When deploying LLMs in government, you can't optimize for everything—better factuality and honesty require accepting higher costs and energy use, and bias reduction requires separate attention regardless of model quality.
This paper presents a framework for evaluating large language models specifically for Dutch government use, identifying six key dimensions (factuality, honesty, bias, energy use, cost, and data transparency) and testing 30+ models. The research reveals important trade-offs: better accuracy comes with higher environmental and financial costs, while bias operates independently of both.