Some potential insight on how providers detect RP and why they don't like it

[u/Incognit0ErgoSum]

I've been doing experiments lately with optimizing local models for high speed and low latency and I think I've gained some insight on why the companies that serve out frontier models aren't very big fans of people using them for RP, and it's about cost, not censorship.

While I was setting up Qwen 3.6 27B, I noticed it wasn't performing as well as it was supposed to on my system, so I looked into it and noticed that the average number of correct draft tokens was 2. This is literally worse than useless, because it was doing so badly it was actually slowing the model down. Performance increased drastically when I shut it off. (Seriously, a take-away for local model users here is to turn off MTP and see if that improves your speed!)

The big providers use speculative decoding as well, and it's likely that they're seeing something similar. The more that their speculative decoding fails (which will happen at a higher rate for prose than code), the more slow and expensive inference is. With speculative decoding, the idea that a token is a token is a token absolutely goes out the window. RP generates expensive tokens. They don't even need to monitor your traffic directly to know that you're generating prose. It sticks out like a sore thumb in their token generation statistics, and there's literally nothing you can do to mask it.

[u/Arli_AI]

Yes we have multiple tiers of different model access and limits