Whether treated surgically or with endovascular techniques, large and giant cerebral aneurysms are particularly difficult to treat. Nevertheless, high porosity stents can be used to accomplish stent-assisted coiling and even standalone stent-based treatments that have been shown to improve the occlusion of such aneurysms. Further, stent assisted coiling can reduce the incidence of complications that sometimes result from embolic coiling (e.g., neck remnants and thromboembolism). However, in treating cerebral aneurysms at bifurcation termini, it remains unclear which configuration of high porosity stents will result in the most advantageous hemodynamic environment. The goal of this study was to compare how three different stent configurations affected fluid dynamics in a large patient-specific aneurysm model. Three common stent configurations were deployed into the model: a half-Y, a full-Y, and a crossbar configuration. Particle image velocimetry was used to examine post-treatment flow patterns and quantify root-mean-squared velocity magnitude () within the aneurysmal sac. While each configuration did reduce within the aneurysm, the full-Y configuration resulted in the greatest reduction across all flow conditions (an average of 56% with respect to the untreated case). The experimental results agreed well with clinical follow up after treatment with the full-Y configuration; there was evidence of thrombosis within the sac from the stents alone before coil embolization was performed. A computational simulation of the full-Y configuration aligned well with the experimental and in vivo findings, indicating potential for clinically useful prediction of post-treatment hemodynamics. This study found that applying different stent configurations resulted in considerably different fluid dynamics in an anatomically accurate aneurysm model and that the full-Y configuration performed best. The study indicates that knowledge of how stent configurations will affect post-treatment hemodynamics could be important in interventional planning and demonstrates the capability for such planning based on novel computational tools.