Cost7 min read
How to reduce Cursor and AI coding assistant costs
Agentic coding assistants are, per developer, the most token-hungry workload most companies run. A single "fix this failing test" can become dozens of model calls, each re-sending large file context. Multiply by a team and the bill gets attention quickly.
Why coding tools burn tokens
- Context is enormous. File contents, project structure, diffs, and terminal output — resent on nearly every step.
- Agent loops multiply calls. Read, plan, edit, run, observe, repeat. One task, many round trips.
- Everything defaults to the frontier model. Including the steps that do not need it.
Four levers
- Bring your own key and route it. Most tools — Cursor, Cline, Aider, Continue, Claude Code — accept a custom base URL and key. Point them at a gateway and you regain control over which model serves which request. See the coding tools setup guide.
- Route by step type. Planning and architecture genuinely benefit from a frontier model. File reads, lint fixes, renames, and test scaffolding do not. Split them and the mix shifts sharply.
- Exploit prompt caching. Coding sessions re-send near-identical prefixes constantly, which is close to the ideal case for prompt caching. Ensure your gateway preserves cache-friendly prefix ordering rather than reshuffling it.
- Constrain context. Narrower file scoping and tighter ignore rules reduce tokens per step more reliably than any model swap.
Measure cost per completed task, not per request. A cheaper model that needs three extra agent loops to land the same diff is not cheaper. Neural Router's Agent service type optimizes trajectory economics rather than single-call price.
Related: running Claude Code through a proxy and the best LLM for coding.