Expert judgment is the dominant strategy used for software development effort estimation. Yet, expert-based judgment can provide over-optimistic effort estimates, leading to projects’ poor budget planning and cost. and time overruns. Large Language Models (LLMs) are good candidates to assist software professionals in effort estimation. However, their effective leveraging for software development effort estimation requires thoroughly investigating their limitations and to what extent they overlap with those of (human) software practitioners. One primary limitation of LLMs is the sensitivity of their responses to prompt changes. Similarly, empirical studies showed that changes in the request format (e.g., rephrasing) could impact (human) software professionals’ effort estimates. In this paper, we replicated a series of experiments, which were initially conducted with (human) software professionals in the literature, to see how LLMs’ effort estimates change due to the transition from the traditional request format (i.e., ”How much effort is required to complete X?”) to the alternative request format (i.e., ”How much can be completed in Y work hours?”). Our experiments involved three different LLMs (GPT-4, Gemini 1.5 Pro, Llama 3.1) and 88 software project specifications (per treatment in each experiment), resulting in 880 prompts, in total that we prepared using 704 user stories from 3 open-source projects (Hyperledger Fabric, Mulesoft Mule, Spring XD). Our findings align with the original experiments conducted with software professionals: The first four experiments showed that LLMs provide lower effort estimates due to transitioning from the traditional to the alternative request format. The findings of the fifth and first experiments detected that LLMs display patterns analogous to anchoring bias, a human cognitive bias defined as the tendency to stick to the anchor (i.e., the ”Y work-hours” in the alternative request format).