Context Cost Optimization

One of the most significant advancements in modern AI assistants is their massive context windows. The ability to reason over tens of thousands of lines of code is a game-changer, but it’s not free. Every piece of information you provide—every line of code, every sentence in a chat—is counted in tokens, and tokens have a cost.

Understanding the relationship between context and cost is key to using these tools sustainably and efficiently.

The Fundamental Equation: More Tokens = More Cost

The pricing model for all underlying LLMs is based on token consumption. This includes both the input tokens (the context you provide) and the output tokens (the code and text the AI generates).

What Increases Context Cost?

• Large Files: Including a 5,000-line file in your prompt uses thousands of tokens.
• Long Conversations: A long chat history is continuously fed back into the context with each new message.
• Verbose Prompts: Unnecessarily long and detailed instructions add to the token count.
• Powerful Models: Premium models like Claude Opus 4 or using Cursor’s “Max Mode” have a higher cost per token.

Why Does it Matter?

On a subscription plan, excessive token usage will exhaust your monthly request limits faster. If you’re using a direct API key, it will directly increase your bill. Smart context management allows you to do more with your existing plan.

Strategies for Cost-Effective Context Management

Optimizing for cost doesn’t mean sacrificing quality. It means being deliberate and efficient with the context you provide.

1. Be Surgical, Not Exhaustive. Don’t attach an entire directory when a single file or function will do. The more you can narrow down the relevant “State Context,” the fewer tokens you’ll use. Start small and add more context only if the AI needs it.

2. Reset and Refresh. When you switch to a completely new task, start a new chat. In Claude Code, use the /clear command. This is the simplest and most effective way to prevent the token cost of a previous task from inflating the cost of a new one.

3. Use the Right Model for the Job. Don’t use a sledgehammer to crack a nut. For simple tasks like generating boilerplate, writing a unit test, or explaining a small code snippet, use a faster, cheaper model (like Claude Sonnet or Cursor’s “Auto” mode). Save the more expensive, high-power models for complex architectural planning or deep debugging sessions.

4. Leverage Summarization. Instead of feeding a massive file or a long document directly into the prompt, ask the AI to summarize it first.

   Summarize the main responsibilities of the @/src/services/billing/invoiceGenerator.ts file.

   You can then use this much smaller, token-efficient summary as the basis for your next prompt.

5. Trust the Built-in Tools. Tools like Codebase Indexing are designed to find relevant code without loading entire files into the context window. Trust the AI to use its index to find what it needs. A well-indexed codebase is inherently more cost-effective.

6. Monitor Your Usage. Keep an eye on your consumption. Claude Code provides a /cost command to check your spending. The Cursor Dashboard also provides a detailed breakdown of your requests and token usage.

By being mindful of the context you provide, you can strike the perfect balance between providing the AI with the information it needs and managing your costs effectively.