Modeling is widely used to predict radionuclide distribution following accidental radionuclide releases. Modeling is crucial in emergency response planning and risk communication, and understanding model uncertainty is important not only in conducting analysis consistent with current regulatory guidance, but also in gaining stakeholder and decision-maker trust in the process and confidence in the results. However, while methods for dealing with parameter uncertainty are fairly well developed, an adequate representation of uncertainties associated with models remains rare. This paper addresses uncertainty about a model's structure (i.e., the relevance of simplifying assumptions and mathematical equations) that is seldom addressed in practical applications of environmental modeling. The use of several alternative models to derive a range of model outputs or risks is probably the only available technique to assess consistency in model prediction. Since each independent model requires significant resources for development and calibration, multiple models are not generally applied to the same problem. This study uses results from one such model intercomparison conducted by the Fruits Working Group, which was created under the International Atomic Energy Agency (IAEA) BIOMASS (BIOsphere Modelling and ASSessment) Program. Model-model intercomparisons presented in this study were conducted by the working group for two different scenarios (acute or continuous deposition), one radionuclide ((137)Cs), and three fruit-bearing crops (strawberries, apples, and blackcurrants). The differences between models were as great as five orders of magnitude for short-term predictions following acute radionuclide deposition. For long-term predictions and for the continuous deposition scenario, the differences between models were about two orders of magnitude. The difference between strawberry, apple, and blackcurrant contamination predicted by one model is far less than the difference in prediction of contamination for a single plant species given by different models. This study illustrates the importance of problem formulation and implementation of an analytic-deliberative process in risk characterization.