If anything I am confused by how high cache costs still are across many models

The really expensive part of inference - the compute and memory traffic required to perform prompt processing and decode - is paid for roughly once per token in a multi-turn session when prompt caching is working properly.

The difference to the provider's costs between:

- Submitting 100 back-to-back requests, each adding 1k tokens to the context

- Submitting 1 100k token context request

Is negligible. Yet the consumer is going to pay a lot more in the former case for all those repeatedly submitted input tokens that you get charged the cached rate for (but which required no additional compute on the provider side).

DeepSeek V4.1 has an insanely optimized KV-cache that takes around 900MB per million tokens, making it feasible to move to disk between requests. Makes sense they're cheap

The amazing part of DeepSeek's caching is that the TTL is (although not guaranteed) like 8-24 hours. Makes a big difference to how much time I can spend reviewing code, when most everyone else offers maybe 1 hour (for subscribers - often minutes for API users).