Context Caching
Reusing pre-processed input across requests to cut cost and latency.
/ quick answer
Anthropic and OpenAI cache expensive prefixes (long system prompts, docs) between calls. Cached tokens are ~10x cheaper and skip re-encoding — key for RAG and agent loops. Reusing pre-processed input across requests to cut cost and latency.
/ frequently asked
What is Context Caching?
Anthropic and OpenAI cache expensive prefixes (long system prompts, docs) between calls. Cached tokens are ~10x cheaper and skip re-encoding — key for RAG and agent loops.
What is an example of Context Caching?
A 50k-token system prompt costs $0.05 fresh but $0.005 cached across the next 5 minutes.
Why does Context Caching matter?
Reusing pre-processed input across requests to cut cost and latency.
What should I learn after Context Caching?
Open the related workflows and prompts further down this page: they show how this idea is applied in a real task, step by step.
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →Context Engineering
Context engineering is the discipline of deciding exactly what information enters a model's context window, in what order and at what cost.
- →Cost Per Token
The unit economics of LLM APIs.
- →MCP Resources
MCP resources are addressable read-only context — files, records, docs — that a client can pull into the model instead of calling a tool.
- →MCP (Model Context Protocol)
Open protocol that lets LLMs connect to tools, data sources and apps through a standard interface.
Related workflows
Turn this into a repeatable process.
- →How to Create a Website with AI
Go from idea to a live, custom-domain website in one afternoon using AI builders.
- →How to Build an AI Content System
A repeatable pipeline that turns one input into publish-ready content across every channel.
- →How to Start a Niche Website with AI
Pick a niche, validate demand, build the site, and publish ranking content using AI end-to-end.
- →Personal Research Assistant Workflow
A repeatable system to research any topic deeply in under 30 minutes.
Related tool stacks
The tools that run it in production.
- →AI Research & Knowledge Stack
Default toolset for analysts, founders and creators doing deep research with AI.
Comparisons & alternatives
Pick between the options.
- →RAG vs Long Context Windows
Is RAG obsolete now that context windows are 1M+ tokens? Not quite — here's when each wins.
- →ChatGPT vs Claude
Two leading conversational AI assistants compared across reasoning, writing, coding, and pricing.
- →Lovable vs Bolt
Two AI app builders compared on speed, backend, deployment, and production readiness.
- →OpenAI API vs Anthropic API
Choosing between the two leading LLM API providers for production apps.