Imagine two different text generation models. Model A processes an initial prompt by allowing every word in the prompt to be contextually informed by every other word in that same prompt simultaneously. Model B processes the same prompt sequentially, where its understanding of any given word is only informed by the words that preceded it. For a task requiring a deep understanding of a complex prompt with long-range dependencies, which model's approach to processing the prompt is likely to be more effective? Justify your reasoning based on the described information flow.