Tokens: ↑ input 3.57M • cache hit 0.00% • ↓ output 21.68K • $ 0.00

I have no clue what happened in my self hosted setup but I am now able to have very long running conversations and finally got to witness the context compression in action. Using a full sized qwen3 coder 480B model. I'm able to make really good documentation for new codebases. I don't think the model has actually processed 3.57 million tokens in this chat, but regardless, a week ago things would break after just a few tool calls. Would love to get an accurate metric of the context length in my requests, but regardless, I'm very VERY happy right now.

Awesome, if you want to share the prompts that you’ve used to be successful I’m sure the people in the community would appreciate it!

I've made no modification to the default tool-calling/starter prompts haha.