Open resource for developers
Spend less on LLM tokens.
Get better results.
Practical strategies for reducing token usage across Claude, GPT, and other AI models — without sacrificing output quality.
Read the guides →Context Engineering
Token optimization is a context problem, not a prompt-shortening problem. Learn session management, JIT retrieval, and repo memory.
Read more →Prompt Caching
Cache reads cost 90% less than regular input tokens. Design your prompt architecture for maximum cache hits.
Read more →Tool Overhead
MCP servers and tool definitions can add 55K–134K tokens before any work starts. On-demand loading cuts that by 85%.
Read more →Latest guides
LLM Token Optimization Strategies: The Complete Guide for 2026Mar 65 Ways to Reduce Your LLM API Costs TodayMar 6Context Engineering: Why Reducing LLM Token Usage Isn't About Shorter PromptsMar 5Claude Code: How to Get More Done With Fewer TokensMar 4How to Reduce OpenAI and Claude API Token Costs: A Developer's GuideMar 3
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