Biomass is key to understanding the structure and function of forest ecosystems. Precisely estimating tree biomass is critical in reducing greenhouse gas emissions and assessing regional climate stability. The allometric model has been commonly applied to quantify the relation between plant biomass and individual size. This power law relationship describes the disproportional increase of biomass as the individual size grows, which contradicts the intensifying inter-/intra-specific competition and resource limits. However, the allometric model often passed model validation in previous studies, partly due to improper data structures characterized by lacking large-sized individuals. By introducing the limiting factor, the logistic model describes the accelerating biomass growth at first but later a slowing growth rate with increasing branch size. However, the logistic model has rarely been applied to estimate biomass. In this work, we collected data from 197 studies conducted in the temperate forest during 1945–2016 based on two published datasets. A total of 26402 samples of leaves, stems, and aboveground biomass were collected from 198 tree species. Our results showed that the logistic model had a larger R2* (0.81), smaller RMSE (0.39 g), and smaller AIC (5809.1) than the allometric model (R2* 0.76, RMSE 0.44 g, and AIC 7189.9). This confirms the better performance of the logistic model relative to the allometric model. The logistic model's better predictive performance was also demonstrated via the linear regression between the estimated biomass and measurements. Furthermore, compared with allometric models, the logistic models were of greater ecological significance by providing the equilibrium biomass, equilibrium growth rate, and points of inflection (POIs) that described the threshold tree sizes starting approaching equilibrium biomass. We further categorized the trees with the bigger-than-POI size as the large-sized samples, which merely take the ≤0.71% share in our dataset. This study addresses that the logistic model outperforms the allometric model in predicting tree biomass of temperate forests. This benefits clearly understanding the size-related strategies of carbon sequestration of plants, precisely predicting the dynamics and spatial distribution of forest carbon storage, and mitigating future climate change.