低成本下的高性能模型，是悖论还是可能？

Core Viewpoint - The article discusses the paradox of achieving high performance in AI models at low costs, questioning whether the decline in perceived model performance is intentional by AI companies and exploring the implications of cost-saving measures on model quality [2][3]. Group 1: Low-Cost High-Performance Models - The performance and cost dilemma of large language models (LLMs) has been a focal point of public and industry concern, with ongoing discussions about whether top model companies sacrifice precision or service stability to save on inference costs [2][3]. - Following the popularity of ChatGPT, users have expressed dissatisfaction with perceived declines in performance, citing issues such as weakened logic, increased errors, and difficulties in following instructions [2][3]. - The public's concern about companies sacrificing model performance for cost savings is supported by technical and market evidence, particularly highlighted in the controversy surrounding the DeepSeek-R1 model [3][4]. - The true "full version" of DeepSeek-R1 requires significant hardware investment, with initial costs reaching hundreds of thousands of yuan, leading some platforms to potentially use distilled versions that compromise inference capability and stability [3][4]. Group 2: Cost Management Strategies - To balance costs and performance, high-end "full version" models are not widely available, especially in a market flooded with free or low-cost services that often lack sufficient performance [6]. - AI companies are increasingly adopting model distillation or simplified models to reduce inference costs and manage financial investments [6]. - Common strategies to address cost pressures include lowering model precision through techniques such as model quantization, pruning, and knowledge distillation, which have become standard practices in the industry [6].