The main focus of OmniBench is to evaluate how well could the omni-language models (OLMs) understand and reconstruct the context given information from image, audio and text modalities. Setting up questions with four available options for the models, we use accuracy, i.e., the ratio matched letter of the correct option and model response, as the evaluation metric (n.b., the accuracy of a random guess model is 25% under this setting).