Variable　Refrigerant　Flow　(VRF)　systems　are　gradually　gaining　popularity　in　small　and　medium-sized　com-mercial　and　residential　buildings　owing　to　their　high　part-load　performance,　flexible　control,　and　ease　of　installation　and　maintenance.　Developing　models　of　VRF　systems　to　predict　their　performance　are　important　for　model-based　control,　fault　diagnostic　and　detection.　There　are　VRF　models　published　in　existing　literatures,　however　those　models　were　developed　and　validated　in　different　datasets.　As　a　result,　the　model　accuracy　cannot　be　directly　compared.　To　fill　this　gap,　this　paper　presents　a　comprehensive　review　of　the　existing　VRF　models,　and　summarizes　the　input/output　parameters　and　mathematical　formulas　of　16　VRF　models　from　literature　(referred　to　as　physics-based　model).　Next,　we　validate　and　compare　the　model　accuracy　of　existing　models　using　the　same　dataset.　Additionally,　we　develop　data-driven　models　using　the　state-of-art　machine　learning　algo-rithms,　and　compare　the　model　accuracy　between　existing　physics-based　models　with　data-driven　models.　We　find　the　model　proposed　by　Hu　et　al.　in　2019,　which　regresses　the　VRF　cooling　capacity　and　COP　as　a　linear　combination　of　indoor　and　outdoor　temperatures　times　a　cubed　polynomial　function　of　compressor　frequency,　is　the　most　accurate　physics-based　model,　with　a　prediction　error　of　22.19%　in　the　training　dataset　and　22.44%　in　the　validation　dataset.　XGBoost　is　the　most　accurate　data-driven　model,　with　a　prediction　error　of　19.29%　in　the　training　dataset　and　22.02%　in　the　validation　dataset.　The　data-driven　model　is　more　accurate　while　the　physics　-based　model　is　more　generalizable.　The　findings　of　this　study　can　help　researchers　to　select　the　proper　VRF　model　for　building　energy　prediction,　model-based　optimization,　and　fault　diagnostic　and　detection.