In this episode of My Weird Prompts, brothers Herman and Corn Poppleberry dive into a provocative thought experiment: if cloud inference costs were identical, would there ever be a reason to choose a small model over a trillion-parameter giant? Moving beyond the "bigger is better" hype of previous years, the duo explores the physical realities of latency, the hidden costs of model verbosity, and the rise of high-density models in 2025. Whether you are a developer looking for better throughput or a business leader seeking reliable specialization, this discussion reveals why the most powerful tool isn't always the largest one.
