Quantitative proteomics in large cohorts is highly valuable for biomedical/pharmaceutical investigations but often suffers from suboptimal reliability, accuracy, and reproducibility. Here we describe a new ultra-high-resolution (UHR) IonStar method achieving precise/reproducible protein measurement in large-cohorts while eliminating accuracy-related problems such as ratio compression, by taking advantage of the exceptional selectivity of UHR-MS1 detection (240k_FWHM). Using mixed-proteome sets reflecting large-cohort analysis using technical or biological replicates (N=56), we comprehensively compared the quantitative performances of UHR-IonStar with a state-of-the-art SWATH-MS method. The SWATH-MS employs a cutting-edge TripleTOF and a SpectronautTM package and was meticulously optimized to maximize sensitivity, reproducibility and proteome-coverage, by developing a highly extensive, customized spectral-library, reproducible/robust capillary-LC separation and narrow, variable-window acquisition. Comparing quantitative performances of the two distinct methods (i.e. MS1-vs.MS2-based) affords interesting and valuable observations. While the performances for higher-abundance proteins (i.e. higher 75% proteins in abundance) are quite similar by the two methods, UHR-IonStar showed markedly better quantitative accuracy, precision and reproducibility (e.g. R2>0.9 by UHR-IonStar vs. R2<0.4 by SWATH-MS) for proteins of lower 25% in abundance, and much improved sensitivity/selectivity for discovering significantly-altered proteins, especially these with changes ≤2.5 folds. Furthermore, UHR-IonStar showed more accurate protein quantification in single analysis of each individual sample in a large set, which is an inadequately-investigated albeit highly critical parameter for large-cohort analysis. Finally, we compared the two methods in measuring time courses of altered proteins in cancer cells (N=36) treated by paclitaxel, where dysregulated biological processes have been well-established. UHR-IonStar discovered more altered-proteins in biological processes and pathways that are known to be induced by paclitaxel, with substantially better significance scores. Additionally, UHR-IonStar showed markedly superior ability in accurately depicting the time courses of tubulin-α/β isoforms which are well-known to be consistently induced by paclitaxel. In summary, UHR-IonStar represents a reliable, robust and cost-effective solution for large-cohort proteomics quantification with excellent accuracy and precision.