As the demand for mental health services outpaces clinical workforce capacity, users are increasingly turning to large language models (LLMs) for accessible and private support. Although recent evaluations suggest LLM care can reduce clinical symptoms, how models actually conduct therapeutic interactions compared to trained human clinicians remains largely unknown. Drawing on established clinical psychology frameworks that conceptualize therapy as a sequential decision-making process, we can systematically analyze the conversational interventions used in AI-driven sessions. To achieve this, we introduce an original, clinically validated ontology of ten distinct therapeutic moves to descriptively categorize strategies LLMs favor or neglect. Applying this ontology at scale reveals that models drastically over-use probing, disregard common CBT-favored techniques and are strongly context-anchored. Moreover, we propose exposing these categories as callable tools, bringing models significantly closer to the human clinical baseline.

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