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Prompt caching optimizes your API usage by allowing resuming from specific prefixes in your prompts. This significantly reduces processing time and costs for repetitive tasks or prompts with consistent elements.

To learn how zero data retention (ZDR) applies to this feature, see API and data retention.

There are two ways to enable prompt caching:

  • Automatic caching: Add a single cache_control field at the top level of your request. The system automatically applies the cache breakpoint to the last cacheable block and moves it forward as conversations grow. Best for multi-turn conversations where the growing message history should be cached automatically.
  • Explicit cache breakpoints: Place cache_control directly on individual content blocks for fine-grained control over exactly what gets cached.

The simplest way to start is with automatic caching:

bash
curl https://api.anthropic.com/v1/messages \
  -H "content-type: application/json" \
  -H "x-api-key: $ANTHROPIC_API_KEY" \
  -H "anthropic-version: 2023-06-01" \
  -d '{
    "model": "claude-opus-5",
    "max_tokens": 1024,
    "cache_control": {"type": "ephemeral"},
    "system": "You are an AI assistant tasked with analyzing literary works. Your goal is to provide insightful commentary on themes, characters, and writing style.",
    "messages": [
      {
        "role": "user",
        "content": "Analyze the major themes in Pride and Prejudice."
      }
    ]
  }'
bash
ant messages create --transform usage <<'YAML'
model: claude-opus-5
max_tokens: 1024
cache_control:
  type: ephemeral
system: >-
  You are an AI assistant tasked with analyzing literary works. Your goal is
  to provide insightful commentary on themes, characters, and writing style.
messages:
  - role: user
    content: Analyze the major themes in Pride and Prejudice.
YAML
python
client = anthropic.Anthropic()

response = client.messages.create(
    model="claude-opus-5",
    max_tokens=1024,
    cache_control={"type": "ephemeral"},
    system="You are an AI assistant tasked with analyzing literary works. Your goal is to provide insightful commentary on themes, characters, and writing style.",
    messages=[
        {
            "role": "user",
            "content": "Analyze the major themes in 'Pride and Prejudice'.",
        }
    ],
)
print(response.usage.model_dump_json())
typescript
const client = new Anthropic();

const response = await client.messages.create({
  model: "claude-opus-5",
  max_tokens: 1024,
  cache_control: { type: "ephemeral" },
  system:
    "You are an AI assistant tasked with analyzing literary works. Your goal is to provide insightful commentary on themes, characters, and writing style.",
  messages: [
    {
      role: "user",
      content: "Analyze the major themes in 'Pride and Prejudice'."
    }
  ]
});
console.log(response.usage);
csharp
AnthropicClient client = new();

var parameters = new MessageCreateParams
{
    Model = Model.ClaudeOpus5,
    MaxTokens = 1024,
    CacheControl = new CacheControlEphemeral(),
    System = "You are an AI assistant tasked with analyzing literary works. Your goal is to provide insightful commentary on themes, characters, and writing style.",
    Messages =
    [
        new()
        {
            Role = Role.User,
            Content = "Analyze the major themes in 'Pride and Prejudice'."
        }
    ]
};

var message = await client.Messages.Create(parameters);
Console.WriteLine(message.Usage);
go
client := anthropic.NewClient()

response, err := client.Messages.New(context.TODO(), anthropic.MessageNewParams{
	Model:        anthropic.ModelClaudeOpus5,
	MaxTokens:    1024,
	CacheControl: anthropic.NewCacheControlEphemeralParam(),
	System: []anthropic.TextBlockParam{
		{Text: "You are an AI assistant tasked with analyzing literary works. Your goal is to provide insightful commentary on themes, characters, and writing style."},
	},
	Messages: []anthropic.MessageParam{
		anthropic.NewUserMessage(anthropic.NewTextBlock("Analyze the major themes in 'Pride and Prejudice'.")),
	},
})
if err != nil {
	log.Fatal(err)
}
fmt.Println(response.Usage.RawJSON())
java
import com.anthropic.models.messages.CacheControlEphemeral;
// ...
public class PromptCachingExample {

  public static void main(String[] args) {
    AnthropicClient client = AnthropicOkHttpClient.fromEnv();

    MessageCreateParams params = MessageCreateParams.builder()
        .model(Model.CLAUDE_OPUS_5)
        .maxTokens(1024)
        .cacheControl(CacheControlEphemeral.builder().build())
        .system("You are an AI assistant tasked with analyzing literary works. Your goal is to provide insightful commentary on themes, characters, and writing style.")
        .addUserMessage("Analyze the major themes in 'Pride and Prejudice'.")
        .build();

    Message message = client.messages().create(params);
    System.out.println(message.usage());
  }
}
php
use Anthropic\Messages\CacheControlEphemeral;
// ...
$client = new Client();

$response = $client->messages->create(
    maxTokens: 1024,
    messages: [
        ['role' => 'user', 'content' => "Analyze the major themes in 'Pride and Prejudice'."]
    ],
    model: 'claude-opus-5',
    cacheControl: CacheControlEphemeral::with(),
    system: "You are an AI assistant tasked with analyzing literary works. Your goal is to provide insightful commentary on themes, characters, and writing style.",
);
echo json_encode($response->usage);
ruby
client = Anthropic::Client.new

response = client.messages.create(
  model: "claude-opus-5",
  max_tokens: 1024,
  cache_control: {type: "ephemeral"},
  system: "You are an AI assistant tasked with analyzing literary works. Your goal is to provide insightful commentary on themes, characters, and writing style.",
  messages: [
    {
      role: "user",
      content: "Analyze the major themes in 'Pride and Prejudice'."
    }
  ]
)
puts response.usage

With automatic caching, the system caches all content up to and including the last cacheable block. On subsequent requests with the same prefix, cached content is reused automatically.

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