Multi-case-base reasoning (MCBR) extends CBR to draw on multiple case-bases that may address somewhat different tasks. MCBR is often advocated to overcome problems such as insufficient local storage to accommodate the entire case-base, or to enable use of distributed case sources. However, this raises an important question: When it is possible to merge different case-bases, is MCBR needed? This paper answers that question with an experimental assessment of how MCBR affects solution quality, demonstrating that MCBR can improve accuracy compared to merging, even if the cross-case-base adaptation method used by MCBR is also applied to the external cases before merging.