Single-cell mRNA sequencing (scRNA-seq) allows to profile heterogeneous cell populations, offering exciting possibilities to tackle a variety of biological and medical questions. A range of methods has been recently developed, making it necessary to systematically compare their sensitivity, accuracy, precision and cost-efficiency. Here, we have generated and analyzed scRNA-seq data from 479 mouse ES cells and spike-in controls that were prepared with four different methods in two independent replicates each. We compare their sensitivity by the number of detected genes and by the efficiency with which they capture spiked-in mRNAs, their accuracy by correlating spiked-in mRNA concentrations with estimated expression levels, their precision by power simulations and variance decomposition and their efficiency by their costs to reach a given amount of power. While accuracy is similar for all methods, we find that Smart-seq on a microfluidic platform is the most sensitive method, CEL-seq is the most precise method and SCRB-seq and Drop-seq are the most efficient methods. Our analysis provides a solid basis to choose among four available scRNA-seq methods and to benchmark future method development.