Batch Processing ↗

anthropic-platform guide intermediate batches

Editorial Notes

Batch processing is the single biggest lever for cutting Claude API costs on non-urgent workloads — it runs requests asynchronously at roughly half the price, which is why it appears in both the cost-optimization and deployment paths. Pay close attention to the 24-hour completion window and the polling and retrieval flow, since batches are not interactive and you design around eventual results. The common pitfall is reaching for batch on latency-sensitive paths where it does not belong. OpenAI offers an equivalent Batch API with a similar discount but a different file-based job format; read the rate-limits page next to see how batch sidesteps per-minute caps.

Original Documentation

Batch processing is a powerful approach for handling large volumes of requests efficiently. Instead of processing requests one at a time with immediate responses, batch processing allows you to submit multiple requests together for asynchronous processing. This pattern is particularly useful when:

You need to process large volumes of data
Immediate responses are not required
You want to optimize for cost efficiency
You’re running large-scale evaluations or analyses

The Message Batches API is Anthropic’s first implementation of this pattern.

This feature is not covered by Zero Data Retention (ZDR) arrangements. Data is retained according to the feature’s standard retention policy.

Message Batches API#

The Message Batches API is a powerful, cost-effective way to asynchronously process large volumes of Messages requests. This approach is well-suited to tasks that do not require immediate responses, with most batches finishing in less than 1 hour while reducing costs by 50% and increasing throughput.

You can explore the API reference directly, in addition to this guide.

How the Message Batches API works#

When you send a request to the Message Batches API:

The system creates a new Message Batch with the provided Messages requests.
The batch is then processed asynchronously, with each request handled independently.
You can poll for the status of the batch and retrieve results when processing has ended for all requests.

This is especially useful for bulk operations that don’t require immediate results, such as:

Large-scale evaluations: Process thousands of test cases efficiently.
Content moderation: Analyze large volumes of user-generated content asynchronously.
Data analysis: Generate insights or summaries for large datasets.
Bulk content generation: Create large amounts of text for various purposes (e.g., product descriptions, article summaries).

Batch limitations#

A Message Batch is limited to either 100,000 Message requests or 256 MB in size, whichever is reached first.
The system processes each batch as fast as possible, with most batches completing within 1 hour. You will be able to access batch results when all messages have completed or after 24 hours, whichever comes first. Batches will expire if processing does not complete within 24 hours.
Batch results are available for 29 days after creation. After that, you may still view the Batch, but its results will no longer be available for download.
Batches are scoped to a Workspace. You may view all batches (and their results) that were created within the Workspace that your API key belongs to.
Rate limits apply to both Batches API HTTP requests and the number of requests within a batch waiting to be processed. See Message Batches API rate limits. Additionally, processing may be slowed down based on current demand and your request volume. In that case, you may see more requests expiring after 24 hours.
Due to high throughput and concurrent processing, batches may go slightly over your Workspace’s configured spend limit.

Supported models#

All active models support the Message Batches API.

What can be batched#

Any request that you can make to the Messages API can be included in a batch. This includes:

Vision
Tool use
System messages
Multi-turn conversations
Any beta features

Since each request in the batch is processed independently, you can mix different types of requests within a single batch.

Since batches can take longer than 5 minutes to process, consider using the 1-hour cache duration with prompt caching for better cache hit rates when processing batches with shared context.

Pricing#

The Batches API offers significant cost savings. All usage is charged at 50% of the standard API prices.

Model	Batch input	Batch output
Claude Opus 4.6	$2.50 / MTok	$12.50 / MTok
Claude Opus 4.5	$2.50 / MTok	$12.50 / MTok
Claude Opus 4.1	$7.50 / MTok	$37.50 / MTok
Claude Opus 4	$7.50 / MTok	$37.50 / MTok
Claude Sonnet 4.6	$1.50 / MTok	$7.50 / MTok
Claude Sonnet 4.5	$1.50 / MTok	$7.50 / MTok
Claude Sonnet 4	$1.50 / MTok	$7.50 / MTok
Claude Sonnet 3.7 (deprecated)	$1.50 / MTok	$7.50 / MTok
Claude Haiku 4.5	$0.50 / MTok	$2.50 / MTok
Claude Haiku 3.5	$0.40 / MTok	$2 / MTok
Claude Opus 3 (deprecated)	$7.50 / MTok	$37.50 / MTok
Claude Haiku 3	$0.125 / MTok	$0.625 / MTok

How to use the Message Batches API#

Prepare and create your batch#

A Message Batch is composed of a list of requests to create a Message. The shape of an individual request is comprised of:

A unique custom_id for identifying the Messages request
A params object with the standard Messages API parameters

You can create a batch by passing this list into the requests parameter:

curl https://api.anthropic.com/v1/messages/batches \
     --header "x-api-key: $ANTHROPIC_API_KEY" \
     --header "anthropic-version: 2023-06-01" \
     --header "content-type: application/json" \
     --data \
'{
    "requests": [
        {
            "custom_id": "my-first-request",
            "params": {
                "model": "claude-opus-4-6",
                "max_tokens": 1024,
                "messages": [
                    {"role": "user", "content": "Hello, world"}
                ]
            }
        },
        {
            "custom_id": "my-second-request",
            "params": {
                "model": "claude-opus-4-6",
                "max_tokens": 1024,
                "messages": [
                    {"role": "user", "content": "Hi again, friend"}
                ]
            }
        }
    ]
}'

import anthropic
from anthropic.types.message_create_params import MessageCreateParamsNonStreaming
from anthropic.types.messages.batch_create_params import Request

client = anthropic.Anthropic()

message_batch = client.messages.batches.create(
    requests=[
        Request(
            custom_id="my-first-request",
            params=MessageCreateParamsNonStreaming(
                model="claude-opus-4-6",
                max_tokens=1024,
                messages=[
                    {
                        "role": "user",
                        "content": "Hello, world",
                    }
                ],
            ),
        ),
        Request(
            custom_id="my-second-request",
            params=MessageCreateParamsNonStreaming(
                model="claude-opus-4-6",
                max_tokens=1024,
                messages=[
                    {
                        "role": "user",
                        "content": "Hi again, friend",
                    }
                ],
            ),
        ),
    ]
)

print(message_batch)


const anthropic = new Anthropic();

const messageBatch = await anthropic.messages.batches.create({
  requests: [
    {
      custom_id: "my-first-request",
      params: {
        model: "claude-opus-4-6",
        max_tokens: 1024,
        messages: [{ role: "user", content: "Hello, world" }]
      }
    },
    {
      custom_id: "my-second-request",
      params: {
        model: "claude-opus-4-6",
        max_tokens: 1024,
        messages: [{ role: "user", content: "Hi again, friend" }]
      }
    }
  ]
});

console.log(messageBatch);

import com.anthropic.client.AnthropicClient;
import com.anthropic.client.okhttp.AnthropicOkHttpClient;
import com.anthropic.models.messages.Model;
import com.anthropic.models.messages.batches.*;

public class BatchExample {

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

    BatchCreateParams params = BatchCreateParams.builder()
      .addRequest(
        BatchCreateParams.Request.builder()
          .customId("my-first-request")
          .params(
            BatchCreateParams.Request.Params.builder()
              .model(Model.CLAUDE_OPUS_4_6)
              .maxTokens(1024)
              .addUserMessage("Hello, world")
              .build()
          )
          .build()
      )
      .addRequest(
        BatchCreateParams.Request.builder()
          .customId("my-second-request")
          .params(
            BatchCreateParams.Request.Params.builder()
              .model(Model.CLAUDE_OPUS_4_6)
              .maxTokens(1024)
              .addUserMessage("Hi again, friend")
              .build()
          )
          .build()
      )
      .build();

    MessageBatch messageBatch = client.messages().batches().create(params);

    System.out.println(messageBatch);
  }
}

package main

import (
	"context"
	"fmt"

	"github.com/anthropics/anthropic-sdk-go"
)

func main() {
	client := anthropic.NewClient()

	batch, _ := client.Messages.Batches.New(context.Background(),
		anthropic.BatchCreateParams{
			Requests: []anthropic.BatchCreateParamsRequest{
				{
					CustomID: "my-first-request",
					Params: anthropic.BatchCreateParamsRequestParams{
						Model:     anthropic.ModelClaudeOpus4_6,
						MaxTokens: 1024,
						Messages: []anthropic.MessageParam{
							anthropic.NewUserMessage(
								anthropic.NewTextBlock("Hello, world"),
							),
						},
					},
				},
				{
					CustomID: "my-second-request",
					Params: anthropic.BatchCreateParamsRequestParams{
						Model:     anthropic.ModelClaudeOpus4_6,
						MaxTokens: 1024,
						Messages: []anthropic.MessageParam{
							anthropic.NewUserMessage(
								anthropic.NewTextBlock("Hi again, friend"),
							),
						},
					},
				},
			},
		})

	fmt.Println(batch.ID)
}

require "anthropic"

client = Anthropic::Client.new

batch = client.messages.batches.create(
  requests: [
    {
      custom_id: "my-first-request",
      params: {
        model: "claude-opus-4-6",
        max_tokens: 1024,
        messages: [
          { role: "user", content: "Hello, world" }
        ]
      }
    },
    {
      custom_id: "my-second-request",
      params: {
        model: "claude-opus-4-6",
        max_tokens: 1024,
        messages: [
          { role: "user", content: "Hi again, friend" }
        ]
      }
    }
  ]
)

puts batch

using Anthropic;

var client = new AnthropicClient();

var batch = await client.Messages.Batches.CreateAsync(
    new BatchCreateParams
    {
        Requests = new[]
        {
            new BatchCreateParams.Request
            {
                CustomId = "my-first-request",
                Params = new BatchCreateParams.Request.Params
                {
                    Model = "claude-opus-4-6",
                    MaxTokens = 1024,
                    Messages = new[]
                    {
                        new MessageParam { Role = "user", Content = "Hello, world" }
                    }
                }
            },
            new BatchCreateParams.Request
            {
                CustomId = "my-second-request",
                Params = new BatchCreateParams.Request.Params
                {
                    Model = "claude-opus-4-6",
                    MaxTokens = 1024,
                    Messages = new[]
                    {
                        new MessageParam { Role = "user", Content = "Hi again, friend" }
                    }
                }
            }
        }
    });

Console.WriteLine(batch);

<?php

use Anthropic\Client;

$client = new Client(
    apiKey: getenv("ANTHROPIC_API_KEY")
);

$batch = $client->messages->batches->create([
    'requests' => [
        [
            'custom_id' => 'my-first-request',
            'params' => [
                'model' => 'claude-opus-4-6',
                'max_tokens' => 1024,
                'messages' => [
                    ['role' => 'user', 'content' => 'Hello, world']
                ]
            ]
        ],
        [
            'custom_id' => 'my-second-request',
            'params' => [
                'model' => 'claude-opus-4-6',
                'max_tokens' => 1024,
                'messages' => [
                    ['role' => 'user', 'content' => 'Hi again, friend']
                ]
            ]
        ]
    ]
]);

print_r($batch);

In this example, two separate requests are batched together for asynchronous processing. Each request has a unique custom_id and contains the standard parameters you’d use for a Messages API call.

Test your batch requests with the Messages API

Validation of the params object for each message request is performed asynchronously, and validation errors are returned when processing of the entire batch has ended. You can ensure that you are building your input correctly by verifying your request shape with the Messages API first.

When a batch is first created, the response will have a processing status of in_progress.

{
  "id": "msgbatch_01HkcTjaV5uDC8jWR4ZsDV8d",
  "type": "message_batch",
  "processing_status": "in_progress",
  "request_counts": {
    "processing": 2,
    "succeeded": 0,
    "errored": 0,
    "canceled": 0,
    "expired": 0
  },
  "ended_at": null,
  "created_at": "2024-09-24T18:37:24.100435Z",
  "expires_at": "2024-09-25T18:37:24.100435Z",
  "cancel_initiated_at": null,
  "results_url": null
}

Tracking your batch#

The Message Batch’s processing_status field indicates the stage of processing the batch is in. It starts as in_progress, then updates to ended once all the requests in the batch have finished processing, and results are ready. You can monitor the state of your batch by visiting the Console, or using the retrieval endpoint.

Polling for Message Batch completion#

To poll a Message Batch, you’ll need its id, which is provided in the response when creating a batch or by listing batches. You can implement a polling loop that checks the batch status periodically until processing has ended:

import anthropic
import time

client = anthropic.Anthropic()

message_batch = None
while True:
    message_batch = client.messages.batches.retrieve(MESSAGE_BATCH_ID)
    if message_batch.processing_status == "ended":
        break

    print(f"Batch {MESSAGE_BATCH_ID} is still processing...")
    time.sleep(60)
print(message_batch)


const anthropic = new Anthropic();

let messageBatch;
while (true) {
  messageBatch = await anthropic.messages.batches.retrieve(MESSAGE_BATCH_ID);
  if (messageBatch.processing_status === "ended") {
    break;
  }

  console.log(`Batch ${messageBatch} is still processing... waiting`);
  await new Promise((resolve) => setTimeout(resolve, 60_000));
}
console.log(messageBatch);

#!/bin/sh

until [[ $(curl -s "https://api.anthropic.com/v1/messages/batches/$MESSAGE_BATCH_ID" \
          --header "x-api-key: $ANTHROPIC_API_KEY" \
          --header "anthropic-version: 2023-06-01" \
| grep -o '"processing_status":[[:space:]]*"[^"]*"' \
| cut -d'"' -f4) == "ended" ]]; do
    echo "Batch $MESSAGE_BATCH_ID is still processing..."
    sleep 60
done

echo "Batch $MESSAGE_BATCH_ID has finished processing"

Listing all Message Batches#

You can list all Message Batches in your Workspace using the list endpoint. The API supports pagination, automatically fetching additional pages as needed:

import anthropic

client = anthropic.Anthropic()

# Automatically fetches more pages as needed.
for message_batch in client.messages.batches.list(limit=20):
    print(message_batch)


const anthropic = new Anthropic();

// Automatically fetches more pages as needed.
for await (const messageBatch of anthropic.messages.batches.list({
  limit: 20
})) {
  console.log(messageBatch);
}

#!/bin/sh

if ! command -v jq &> /dev/null; then
    echo "Error: This script requires jq. Please install it first."
    exit 1
fi

BASE_URL="https://api.anthropic.com/v1/messages/batches"

has_more=true
after_id=""

while [ "$has_more" = true ]; do
    # Construct URL with after_id if it exists
    if [ -n "$after_id" ]; then
        url="${BASE_URL}?limit=20&after_id=${after_id}"
    else
        url="$BASE_URL?limit=20"
    fi

    response=$(curl -s "$url" \
              --header "x-api-key: $ANTHROPIC_API_KEY" \
              --header "anthropic-version: 2023-06-01")

    # Extract values using jq
    has_more=$(echo "$response" | jq -r '.has_more')
    after_id=$(echo "$response" | jq -r '.last_id')

    # Process and print each entry in the data array
    echo "$response" | jq -c '.data[]' | while read -r entry; do
        echo "$entry" | jq '.'
    done
done

import com.anthropic.client.AnthropicClient;
import com.anthropic.client.okhttp.AnthropicOkHttpClient;
import com.anthropic.models.messages.batches.*;

public class BatchListExample {

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

    // Automatically fetches more pages as needed
    for (MessageBatch messageBatch : client
      .messages()
      .batches()
      .list(BatchListParams.builder().limit(20).build())) {
      System.out.println(messageBatch);
    }
  }
}

Retrieving batch results#

Once batch processing has ended, each Messages request in the batch will have a result. There are 4 result types:

Result Type	Description
`succeeded`	Request was successful. Includes the message result.
`errored`	Request encountered an error and a message was not created. Possible errors include invalid requests and internal server errors. You will not be billed for these requests.
`canceled`	User canceled the batch before this request could be sent to the model. You will not be billed for these requests.
`expired`	Batch reached its 24 hour expiration before this request could be sent to the model. You will not be billed for these requests.

You will see an overview of your results with the batch’s request_counts, which shows how many requests reached each of these four states.

Results of the batch are available for download at the results_url property on the Message Batch, and if the organization permission allows, in the Console. Because of the potentially large size of the results, it’s recommended to stream results back rather than download them all at once.

#!/bin/sh
curl "https://api.anthropic.com/v1/messages/batches/msgbatch_01HkcTjaV5uDC8jWR4ZsDV8d" \
  --header "anthropic-version: 2023-06-01" \
  --header "x-api-key: $ANTHROPIC_API_KEY" \
| grep -o '"results_url":[[:space:]]*"[^"]*"' \
| cut -d'"' -f4 \
| while read -r url; do
    curl -s "$url" \
      --header "anthropic-version: 2023-06-01" \
      --header "x-api-key: $ANTHROPIC_API_KEY" \
| sed 's/}{/}\n{/g' \
| while IFS= read -r line
    do
      result_type=$(echo "$line" | sed -n 's/.*"result":[[:space:]]*{[[:space:]]*"type":[[:space:]]*"\([^"]*\)".*/\1/p')
      custom_id=$(echo "$line" | sed -n 's/.*"custom_id":[[:space:]]*"\([^"]*\)".*/\1/p')
      error_type=$(echo "$line" | sed -n 's/.*"error":[[:space:]]*{[[:space:]]*"type":[[:space:]]*"\([^"]*\)".*/\1/p')

      case "$result_type" in
        "succeeded")
          echo "Success! $custom_id"
          ;;
        "errored")
          if [ "$error_type" = "invalid_request" ]; then
            # Request body must be fixed before re-sending request
            echo "Validation error: $custom_id"
          else
            # Request can be retried directly
            echo "Server error: $custom_id"
          fi
          ;;
        "expired")
          echo "Expired: $line"
          ;;
      esac
    done
  done

import anthropic

client = anthropic.Anthropic()

# Stream results file in memory-efficient chunks, processing one at a time
for result in client.messages.batches.results(
    "msgbatch_01HkcTjaV5uDC8jWR4ZsDV8d",
):
    match result.result.type:
        case "succeeded":
            print(f"Success! {result.custom_id}")
        case "errored":
            if result.result.error.type == "invalid_request":
                # Request body must be fixed before re-sending request
                print(f"Validation error {result.custom_id}")
            else:
                # Request can be retried directly
                print(f"Server error {result.custom_id}")
        case "expired":
            print(f"Request expired {result.custom_id}")


const anthropic = new Anthropic();

// Stream results file in memory-efficient chunks, processing one at a time
for await (const result of await anthropic.messages.batches.results(
  "msgbatch_01HkcTjaV5uDC8jWR4ZsDV8d"
)) {
  switch (result.result.type) {
    case "succeeded":
      console.log(`Success! ${result.custom_id}`);
      break;
    case "errored":
      if (result.result.error.type == "invalid_request") {
        // Request body must be fixed before re-sending request
        console.log(`Validation error: ${result.custom_id}`);
      } else {
        // Request can be retried directly
        console.log(`Server error: ${result.custom_id}`);
      }
      break;
    case "expired":
      console.log(`Request expired: ${result.custom_id}`);
      break;
  }
}

import com.anthropic.client.AnthropicClient;
import com.anthropic.client.okhttp.AnthropicOkHttpClient;
import com.anthropic.core.http.StreamResponse;
import com.anthropic.models.messages.batches.BatchResultsParams;
import com.anthropic.models.messages.batches.MessageBatchIndividualResponse;

public class BatchResultsExample {

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

    // Stream results file in memory-efficient chunks, processing one at a time
    try (
      StreamResponse<MessageBatchIndividualResponse> streamResponse = client
        .messages()
        .batches()
        .resultsStreaming(
          BatchResultsParams.builder()
            .messageBatchId("msgbatch_01HkcTjaV5uDC8jWR4ZsDV8d")
            .build()
        )
    ) {
      streamResponse
        .stream()
        .forEach(result -> {
          if (result.result().isSucceeded()) {
            System.out.println("Success! " + result.customId());
          } else if (result.result().isErrored()) {
            if (result.result().asErrored().error().error().isInvalidRequestError()) {
              // Request body must be fixed before re-sending request
              System.out.println("Validation error: " + result.customId());
            } else {
              // Request can be retried directly
              System.out.println("Server error: " + result.customId());
            }
          } else if (result.result().isExpired()) {
            System.out.println("Request expired: " + result.customId());
          }
        });
    }
  }
}

The results will be in .jsonl format, where each line is a valid JSON object representing the result of a single request in the Message Batch. For each streamed result, you can do something different depending on its custom_id and result type. Here is an example set of results:

{"custom_id":"my-second-request","result":{"type":"succeeded","message":{"id":"msg_014VwiXbi91y3JMjcpyGBHX5","type":"message","role":"assistant","model":"claude-opus-4-6","content":[{"type":"text","text":"Hello again! It's nice to see you. How can I assist you today? Is there anything specific you'd like to chat about or any questions you have?"}],"stop_reason":"end_turn","stop_sequence":null,"usage":{"input_tokens":11,"output_tokens":36}}}}
{"custom_id":"my-first-request","result":{"type":"succeeded","message":{"id":"msg_01FqfsLoHwgeFbguDgpz48m7","type":"message","role":"assistant","model":"claude-opus-4-6","content":[{"type":"text","text":"Hello! How can I assist you today? Feel free to ask me any questions or let me know if there's anything you'd like to chat about."}],"stop_reason":"end_turn","stop_sequence":null,"usage":{"input_tokens":10,"output_tokens":34}}}}

If your result has an error, its result.error will be set to the standard error shape.

Batch results may not match input order

Batch results can be returned in any order, and may not match the ordering of requests when the batch was created. In the above example, the result for the second batch request is returned before the first. To correctly match results with their corresponding requests, always use the custom_id field.

Canceling a Message Batch#

You can cancel a Message Batch that is currently processing using the cancel endpoint. Immediately after cancellation, a batch’s processing_status will be canceling. You can use the same polling technique described above to wait until cancellation is finalized. Canceled batches end up with a status of ended and may contain partial results for requests that were processed before cancellation.

import anthropic

client = anthropic.Anthropic()

message_batch = client.messages.batches.cancel(
    MESSAGE_BATCH_ID,
)
print(message_batch)


const anthropic = new Anthropic();

const messageBatch = await anthropic.messages.batches.cancel(MESSAGE_BATCH_ID);
console.log(messageBatch);

#!/bin/sh
curl --request POST https://api.anthropic.com/v1/messages/batches/$MESSAGE_BATCH_ID/cancel \
    --header "x-api-key: $ANTHROPIC_API_KEY" \
    --header "anthropic-version: 2023-06-01"

import com.anthropic.client.AnthropicClient;
import com.anthropic.client.okhttp.AnthropicOkHttpClient;
import com.anthropic.models.messages.batches.*;

public class BatchCancelExample {

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

    MessageBatch messageBatch = client
      .messages()
      .batches()
      .cancel(BatchCancelParams.builder().messageBatchId(MESSAGE_BATCH_ID).build());
    System.out.println(messageBatch);
  }
}

The response will show the batch in a canceling state:

{
  "id": "msgbatch_013Zva2CMHLNnXjNJJKqJ2EF",
  "type": "message_batch",
  "processing_status": "canceling",
  "request_counts": {
    "processing": 2,
    "succeeded": 0,
    "errored": 0,
    "canceled": 0,
    "expired": 0
  },
  "ended_at": null,
  "created_at": "2024-09-24T18:37:24.100435Z",
  "expires_at": "2024-09-25T18:37:24.100435Z",
  "cancel_initiated_at": "2024-09-24T18:39:03.114875Z",
  "results_url": null
}

Using prompt caching with Message Batches#

The Message Batches API supports prompt caching, allowing you to potentially reduce costs and processing time for batch requests. The pricing discounts from prompt caching and Message Batches can stack, providing even greater cost savings when both features are used together. However, since batch requests are processed asynchronously and concurrently, cache hits are provided on a best-effort basis. Users typically experience cache hit rates ranging from 30% to 98%, depending on their traffic patterns.

To maximize the likelihood of cache hits in your batch requests:

Include identical cache_control blocks in every Message request within your batch
Maintain a steady stream of requests to prevent cache entries from expiring after their 5-minute lifetime
Structure your requests to share as much cached content as possible

Example of implementing prompt caching in a batch:

curl https://api.anthropic.com/v1/messages/batches \
     --header "x-api-key: $ANTHROPIC_API_KEY" \
     --header "anthropic-version: 2023-06-01" \
     --header "content-type: application/json" \
     --data \
'{
    "requests": [
        {
            "custom_id": "my-first-request",
            "params": {
                "model": "claude-opus-4-6",
                "max_tokens": 1024,
                "system": [
                    {
                        "type": "text",
                        "text": "You are an AI assistant tasked with analyzing literary works. Your goal is to provide insightful commentary on themes, characters, and writing style.\n"
                    },
                    {
                        "type": "text",
                        "text": "<the entire contents of Pride and Prejudice>",
                        "cache_control": {"type": "ephemeral"}
                    }
                ],
                "messages": [
                    {"role": "user", "content": "Analyze the major themes in Pride and Prejudice."}
                ]
            }
        },
        {
            "custom_id": "my-second-request",
            "params": {
                "model": "claude-opus-4-6",
                "max_tokens": 1024,
                "system": [
                    {
                        "type": "text",
                        "text": "You are an AI assistant tasked with analyzing literary works. Your goal is to provide insightful commentary on themes, characters, and writing style.\n"
                    },
                    {
                        "type": "text",
                        "text": "<the entire contents of Pride and Prejudice>",
                        "cache_control": {"type": "ephemeral"}
                    }
                ],
                "messages": [
                    {"role": "user", "content": "Write a summary of Pride and Prejudice."}
                ]
            }
        }
    ]
}'

import anthropic
from anthropic.types.message_create_params import MessageCreateParamsNonStreaming
from anthropic.types.messages.batch_create_params import Request

client = anthropic.Anthropic()

message_batch = client.messages.batches.create(
    requests=[
        Request(
            custom_id="my-first-request",
            params=MessageCreateParamsNonStreaming(
                model="claude-opus-4-6",
                max_tokens=1024,
                system=[
                    {
                        "type": "text",
                        "text": "You are an AI assistant tasked with analyzing literary works. Your goal is to provide insightful commentary on themes, characters, and writing style.\n",
                    },
                    {
                        "type": "text",
                        "text": "<the entire contents of Pride and Prejudice>",
                        "cache_control": {"type": "ephemeral"},
                    },
                ],
                messages=[
                    {
                        "role": "user",
                        "content": "Analyze the major themes in Pride and Prejudice.",
                    }
                ],
            ),
        ),
        Request(
            custom_id="my-second-request",
            params=MessageCreateParamsNonStreaming(
                model="claude-opus-4-6",
                max_tokens=1024,
                system=[
                    {
                        "type": "text",
                        "text": "You are an AI assistant tasked with analyzing literary works. Your goal is to provide insightful commentary on themes, characters, and writing style.\n",
                    },
                    {
                        "type": "text",
                        "text": "<the entire contents of Pride and Prejudice>",
                        "cache_control": {"type": "ephemeral"},
                    },
                ],
                messages=[
                    {
                        "role": "user",
                        "content": "Write a summary of Pride and Prejudice.",
                    }
                ],
            ),
        ),
    ]
)


const anthropic = new Anthropic();

const messageBatch = await anthropic.messages.batches.create({
  requests: [
    {
      custom_id: "my-first-request",
      params: {
        model: "claude-opus-4-6",
        max_tokens: 1024,
        system: [
          {
            type: "text",
            text: "You are an AI assistant tasked with analyzing literary works. Your goal is to provide insightful commentary on themes, characters, and writing style.\n"
          },
          {
            type: "text",
            text: "<the entire contents of Pride and Prejudice>",
            cache_control: { type: "ephemeral" }
          }
        ],
        messages: [
          { role: "user", content: "Analyze the major themes in Pride and Prejudice." }
        ]
      }
    },
    {
      custom_id: "my-second-request",
      params: {
        model: "claude-opus-4-6",
        max_tokens: 1024,
        system: [
          {
            type: "text",
            text: "You are an AI assistant tasked with analyzing literary works. Your goal is to provide insightful commentary on themes, characters, and writing style.\n"
          },
          {
            type: "text",
            text: "<the entire contents of Pride and Prejudice>",
            cache_control: { type: "ephemeral" }
          }
        ],
        messages: [{ role: "user", content: "Write a summary of Pride and Prejudice." }]
      }
    }
  ]
});

import com.anthropic.client.AnthropicClient;
import com.anthropic.client.okhttp.AnthropicOkHttpClient;
import com.anthropic.models.messages.CacheControlEphemeral;
import com.anthropic.models.messages.Model;
import com.anthropic.models.messages.TextBlockParam;
import com.anthropic.models.messages.batches.*;
import java.util.List;

public class BatchExample {

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

    BatchCreateParams createParams = BatchCreateParams.builder()
      .addRequest(
        BatchCreateParams.Request.builder()
          .customId("my-first-request")
          .params(
            BatchCreateParams.Request.Params.builder()
              .model(Model.CLAUDE_OPUS_4_6)
              .maxTokens(1024)
              .systemOfTextBlockParams(
                List.of(
                  TextBlockParam.builder()
                    .text(
                      "You are an AI assistant tasked with analyzing literary works. Your goal is to provide insightful commentary on themes, characters, and writing style.\n"
                    )
                    .build(),
                  TextBlockParam.builder()
                    .text("<the entire contents of Pride and Prejudice>")
                    .cacheControl(CacheControlEphemeral.builder().build())
                    .build()
                )
              )
              .addUserMessage("Analyze the major themes in Pride and Prejudice.")
              .build()
          )
          .build()
      )
      .addRequest(
        BatchCreateParams.Request.builder()
          .customId("my-second-request")
          .params(
            BatchCreateParams.Request.Params.builder()
              .model(Model.CLAUDE_OPUS_4_6)
              .maxTokens(1024)
              .systemOfTextBlockParams(
                List.of(
                  TextBlockParam.builder()
                    .text(
                      "You are an AI assistant tasked with analyzing literary works. Your goal is to provide insightful commentary on themes, characters, and writing style.\n"
                    )
                    .build(),
                  TextBlockParam.builder()
                    .text("<the entire contents of Pride and Prejudice>")
                    .cacheControl(CacheControlEphemeral.builder().build())
                    .build()
                )
              )
              .addUserMessage("Write a summary of Pride and Prejudice.")
              .build()
          )
          .build()
      )
      .build();

    MessageBatch messageBatch = client.messages().batches().create(createParams);
  }
}

In this example, both requests in the batch include identical system messages and the full text of Pride and Prejudice marked with cache_control to increase the likelihood of cache hits.

Best practices for effective batching#

To get the most out of the Batches API:

Monitor batch processing status regularly and implement appropriate retry logic for failed requests.
Use meaningful custom_id values to easily match results with requests, since order is not guaranteed.
Consider breaking very large datasets into multiple batches for better manageability.
Dry run a single request shape with the Messages API to avoid validation errors.

Troubleshooting common issues#

If experiencing unexpected behavior:

Verify that the total batch request size doesn’t exceed 256 MB. If the request size is too large, you may get a 413 request_too_large error.
Check that you’re using supported models for all requests in the batch.
Ensure each request in the batch has a unique custom_id.
Ensure that it has been less than 29 days since batch created_at (not processing ended_at) time. If over 29 days have passed, results will no longer be viewable.
Confirm that the batch has not been canceled.

Note that the failure of one request in a batch does not affect the processing of other requests.

Batch storage and privacy#

Workspace isolation: Batches are isolated within the Workspace they are created in. They can only be accessed by API keys associated with that Workspace, or users with permission to view Workspace batches in the Console.
Result availability: Batch results are available for 29 days after the batch is created, allowing ample time for retrieval and processing.

FAQ#

How long does it take for a batch to process?

Batches may take up to 24 hours for processing, but many will finish sooner. Actual processing time depends on the size of the batch, current demand, and your request volume. It is possible for a batch to expire and not complete within 24 hours.

Is the Batches API available for all models?

See above for the list of supported models.

Can I use the Message Batches API with other API features?

Yes, the Message Batches API supports all features available in the Messages API, including beta features. However, streaming is not supported for batch requests.

How does the Message Batches API affect pricing?

The Message Batches API offers a 50% discount on all usage compared to standard API prices. This applies to input tokens, output tokens, and any special tokens. For more on pricing, visit the pricing page.

Can I update a batch after it's been submitted?

No, once a batch has been submitted, it cannot be modified. If you need to make changes, you should cancel the current batch and submit a new one. Note that cancellation may not take immediate effect.

Are there Message Batches API rate limits and do they interact with the Messages API rate limits?

The Message Batches API has HTTP requests-based rate limits in addition to limits on the number of requests in need of processing. See Message Batches API rate limits. Usage of the Batches API does not affect rate limits in the Messages API.

How do I handle errors in my batch requests?

When you retrieve the results, each request will have a result field indicating whether it succeeded, errored, was canceled, or expired. For errored results, additional error information will be provided. View the error response object in the API reference.

How does the Message Batches API handle privacy and data separation?

The Message Batches API is designed with strong privacy and data separation measures:

Batches and their results are isolated within the Workspace in which they were created. This means they can only be accessed by API keys from that same Workspace.
Each request within a batch is processed independently, with no data leakage between requests.
Results are only available for a limited time (29 days), and follow Anthropic’s data retention policy.
Downloading batch results in the Console can be disabled on the organization-level or on a per-workspace basis.

Can I use prompt caching in the Message Batches API?

Yes, it is possible to use prompt caching with Message Batches API. However, because asynchronous batch requests can be processed concurrently and in any order, cache hits are provided on a best-effort basis.

Link last verified June 7, 2026. View original ↗

Source: Anthropic Platform Docs

Link last verified: 2026-02-26