How to Reduce AI Coding Agent Costs

Claude Code, Cursor, and Copilot bills add up. Here's where the money goes and how to cut it.

Where your money actually goes

If you're using Claude Code on the API (not the subscription), your bill breaks down like this:

The expensive part isn't Claude's answers. It's Claude reading your code. Every question triggers full-file reads. A medium project session costs $0.14 on Sonnet or $0.70 on Opus, mostly from input.

5 ways to reduce costs

1. Use a cheaper model for routine tasks

Opus ($15/1M input) is 5x more expensive than Sonnet ($3/1M input). Use Sonnet for most tasks, switch to Opus only for complex reasoning. Haiku ($0.25/1M) works for simple code generation.

2. Reduce input tokens (biggest impact)

This is where Code Context Engine fits in. Instead of Claude reading entire files, CCE indexes your codebase and returns only the relevant functions. 94% fewer input tokens, benchmarked.

uv tool install code-context-engine
cd your-project
cce init

Before CCE: 83,681 tokens per query. After: 4,927 tokens. Same answer quality.

3. Compress output tokens

Tools like output compression reduce Claude's reply length. CCE includes this built in (4 levels: off, lite, standard, max). Saves 30-75% on output tokens. Smaller impact than input savings but still meaningful.

4. Use prompt caching

If you're using the Claude API directly, enable prompt caching. Repeated context (system prompts, file contents) gets cached and costs 90% less on subsequent reads. This is automatic in Claude Code.

5. Be specific in your prompts

Vague prompts ("fix the auth") make Claude read more files to understand what you mean. Specific prompts ("fix the JWT expiry check in auth/tokens.py line 42") read fewer files. Less context = lower cost.

Impact comparison

What this looks like in practice

A developer on Sonnet spending $0.14/session:

That's 5-10 sessions per day without thinking about costs.

uv tool install code-context-engine && cce init