LLM costs compound quickly due to stateless architecture, but strategic token management can dramatically reduce expenses.
Every conversation with an LLM reprocesses the entire chat history, making long threads increasingly expensive. A 10-message conversation doesn't cost 10x a single message. It costs much more due to cumulative token usage.
Understanding token economics
LLMs charge for both input tokens (your prompts, files, context) and output tokens (model responses). Each model has different pricing tiers: Claude Opus costs significantly more per token than Claude Sonnet, but may complete tasks faster.
The stateless nature means every new message includes all previous context. A conversation that starts at 100 tokens per message can balloon to 1,000+ tokens per message as history accumulates.
Conversation management
Start fresh for new tasks
Begin a new chat for each distinct task rather than continuing existing conversations. This prevents irrelevant context from inflating token counts.
Claude Desktop users: Use /clear to reset conversation history while staying in the same interface.
Summarize and compact long threads
When conversations exceed 20-30 exchanges, summarize the key context and start fresh. Preserve only essential information needed for the current task.
Claude Desktop users: Use /compact to automatically condense conversation history while retaining relevant context.
Model selection strategy
Match model to task complexity
Don't default to the most powerful model for every task. Use premium models (Claude Opus) for complex planning and reasoning, then switch to efficient models (Claude Haiku) for execution and follow-up work.
Claude Desktop users: Use /mod to manually select different models within the same conversation.
Progressive model usage
Start with a capable model to establish direction and requirements, then move to cost-effective models for iteration and refinement.
