In northwestern Mexico (Gulf of California and Pacific Coast), a fishery on the squalid callista Megapitaria squalida (Sowerby, 1835) was established four decades ago, but little is known about its growth. The objective of this study was to assess individual growth parameters by testing the best growth model fitted to M. squalida. Four growth models were tested, von Bertalanffy, Logistic, Gompertz, and Schnute. The best model was selected based on the Akaike information criterion (AIC) and Bayesian information criterion (BIC). The maximum log-likelihood algorithm was used to parametrize the models considering a multiplicative error structure. According to AIC and BIC, the Logistic model was hierarchically ordered in first place, but the Gompertz, von Bertalanffy, and Schnute models received strong support from the data with AIC and BIC. As no clear “best model” was found, the multimodel inference approach (MMI) was applied to estimate the asymptotic length (L_∞). The averaged L_∞ was 95.1 mm shell length (SL) with a 95% confidence interval (CI) of 86.2–104.1 mm SL using AIC and 95.7 mm (95% CI: 86.3–105.1 mm) using BIC. The conclusion was that MMI was the best technique for estimating L_∞ of M. squalida in the eastern Gulf of California, merely using a single model out of the four tested models.