Stable isotope mixing models are increasingly used to quantify consumer diets but may be misused or misinterpreted. The paper outlines best practices for their effective application. Mixing models now estimate probability distributions of source contributions, have user-friendly interfaces, and incorporate complexities like variability in isotope signatures, discrimination factors, hierarchical variance structure, covariates, and concentration dependence. Proper implementation requires clear study questions, informed by system knowledge, and strong sampling designs to characterize isotope variability on appropriate spatio-temporal scales. Models should be appropriate for the question, with awareness of their assumptions and limitations. Interpretation of model outputs must be cautious, as they estimate proportions of assimilated resources with substantial uncertainty. Graphing data before analysis is essential. The paper also discusses the importance of prior knowledge in identifying questions and spatial/temporal scales, considering what is known about the animal's diet, and using appropriate diet-tissue discrimination factors. It emphasizes the need for a well-planned sampling design, including the choice of tissues that reflect the diet period. All sources should be included in the model to avoid bias. Sources may be grouped if they are not significantly different in isotopic composition. Concentration dependence and isotopic routing should be considered, especially when food sources have large disparities in elemental concentrations. Uncertainties should be incorporated into the models, with Bayesian approaches allowing for flexible model specification and uncertainty characterization. The paper concludes that these best practices will help maximize the usefulness of stable isotope mixing models in food-web studies while being mindful of their limitations and assumptions.Stable isotope mixing models are increasingly used to quantify consumer diets but may be misused or misinterpreted. The paper outlines best practices for their effective application. Mixing models now estimate probability distributions of source contributions, have user-friendly interfaces, and incorporate complexities like variability in isotope signatures, discrimination factors, hierarchical variance structure, covariates, and concentration dependence. Proper implementation requires clear study questions, informed by system knowledge, and strong sampling designs to characterize isotope variability on appropriate spatio-temporal scales. Models should be appropriate for the question, with awareness of their assumptions and limitations. Interpretation of model outputs must be cautious, as they estimate proportions of assimilated resources with substantial uncertainty. Graphing data before analysis is essential. The paper also discusses the importance of prior knowledge in identifying questions and spatial/temporal scales, considering what is known about the animal's diet, and using appropriate diet-tissue discrimination factors. It emphasizes the need for a well-planned sampling design, including the choice of tissues that reflect the diet period. All sources should be included in the model to avoid bias. Sources may be grouped if they are not significantly different in isotopic composition. Concentration dependence and isotopic routing should be considered, especially when food sources have large disparities in elemental concentrations. Uncertainties should be incorporated into the models, with Bayesian approaches allowing for flexible model specification and uncertainty characterization. The paper concludes that these best practices will help maximize the usefulness of stable isotope mixing models in food-web studies while being mindful of their limitations and assumptions.