Use a sequence model when order and context across positions matter—for example, when predicting the next word or letter from earlier elements—or when examples have varying sequence lengths and learned features should be shared across positions. A standard feedforward network does not natively represent these sequence dependencies and is better suited to fixed-size inputs and outputs when cross-position dependencies are not central.