The objective of this study was to assess the variability surrounding predicted weaning weight (WW) of nursing beef calves at 210 d of age using 2 models developed to predict milk yield (MY) and calf forage DMI. These models were developed to predict calf WW based on peak milk (PKMK), calf BW, and forage DE. Equations to predict calf forage DMI were published by Tedeschi et al. (2006; Nutrient Digestion and Utilization in Farm Animals; TED06) and Tedeschi et al. (2009; J. Anim. Sci. 87:3380; TED09). Additionally, we evaluated 2 equations to predict MY: Wood (1967; WOD) and the NRC (2000; NRC). Calf ADG was computed using NRC (2000) equations for energy requirements assuming ME content of 5.29 Mcal/kg for milk. A Monte Carlo simulation with 5,000 iterations assumed normal distribution with mean and SD of 3 ± 0.5 Mcal/kg for forage DE, 35 ± 2 kg for calf birth weight (CBW), and 550 ± 50 kg for final shrunk BW (FSBW), and uniform distribution with minimum and maximum at 3 and 12 kg/d for PKMK. Although predicted WW overlapped for all model combinations, their mean and SD varied considerably: 147 ± 72.9, 219 ± 95.1, 262 ± 91.6, and 278 ± 82.2 kg for NRC&TED09, WOD&TED09, NRC&TED06, and WOD&TED06, respectively. Their predicted WW tended to follow normal distributions, except for NRC&TED09 that was skewed to the right. The percentage of predicted WW within 100 and 300 kg were 75.3, 76, 67, and 62.8%, respectively, and within 200 and 300 kg were 10.1, 36.1, 42.4, and 46.4%, respectively. Forage DE had the greatest Spearman correlation (0.74 < r <0.86) with WW, followed by PKMK (38 < r <44), FSBW (0.16 < r <0.18), and CBW (r = 0.01). Forage DE also had the greatest standardized regression coefficient (SRC) for WW: 073, 0.84, 0.81, and 0.80 for NRC&TED09, WOD&TED09, NRC&TED06, and WOD&TED06, respectively, and PKMK had the second greatest SRC: 0.28, 0.39, and 0.43 for WOD&TED09, NRC&TED06, and WOD&TED06, respectively. For NRC&TED09, FSBW had the second greatest SRC (0.18). We concluded that forage DE is the most influential factor that affects calf WW and these predictive models have distinct prediction patterns for calf WW. Future analysis should focus on consolidating predicted MY between these models.