Reliable observations are the prerequisite for high-precision GNSS data processing. Data quality evaluation at the pre-processing procedure can help the user to identify the weakness of the observations. The open source software package G-Nut/Anubis developed at Geodetic Observatory Pecny (GOP) is introduced and used for the evaluation of long-term BeiDou/GPS signal. Several key indicators are selected to evaluate the BeiDou/GPS observations. Quantitative analysis shows that more than 100 stations can achieve 6-hour standalone BeiDou positioning at least. The completeness of the BeiDou phase and code observations at three frequencies is higher than that of the GPS observations in the selected station which is mainly attributed to the high elevation observations from GEO and IGSO satellites. Qualitative analysis shows that the multipath effects for pseudorange observations are correlated with signal, receiver types and firmware versions besides the station environment. Users attempting to improve the pseudorange observations by multipath filter should pay attention also to the receiver-specific settings. Cycle slip counts are used to indicate the quality of carrier-phase observations. Results show that BeiDou C05 satellite is more affected by the cycle slips, in particular the third frequency observables. For GPS, the third frequency is more robust compared to the legacy frequencies. Comprehensive single point positioning results show that positioning accuracy for BeiDou is worse than that of GPS, which is mainly caused by less active satellites, worse geometry and the larger errors in the broadcast ephemeris. The BeiDou positioning accuracy was further degraded by the C13/C15 satellite and dominated by large clock errors. Broadcast evaluation shows that the BeiDou orbit errors are related to the constellations. Overall, the orbit precision for both GPS and BeiDou has been gradually improved. Outliers were observed for navigation records assigned with the healthy status, especially for BeiDou broadcast orbits. and Zhao Lewen, Dousa Jan, Vaclavovic Pavel, Ye Shirong, Xia Fengyu.