Hold Off on Switching to a Cheaper Model: Calculating Effective Costs Considering Cache

In the tech community, the KV-cache hit rate is often highlighted as a key factor in cost optimization. Various sources, including discussions on context engineering and AI agent optimization, emphasize its importance. However, a question arises regarding how this is quantified by MaaS providers. While I am familiar with cash token discounts, I had not delved deeply into the economics of cache usage for platforms like OpenAI, Anthropic, Gemini, and DeepSeek. Upon analysis, I discovered a striking finding: two requests made to the same model with identical token volumes can yield price differences of up to three times, solely based on whether the tokens are cached. This article focuses on the financial aspects of cache and tokens, explaining how to accurately assess real costs. It also highlights why standard price lists may not provide a complete picture of expenses.