Based on the measurement of 3643 branch biomass samples of 60 Korean pine (Pinus koraiensis) trees from Mengjiagang Forest Farm, Heilongjiang Province, all subset regressions techniques were used to develop the branch biomass model (branch, foliage, and total biomass models). The optimal base model of branch biomass was developed as lnw = k1 + k2 lnL(b) + k3 lnD(b). Then, linear mixed models were developed based on PROC MIXED of SAS 9.3 software, and evaluated with AIC, BIC, Log Likelihood and Likelihood ratio tests. The results showed that the foliage and total biomass models with parameters k1, k2 and k3 as mixed effects showed the best performance. The branch biomass model with parameters k5 and k2 as mixed effects showed the best performance. Finally, we evaluated the optimal base model and the mixed model of branch biomass. Model validation confirmed that the mixed model was better than the optimal base model. The mixed model with random parameters could not only provide more accurate and precise prediction, but also showed the individual difference based on variance-covariance structure.