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
Plus there's complicated cluster-wide KVCache distribution schemes, built around RDMA networking, etc etc.
By far the most expensive hardware cost now of inference is the memory cost. KVCache is still using it. Though obv GPU compute is using more energy
It's not just like... "stick it in Redis, all done!"
Yes the official DS provider has a low cache cost, for now (it's just distributed KV). But people are still concerned about ZDR, sovereign AI, getting more model choice in their subscription price, etc. They want more bang for their buck. So when a new model comes out, the bigger impact is really its performance across providers, not the price at a single provider.
And in about 5 years (or less), API prices will stop being a thing. We all used to pay per-minute for phone calls, texts, data. Nobody does anymore. Virtually every mobile provider is now a flat prepaid or contract fee. Carriers improved their technology (going from analog to digital), and it turned out consumers will spend more money on a packaged subscription than on pay-per-use. That's why everything in the world is a subscription now.
Your modern $70 big wireless plan isn't giving you $70 of value. Similarly, your $20 AI subscription that's a great deal right now, will be less valuable in 5 years, when hardware & models are more efficient. You'll still be able to buy a local AI rig, but it will still be very expensive, due to AI models continuing to be large, and NVidia keeping new hardware expensive, as these things together reinforce their dominance and profits, and our reliance on them.
This also follows another pre-existing trend. Why do people keep having to buy a new iPhone or laptop every other year? Because each year companies make software & data less efficient/more bloated, so people need a more powerful machine to run it, which enables them to make even more bloated software. Almost everything people do with a computer today is what they did 15-20 years ago; it's just a higher bit-rate. Despite this, people keep re-buying the same machine, so companies can keep extracting more wealth.
AI works the same way. Rather than cost to consumers going down over time, it will likely go up. Not because it can't get cheaper/more efficient - but because it increases companies' profit. This isn't just a theory, btw; the US economy's growth is now almost entirely reliant on this happening.
As shown in the OpenRouter links you provided, there are over 30 distinct providers of these open-weights models, the majority of them supporting ZDR. They can't raise prices because it's a near-perfect marketplace and if they do, someone will just lower them to compete as long as marginal revenue >= marginal cost. There's no moat or other economic factors (e.g. collusion; hard to do with 30 actors) to allow for price control.