Diethylene glycol-based aerosol size spectrometers have been extensively employed to measure sub-3 nm size-resolved particle concentrations in recent years. Quantification of the performance of these instruments for accurate measurement of particle size distribution is essential and significant. Comparison between diethylene glycol-based scanning mobility size spectrometer (DEG-SMPS) and scanning mode particle size magnifier (PSM) was performed. Duringthe laboratory calibrations, to obtain more reliable data by the PSM, the particle size bin larger than 4 nm is recommended to be set during calibration to reduce the influence of large particles during the inversion and increase the inverted size range of the PSM results as well. It is shown that the results of PSM and DEG-SMPS are almost consistent when measuring the number concentration of particles below 3 nm. The correlation of the number concentration between the two instruments in this study is high, with r2> 0.75 for each day. However, the slope of the total number concentration of PSM versus that of DEG-SMPS varies from 1.4 to 4.5 in this study, which most likely due to the variations in the chemical composition of newly formed particles. In addition, some non-negligible uncertainties, such as the uncertainty caused by the detection efficiency calibration and the uncertainty in the aerosol charge fraction, may also contribute to the difference in the intercomparison. DEG-SMPS classifies particles with DMA and thus can achieve a superior sizing resolution over PSM. Compared with DEG-SMPS, PSM performs better in environment with low particle concentrations, including the days with weak nucleation, due to the fact that PSM completely avoids the low charging efficiency issue of sub-3 nm particles and is less likely affected by the counting uncertainty of a typical CPC in the SMPS system in“magnification stage”. In general, PSM and DEG-SMPS are both adequate for measuring sub-3 nm particle size distributions to better understand new particle formation processes, even some non-negligible uncertainties in the data need to be solved in future research.