Prompt Caching Guide (2025): Lower AI Costs

Prompt caching is mostly about one thing: stop paying to process the same long prompt prefix every time you call a model.

If your app sends a big system prompt, a policy block, a style guide, or a chunk of reference text on every request, caching can lower both cost and time to first token. If every request is unique, it might not help much.

If you want a short overview of the basics, start with Prompt Engineering. For a deeper walkthrough, see our Prompt Engineering in 2025: Complete Guide and this primer on how to write effective AI prompts.

If you want to draft a reusable system prompt fast, try the AI Prompt Generator or the model specific generators for ChatGPT, Claude, Gemini, and Grok.

What is prompt caching?

Prompt caching means the provider stores part of your input, usually the beginning of the request, and reuses it on later requests. That reused part is commonly called the prefix.

A typical cached prefix includes:

system prompt
long instructions and formatting rules
shared reference text (policies, product docs, rubrics)
few shot examples (see prompt frameworks)

The part that usually stays fresh:

the user specific question
the document you are analyzing right now
anything that changes every request

At a high level, it looks like this:

[CACHED PREFIX]
- System prompt
- Policies and style
- Knowledge base
- Few shot examples

[FRESH SUFFIX]
- This user's request
- The current doc

The benefit is simple: cached tokens are cheaper and faster to reuse than reprocessing the same text over and over.

When caching is worth it (and when it is not)

Caching pays off when you have a stable prefix and you reuse it enough times before the cache expires.

Use caching if:

You regularly send 1,024+ tokens of shared context.
You can reuse that exact prefix across multiple calls in a short window.
Your workload is bursty (batch jobs, backfills, queued tasks).
You run multi turn chats where the system prompt stays the same.

Caching is usually not worth it if:

Your prefix changes every request (even small changes count).
Your prompts are short and do not meet the minimum prefix size.
Calls are spread out so far that the cache expires between requests.
You personalize the system prompt per user instead of pushing user data into the fresh suffix.

If you want a checklist for rewriting prompts, the prompt engineering checklist is a good companion.

Provider comparison (OpenAI vs Anthropic vs Google)

Every provider names this a bit differently, but the core idea is the same. The details below match the state of things around late 2025, and they can change, so treat the numbers as a starting point and check current docs.

Feature	OpenAI (GPT-5.1)	Anthropic (Claude 3)	Google (Gemini 3)
Caching mode	Automatic	Explicit API flag	Explicit API flag
Cache lifetime	5 to 10 minutes	5 minutes	15 minutes
Minimum prefix size	1024 tokens	1024 tokens	2048 tokens
Cached input discount	90%	90%	75%
Latency improvement	50 to 80% TTFT	60 to 85% TTFT	40 to 70% TTFT
Longer cache option	Yes (GPT-5.1 Pro)	No	Yes (Gemini 3 Pro)

TTFT means time to first token.

For model specific prompting tips, see our guides on Claude and Gemini.

OpenAI prompt caching (GPT-5.1)

OpenAI caching is the easiest to benefit from because it is automatic. In practice, you mainly need to:

keep the shared prefix identical across requests
hit the minimum prefix length (often 1024 tokens)
reuse it within the cache window (often 5 to 10 minutes)

Simple example

Here is a toy example that shows the shape of the savings. Pricing varies by model and can change, so use this as a mental model.

# Request 1
response = openai.chat.completions.create(
    model="gpt-5.1-turbo",
    messages=[
        {"role": "system", "content": long_system_prompt},   # stable, long
        {"role": "user", "content": "Analyze document 1"}     # changes
    ]
)

# Request 2 (same system prompt, different document)
response = openai.chat.completions.create(
    model="gpt-5.1-turbo",
    messages=[
        {"role": "system", "content": long_system_prompt},   # cached
        {"role": "user", "content": "Analyze document 2"}
    ]
)

What to watch:

Any change to the cached prefix can cause a cache miss.
Do not put timestamps, request IDs, or user names in the system prompt.

Longer sessions with extended caching

If you build longer chats (support bots, copilots, agents), a longer cache window can help. For system prompt design tips, see our GPT-5.1 prompting update.

Anthropic prompt caching (Claude 3)

With Anthropic, you have to mark what you want cached. The pattern is:

send the long, stable system content with cache control
reuse the same system content within the cache window
keep all user specific data in the message that stays fresh

Example:

response = anthropic.messages.create(
    model="claude-3-opus-20240229",
    max_tokens=1024,
    system=[
        {
            "type": "text",
            "text": long_system_prompt,
            "cache_control": {"type": "ephemeral"}
        }
    ],
    messages=[{"role": "user", "content": "Analyze document 1"}]
)

If you want help drafting Claude system prompts, you can start from the Claude prompt generator or browse free Claude prompt templates.

Google context caching (Gemini 3)

Google's approach is closer to "upload stable context once, then reference it by ID". You create a cached content object with a TTL, then reuse it:

import google.generativeai as genai

cache = genai.caching.CachedContent.create(
    model="gemini-3-pro",
    system_instruction=long_system_prompt,
    ttl=datetime.timedelta(minutes=15),
)

response = genai.GenerativeModel.generate_content(
    model="gemini-3-pro",
    cached_content=cache.name,
    contents="Analyze document 1",
)

If you are writing prompts for Gemini often, the Gemini prompt generator is a quick way to get a clean first draft. If you prefer a free version, use the free Gemini prompt generator.

How to structure prompts so caching actually hits

Caching only helps when your prefix stays identical. The goal is to make the cached part stable, and push anything that varies into the suffix.

1. Put stable content first

Good candidates for the prefix:

system prompt
policies and formatting rules
shared reference text
few shot examples

2. Keep variable content out of the prefix

Common cache killers:

"today is ..."
"user is in timezone ..."
request IDs and trace IDs
per user personalization in the system prompt

If you need that data, pass it in the fresh user message instead.

3. Avoid tiny edits in the prefix

Even small edits can break reuse:

Request 1 system prompt: "You are a helpful assistant. Today is Nov 29."
Request 2 system prompt: "You are a helpful assistant. Today is Nov 30."

Move the date to the user message and keep the system prompt stable.

4. Treat prompts like code

When a prompt is used in production, version it, review changes, and test it. This keeps you from accidentally breaking caching and quality at the same time. See Prompt Testing, Versioning, and CI/CD.

If you keep prompts in one place for your team, a shared library helps. See Prompt Libraries.

Simple cost model (cached vs not cached)

You do not need a big spreadsheet to sanity check this. Track:

P: prefix tokens (stable)
S: suffix tokens (variable)
N: number of requests in the cache window
r: cached token price as a fraction of normal (for example 0.1 for 90% off)

Then compare:

without caching: N * (P + S)
with caching: (P + S) + (N - 1) * (r * P + S)

Worked example

Assume cached tokens cost 10% of normal, your prefix is 3000 tokens, your suffix is 1000 tokens, and you make 100 requests in the cache window.

Without caching: 100 * (3000 + 1000) = 400,000 full price input tokens
With caching: 4000 + 99 * (0.1 * 3000 + 1000) = 132,700 full price equivalent tokens

That is about a 67% reduction on the input side.

Multi tenant caching (shared prefix plus user memory)

If you serve many users, you can split context into layers:

a global prefix shared by everyone (policies, company docs)
a per user memory block
the current task

That keeps most of your context reusable without mixing user data across caches.

If you are building agent style systems, the Context Engineering Agents Guide is a helpful next read.

FAQ

Does caching change output quality? No. The model should behave the same. You are changing how the provider handles repeated input, not what the model sees.

Can I reuse a cache across different models? No. Caches are model specific.

How do I know caching is working? Check response metadata and logs. Each provider exposes this in different fields and headers.

Should I cache everything? No. Cache the parts that are long and stable. Keep personal data and anything that changes in the fresh suffix.

Next steps

Working across models: Claude vs ChatGPT vs Gemini
Prompt style patterns: prompt frameworks and prompt chaining
Comparing tools: best prompt builder tools
Free starting points: free ChatGPT prompt generator, free ChatGPT prompt improver, and free Claude prompt templates
Research focused flows: Perplexity prompt generator
Open and self hosted models: DeepSeek prompt generator, Llama prompt generator, and Mistral prompt generator

Key takeaways

Caching helps when you reuse the same long prefix.
Keep the prefix stable and push changing data to the suffix.
Expect the biggest wins in batch workloads and multi turn chats.
Version prompts and test changes so you do not lose cache hits by accident.

Quick way to try it

Pick one workflow that repeats the same instructions (batch doc analysis is a good start).
Move anything stable to the prefix and keep user or doc specific data in the suffix.
Run 10 requests back to back and compare input token spend and time to first token.
For Anthropic or Google, confirm you are setting cache_control or CachedContent correctly.

If you are changing prompts often, read Prompt Testing, Versioning, and CI/CD so you do not break caching by accident.

Wrap up

Prompt caching is not magic. It just rewards you for reusing the same prefix. Start with one workload, measure cache hits, then expand.

Still deciding which provider to use? Check out our Claude vs ChatGPT vs Gemini comparison.

Prompt Caching and Token Costs in 2025: How to Spend Less Without Losing Quality