Prompt Caching Explained: Reduce LLM Costs and Get Faster Responses

Every API call to an LLM processes your entire prompt from scratch. System instructions, reference documents, and conversation history are reprocessed with each request, even when 99% stays identical.

A RAG application sends the same knowledge base repeatedly. A coding assistant re-analyzes unchanged code files. A chatbot rereads its instructions thousands of times per day.

This redundancy becomes expensive at scale. Developers pay full price for processing the same tokens over and over, while users wait for responses that should be instant. Prompt caching changes this dynamic by storing and reusing processed content. For LLM applications, this translates to 90% cost reductions and 80% faster responses.

What Is Prompt Caching?

Prompt caching is a technique that stores frequently used parts of your prompts on the server side. This means you don't pay to reprocess the same information repeatedly. When you send similar requests to a large language model, the cached portions remain available for quick reuse.

The technology has become essential for developers working with LLMs like Claude Opus 4.5 and Gemini 3 Pro. It dramatically reduces both costs and response times for applications that use consistent context or instructions.

How Does Prompt Caching Work in Large Language Models?

Large language models process every token in your prompt each time you make a request. This includes your system instructions, reference documents, conversation history, and the actual query. Without caching, the model treats each request as completely new.

Prompt caching changes this workflow. The system identifies static portions of your prompt and stores their processed state. When you submit a new request with the same prefix, the model skips reprocessing those cached tokens.

The cache typically stores the internal representations of tokens after they've been processed through the model's initial layers. This stored state remains available for a specified time period, usually ranging from 5 to 15 minutes depending on the provider.

Here's what gets cached:

• System prompts and instructions
• Long reference documents
• Few-shot examples
• Conversation history up to the cache point
• Any static context you define

The model only processes new tokens that appear after the cached portion. This creates significant efficiency gains for applications with repetitive context.

Benefits of Prompt Caching for API Costs and Latency

Prompt caching delivers two primary advantages that make it essential for production LLM applications. It cuts operational costs by reducing redundant token processing and speeds up response times by eliminating unnecessary computation. These benefits become more pronounced as your application scales.

Cost Reduction

Prompt caching offers substantial savings on API costs. Most providers charge 90% less for cached tokens compared to regular input tokens. If your application sends the same 10,000-token document with each request, caching reduces that cost by roughly 90%.

The savings compound with volume. Applications making thousands of requests per day see the most dramatic cost reductions. Even smaller projects benefit when they use consistent system prompts or reference materials.

Latency Improvements

Response times improve significantly with caching. The model skips processing cached tokens entirely, which can reduce time-to-first-token by 80% or more. This matters most for applications where users expect instant responses.

Interactive applications benefit the most. Chatbots, coding assistants, and real-time analysis tools all become noticeably faster. Users experience more fluid conversations without the delays typically associated with long context windows.

Prompt Caching in Different AI Models

Each major LLM provider implements prompt caching differently. Understanding these variations helps you choose the right platform for your needs and optimize your implementation accordingly.

Anthropic Claude Prompt Caching

Claude offers prompt caching across its model family, including Claude Opus 4.5, Claude Sonnet 4.5, Claude Sonnet 4, Claude Haiku 4.5, and Claude Haiku 3.5. The feature provides substantial cost savings with cache reads priced at just 10% of regular input tokens.

The cache persists for 5 minutes of inactivity by default. Each new request that uses the cache extends this timer automatically at no additional cost. For longer-duration needs, Anthropic offers a 1-hour cache option at 2x the base input token price, ideal for agentic workflows or batch processing.

You implement caching by marking content blocks with cache_control breakpoints. The system caches everything before these breakpoints, including tools, system messages, text messages, images, documents, and tool use blocks. You can set up to 4 cache breakpoints to separate sections that change at different frequencies.

Minimum cacheable lengths vary by model. Claude Opus 4.5 and Haiku 4.5 require 4,096 tokens, while Claude Sonnet models require 1,024 tokens. Shorter prompts cannot be cached even when marked with cache_control.

Anthropic's pricing structure makes caching highly cost-effective. For Claude Sonnet 4.5, cache writes cost $3.75 per million tokens (25% premium over base input), while cache reads cost only $0.30 per million tokens (90% discount). This makes Claude's caching ideal for sustained conversations, RAG applications, and multi-turn workflows.

Google Gemini Context Caching

Google calls their implementation context caching and offers two distinct approaches.

• Implicit Caching
• Explicit Caching

Implicit caching activates automatically on most Gemini models with no setup required. The system detects repeated content and applies cost savings automatically. Minimum token requirements are:

• 1,024 tokens for Gemini 2.5 Flash and Gemini 3 Flash Preview
• 4,096 tokens for Gemini 2.5 Pro and Gemini 3 Pro Preview

Explicit caching gives you full control with guaranteed cost savings. You manually create cache objects with specific time-to-live settings. The default TTL is 1 hour, but you can set custom durations based on your needs. This approach works best when you know exactly what content to cache and for how long.

Google charges separately for cache storage based on time. You pay for both the initial cache write and the storage duration.

Cache hits cost significantly less than processing fresh tokens. The explicit caching model suits applications like chatbots with extensive system instructions, repetitive video analysis, recurring queries against large document sets, and frequent code repository reviews.

OpenAI Prompt Caching Features

OpenAI offers prompt caching for GPT-4o and newer models like GPT-5.2. The system automatically caches prefixes longer than 1,024 tokens. You don't need to make any code changes because caching works automatically on all eligible requests.

The caching mechanism routes requests based on a hash of your prompt's initial prefix, typically the first 256 tokens. When multiple requests share the same prefix, they get routed to the same server for cache hits. OpenAI provides a 50% discount on cached input tokens.

OpenAI offers two cache retention policies.

• The in-memory policy keeps cached prefixes active for 5-10 minutes of inactivity, with a maximum of one hour.
• The extended retention policy maintains caches for up to 24 hours, available on GPT-5 series and GPT-4.1 models.

You set the retention policy using the prompt_cache_retention parameter.

The system can cache various content types including the complete messages array, images in user messages, tool definitions, and structured output schemas. All of these count toward the 1,024 token minimum. OpenAI's automatic approach simplifies implementation but offers less granular control than explicit cache breakpoints.

Prompt Caching vs Context Caching in LLMs

The terms often get used interchangeably, but they can have subtle differences depending on the provider.

Prompt caching typically refers to caching the prefix of your prompt which is the static portions that don't change between requests. A coding assistant caches its system instructions like "You are a Python expert. Always follow PEP 8 guidelines. Provide explanations with code examples."

These instructions stay the same across all user requests. Each new coding question only processes the user's specific query.

Context caching is essentially the same concept but emphasizes the caching of contextual information. Google uses this term to highlight their focus on storing large context windows for extended periods.

A legal research tool caches an entire 50-page contract document. Multiple lawyers can ask different questions about specific clauses, termination conditions, or liability terms. The full contract remains cached while only the individual questions get processed.

The core mechanism remains identical. Both approaches store processed representations of tokens to avoid redundant computation. The terminology difference is mainly marketing rather than technical.

Some developers use "prompt caching" for short-lived caches of system instructions, while "context caching" refers to longer retention of document collections or knowledge bases. In practice, the features work the same way across providers.

Use Cases for Prompt Caching in RAG Applications

Retrieval-augmented generation applications benefit enormously from prompt caching. These systems retrieve relevant documents and include them in each prompt. Without caching, you pay to process the same retrieved content multiple times.

Common RAG scenarios include:

• Customer Support Systems: Cache your company's documentation, policies, and FAQ content. Each user query only processes the question itself, not the entire knowledge base.
• Code Analysis Tools: Store your codebase or specific files in the cache. Multiple queries about the same code don't require reprocessing the entire file.
• Research Assistants: Cache academic papers or research documents. You can ask multiple questions about the same paper without repeatedly sending its full text.
• Document Q&A: Upload a contract, report, or manual once. All subsequent questions about that document use the cached version.

The pattern works best when users ask multiple questions about the same context. Single-question interactions gain less benefit from caching.

How to Implement Prompt Caching in API Calls

Implementation varies by provider, but the general pattern remains consistent. You structure your prompt so static content appears first, followed by dynamic content.

For Anthropic Claude, you add cache control breakpoints to your messages:

{
"model": "claude-3-5-sonnet-20241022",
"max_tokens": 1024,
"system": [
{
"type": "text",
"text": "Your system prompt here"
},
{
"type": "text",
"text": "Your reference documents",
"cache_control": {"type": "ephemeral"}
}
],
"messages": [...]
}

Google Gemini requires you to create a cached content object first:

cached_content = client.caches.create(
model='gemini-1.5-flash',
contents=[...],
ttl=datetime.timedelta(minutes=60)
)

OpenAI handles caching automatically. You simply structure your prompt with static content first. The system detects repeated prefixes and caches them.

Best practices apply across providers:

• Place all static content at the beginning
• Keep your cache breakpoint in the same location
• Maintain consistent formatting for cached sections
• Monitor cache hit rates to optimize placement

Prompt Caching Pricing Comparison Across LLM Providers

Pricing structures differ significantly between providers. Understanding these differences helps you choose the right platform for your use case.

• Anthropic Claude charges $6.25 and $10 per million tokens for 5m and 1h cache writes and $0.50 per million tokens for cache reads (for Claude Opus 4.5). This represents a 90% discount on cached content. The 5-minute cache lifetime works well for interactive applications.
• Google Gemini uses a different model. Gemini charges $0.20 for prompts having less than or equal to 200k tokens, $0.40 for prompts with more than 200k tokens, and $4.50 per million tokens per hour (for Gemini 3 Pro). The longer cache duration reduces costs for batch processing.
• OpenAI charges $1.750 per million input tokens and $0.175 per million cached tokens for GPT-5.2. Their discount is less aggressive than competitors, but automatic caching simplifies implementation.

The best choice depends on your usage pattern. Frequent requests with short cache lifetimes favor Anthropic. Long-running batch jobs with extended caching benefit from Gemini. OpenAI suits developers who want simplicity over optimization.

Calculate your specific use case by estimating cache hit rates and request frequency. Most applications see 70-95% cache hit rates once properly implemented, translating to substantial cost savings regardless of provider.

Conclusion

Prompt caching has become a critical optimization for LLM applications. It reduces costs and improves latency for any application with repeated context. Whether you're building a chatbot, RAG system, or code assistant, caching delivers immediate benefits.

Start by identifying your static prompt components. Implement caching with your chosen provider's API. Monitor your cache hit rates and adjust your implementation for optimal performance. The investment in proper caching setup pays dividends in both user experience and operational costs.