Context Caching
Reusing pre-processed input across requests to cut cost and latency.
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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.
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 for AI and automation?
Reusing pre-processed input across requests to cut cost and latency. It connects to the workflows, prompts and tool stacks linked on this page, so you can move from definition to execution without leaving Onexial.
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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.