How to make the most economic use of LLM tokens

As AI language models become increasingly integrated into workflows, managing token usage effectively can lead to significant cost savings. Here’s how to optimize your LLM consumption without sacrificing quality.

Understand your token costs

Different models have vastly different pricing structures. Claude Sonnet might cost less per token than Opus, while smaller models like Haiku are even cheaper. Match your task complexity to the appropriate model—don’t use a premium model for simple tasks like basic text formatting or straightforward data extraction.

Craft efficient prompts

Verbose prompts consume tokens unnecessarily. Be clear and concise, eliminating filler words and redundant context. Instead of “I was wondering if you could possibly help me understand…”, simply write “Explain…”. Every word in your prompt costs tokens.

Minimize context window bloat

Each conversation turn includes the entire history, multiplying token usage. For long conversations, periodically start fresh sessions and summarize only essential context. Avoid uploading large documents when a relevant excerpt would suffice.

Use caching strategically

Some platforms cache frequently used prompts or system instructions. Structure your requests to maximize cache hits by keeping consistent prefixes or system prompts across multiple calls.

Batch and preprocess

When possible, batch similar requests together and preprocess data outside the LLM. Use traditional programming for tasks like filtering, sorting, or basic text manipulation, reserving AI for genuinely complex reasoning.

Set smart token limits

Configure maximum token limits for responses. If you need a summary, specify the desired length upfront rather than generating lengthy responses and asking for condensation later—that doubles your token spend.

Test with smaller samples

Before processing large datasets, test your prompts on small samples to refine your approach. This prevents wasting tokens on poorly constructed queries that don’t yield useful results.

The key is intentionality: every token should serve a purpose. By combining the right model selection, prompt engineering, and workflow optimization, you can reduce costs by 50% or more while maintaining output quality.