Large language models (LLMs) often perform better when prompted to explain their reasoning, but it remains unclear how well such gains persist as reasoning depth increases. In this work, we propose a depth-aware evaluation framework alongside the performance results on two structured datasets: CLUTRR (kinship reasoning) and ProofWriter (logical entailment), comparing direct vs. reasoning (reasoning depth = number of inference steps required) prompts across five models. Reasoning gave small gains at shallow depths but quickly weakened and often reversed as tasks grew more complex. In ProofWriter, GPT-5 reached 90% accuracy at depth four in direct model, yet its reasoning accuracy fell below baseline after depth two. Smaller open-source models showed only unstable or negligible gains, underscoring that reasoning in LLMs remains brittle with increased depth.