been tracking how much our cursor bills are spiking lately since every new model release feels like another massive expense. between gpt 5.6 and k3, the sheer volume of tokens flying into our ide is getting ridiculous for any real business use. i stumbled onto a way to use
code mode to handle these requests more efficiently. it basically optimizes how the mcp context is sent sooo you arent wasting money on redundant data.
>the goal is reducing overhead without losing logic accuracyits pretty much the only way to avoid
going broke staying within budget while still using high-end models. i am curious if anyone else has tried
switching entirely to local models for simpler tasks to save even more. it seems like a total game changer for anyone running heavy automation pipelines. does anyone have a specific config setup that works best for this?
https://dev.to/anthonymax/how-to-cut-mcp-token-costs-save-up-to-92-at-scale-with-code-mode-3fco