Batch API

Learn how to use OpenAI’s Batch API to send asynchronous groups of requests with 50% lower costs, a separate pool of significantly higher rate limits, and a clear 24-hour turnaround time. The service is ideal for processing jobs that don’t require immediate responses. You can also explore the API reference directly here.

Overview

While some uses of the OpenAI Platform require you to send synchronous requests, there are many cases where requests do not need an immediate response or rate limits prevent you from executing a large number of queries quickly. Batch processing jobs are often helpful in use cases like:

1. Running evaluations
2. Classifying large datasets
3. Embedding content repositories

The Batch API offers a straightforward set of endpoints that allow you to collect a set of requests into a single file, kick off a batch processing job to execute these requests, query for the status of that batch while the underlying requests execute, and eventually retrieve the collected results when the batch is complete.

Compared to using standard endpoints directly, Batch API has:

1. Better cost efficiency: 50% cost discount compared to synchronous APIs
2. Higher rate limits: Substantially more headroom compared to the synchronous APIs
3. Fast completion times: Each batch completes within 24 hours (and often more quickly)

Getting started

1. Prepare your batch file

Batches start with a .jsonl file where each line contains the details of an individual request to the API. For now, the available endpoints are /v1/responses (Responses API), /v1/chat/completions (Chat Completions API), /v1/embeddings (Embeddings API), /v1/completions (Completions API), and /v1/moderations (Moderations guide). For a given input file, the parameters in each line’s body field are the same as the parameters for the underlying endpoint. Each request must include a unique custom_id value, which you can use to reference results after completion. Here’s an example of an input file with 2 requests. Note that each input file can only include requests to a single model.

When targeting /v1/moderations, include an input field in every request body. Batch accepts both plain-text inputs (for omni-moderation-latest and text-moderation-latest) and multimodal content arrays (for omni-moderation-latest). The Batch worker enforces the same non-streaming requirement as the synchronous Moderations API and rejects requests that set stream=true.

{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "gpt-3.5-turbo-0125", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 1000}}
{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "gpt-3.5-turbo-0125", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 1000}}

Moderations input examples

Text-only request:

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{
  "custom_id": "moderation-text-1",
  "method": "POST",
  "url": "/v1/moderations",
  "body": {
    "model": "omni-moderation-latest",
    "input": "This is a harmless test sentence."
  }
}

Multimodal request:

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{
  "custom_id": "moderation-mm-1",
  "method": "POST",
  "url": "/v1/moderations",
  "body": {
    "model": "omni-moderation-latest",
    "input": [
      {
        "type": "text",
        "text": "Describe this image"
      },
      {
        "type": "image_url",
        "image_url": {
          "url": "https://api.nga.gov/iiif/a2e6da57-3cd1-4235-b20e-95dcaefed6c8/full/!800,800/0/default.jpg"
        }
      }
    ]
  }
}

2. Upload your batch input file

Similar to our Fine-tuning API, you must first upload your input file so that you can reference it correctly when kicking off batches. Upload your .jsonl file using the Files API.

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import fs from "fs";
import OpenAI from "openai";
const openai = new OpenAI();

const file = await openai.files.create({
  file: fs.createReadStream("batchinput.jsonl"),
  purpose: "batch",
});

console.log(file);

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from openai import OpenAI
client = OpenAI()

batch_input_file = client.files.create(
    file=open("batchinput.jsonl", "rb"),
    purpose="batch"
)

print(batch_input_file)

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curl https://api.openai.com/v1/files \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -F purpose="batch" \
  -F file="@batchinput.jsonl"

3. Create the batch

Once you’ve successfully uploaded your input file, you can use the input File object’s ID to create a batch. In this case, let’s assume the file ID is file-abc123. For now, the completion window can only be set to 24h. You can also provide custom metadata via an optional metadata parameter.

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import OpenAI from "openai";
const openai = new OpenAI();

const batch = await openai.batches.create({
  input_file_id: "file-abc123",
  endpoint: "/v1/chat/completions",
  completion_window: "24h"
});

console.log(batch);

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from openai import OpenAI
client = OpenAI()

batch_input_file_id = batch_input_file.id
client.batches.create(
    input_file_id=batch_input_file_id,
    endpoint="/v1/chat/completions",
    completion_window="24h",
    metadata={
        "description": "nightly eval job"
    }
)

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curl https://api.openai.com/v1/batches \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "input_file_id": "file-abc123",
    "endpoint": "/v1/chat/completions",
    "completion_window": "24h"
  }'

This request will return a Batch object with metadata about your batch:

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{
  "id": "batch_abc123",
  "object": "batch",
  "endpoint": "/v1/chat/completions",
  "errors": null,
  "input_file_id": "file-abc123",
  "completion_window": "24h",
  "status": "validating",
  "output_file_id": null,
  "error_file_id": null,
  "created_at": 1714508499,
  "in_progress_at": null,
  "expires_at": 1714536634,
  "completed_at": null,
  "failed_at": null,
  "expired_at": null,
  "request_counts": {
    "total": 0,
    "completed": 0,
    "failed": 0
  },
  "metadata": null
}

4. Check the status of a batch

You can check the status of a batch at any time, which will also return a Batch object.

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import OpenAI from "openai";
const openai = new OpenAI();

const batch = await openai.batches.retrieve("batch_abc123");
console.log(batch);

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from openai import OpenAI
client = OpenAI()

batch = client.batches.retrieve("batch_abc123")
print(batch)

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curl https://api.openai.com/v1/batches/batch_abc123 \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -H "Content-Type: application/json"

The status of a given Batch object can be any of the following:

5. Retrieve the results

Once the batch is complete, you can download the output by making a request against the Files API via the output_file_id field from the Batch object and writing it to a file on your machine, in this case batch_output.jsonl

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import OpenAI from "openai";
const openai = new OpenAI();

const fileResponse = await openai.files.content("file-xyz123");
const fileContents = await fileResponse.text();

console.log(fileContents);

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from openai import OpenAI
client = OpenAI()

file_response = client.files.content("file-xyz123")
print(file_response.text)

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curl https://api.openai.com/v1/files/file-xyz123/content \
  -H "Authorization: Bearer $OPENAI_API_KEY" > batch_output.jsonl

The output .jsonl file will have one response line for every successful request line in the input file. Any failed requests in the batch will have their error information written to an error file that can be found via the batch’s error_file_id.

{"id": "batch_req_123", "custom_id": "request-2", "response": {"status_code": 200, "request_id": "req_123", "body": {"id": "chatcmpl-123", "object": "chat.completion", "created": 1711652795, "model": "gpt-3.5-turbo-0125", "choices": [{"index": 0, "message": {"role": "assistant", "content": "Hello."}, "logprobs": null, "finish_reason": "stop"}], "usage": {"prompt_tokens": 22, "completion_tokens": 2, "total_tokens": 24}, "system_fingerprint": "fp_123"}}, "error": null}
{"id": "batch_req_456", "custom_id": "request-1", "response": {"status_code": 200, "request_id": "req_789", "body": {"id": "chatcmpl-abc", "object": "chat.completion", "created": 1711652789, "model": "gpt-3.5-turbo-0125", "choices": [{"index": 0, "message": {"role": "assistant", "content": "Hello! How can I assist you today?"}, "logprobs": null, "finish_reason": "stop"}], "usage": {"prompt_tokens": 20, "completion_tokens": 9, "total_tokens": 29}, "system_fingerprint": "fp_3ba"}}, "error": null}

The output file will automatically be deleted 30 days after the batch is complete.

6. Cancel a batch

If necessary, you can cancel an ongoing batch. The batch’s status will change to cancelling until in-flight requests are complete (up to 10 minutes), after which the status will change to cancelled.

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import OpenAI from "openai";
const openai = new OpenAI();

const batch = await openai.batches.cancel("batch_abc123");
console.log(batch);

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from openai import OpenAI
client = OpenAI()

client.batches.cancel("batch_abc123")

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curl https://api.openai.com/v1/batches/batch_abc123/cancel \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -H "Content-Type: application/json" \
  -X POST

7. Get a list of all batches

At any time, you can see all your batches. For users with many batches, you can use the limit and after parameters to paginate your results.

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import OpenAI from "openai";
const openai = new OpenAI();

const list = await openai.batches.list();

for await (const batch of list) {
  console.log(batch);
}

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from openai import OpenAI
client = OpenAI()

client.batches.list(limit=10)

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curl https://api.openai.com/v1/batches?limit=10 \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -H "Content-Type: application/json"

Model availability

The Batch API is widely available across most of our models, but not all. Please refer to the model reference docs to ensure the model you’re using supports the Batch API.

Rate limits

Batch API rate limits are separate from existing per-model rate limits. The Batch API has three types of rate limits:

1. Per-batch limits: A single batch may include up to 50,000 requests, and a batch input file can be up to 200 MB in size. Note that /v1/embeddings batches are also restricted to a maximum of 50,000 embedding inputs across all requests in the batch.
2. Enqueued prompt tokens per model: Each model has a maximum number of enqueued prompt tokens allowed for batch processing. You can find these limits on the Platform Settings page.
3. Batch creation rate limit: You can create up to 2,000 batches per hour. If you need to submit more requests, increase the number of requests per batch.

There are no limits for output tokens for the Batch API today. Because Batch API rate limits are a new, separate pool, using the Batch API will not consume tokens from your standard per-model rate limits, thereby offering you a convenient way to increase the number of requests and processed tokens you can use when querying our API.

Batch expiration

Batches that do not complete in time eventually move to an expired state; unfinished requests within that batch are cancelled, and any responses to completed requests are made available via the batch’s output file. You will be charged for tokens consumed from any completed requests.

Expired requests will be written to your error file with the message as shown below. You can use the custom_id to retrieve the request data for expired requests.

{"id": "batch_req_123", "custom_id": "request-3", "response": null, "error": {"code": "batch_expired", "message": "This request could not be executed before the completion window expired."}}
{"id": "batch_req_123", "custom_id": "request-7", "response": null, "error": {"code": "batch_expired", "message": "This request could not be executed before the completion window expired."}}