Guochang contributed to the BerriAI/litellm repository by enhancing the Sonnet v1 cache cost model, focusing on improving pricing predictability and scalability for deployments. He implemented base cache costs and refined the logic for token-based cost calculations in caching scenarios, enabling more accurate resource planning. His work included adding new keys for cache creation costs and updating configuration files to support higher input token limits, which allows the system to handle larger workloads efficiently. Utilizing Python and JSON for backend development and data processing, Guochang delivered a targeted feature with clear technical depth, addressing both efficiency and visibility in cost management.