier labs track CoT controllability in future

models.