# Batch API

Process jobs asynchronously with 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](https://platform.openai.com/docs/api-reference/batch).

## 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](https://platform.openai.com/docs/guides/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](/settings/organization/limits) 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](https://platform.openai.com/docs/api-reference/responses)),
`/v1/chat/completions`
([Chat Completions API](https://platform.openai.com/docs/api-reference/chat)),
`/v1/embeddings`
([Embeddings API](https://platform.openai.com/docs/api-reference/embeddings)),
and `/v1/completions`
([Completions API](https://platform.openai.com/docs/api-reference/completions)).
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.

```jsonl
{"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}}
```

### 2\. Upload your batch input file

Similar to our
[Fine-tuning API](https://platform.openai.com/docs/guides/model-optimization),
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](https://platform.openai.com/docs/api-reference/files).

```javascript
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);
```

```python
from openai import OpenAI
client = OpenAI()

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

print(batch_input_file)
```

```bash
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.

```javascript
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);
```

```python
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"
    }
)
```

```bash
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](https://platform.openai.com/docs/api-reference/batch/object) with
metadata about your batch:

```python
{
  "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.

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

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

```python
from openai import OpenAI
client = OpenAI()

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

```bash
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:

| Status        | Description                                                                    |
| ------------- | ------------------------------------------------------------------------------ |
| `validating`  | the input file is being validated before the batch can begin                   |
| `failed`      | the input file has failed the validation process                               |
| `in_progress` | the input file was successfully validated and the batch is currently being run |
| `finalizing`  | the batch has completed and the results are being prepared                     |
| `completed`   | the batch has been completed and the results are ready                         |
| `expired`     | the batch was not able to be completed within the 24-hour time window          |
| `cancelling`  | the batch is being cancelled (may take up to 10 minutes)                       |
| `cancelled`   | the batch was cancelled                                                        |

### 5\. Retrieve the results

Once the batch is complete, you can download the output by making a request
against the [Files API](https://platform.openai.com/docs/api-reference/files)
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`

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

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

console.log(fileContents);
```

```python
from openai import OpenAI
client = OpenAI()

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

```bash
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`.

Note that the output line order **may not match** the input line order. Instead
of relying on order to process your results, use the custom_id field which will
be present in each line of your output file and allow you to map requests in
your input to results in your output.

```jsonl
{"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`.

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

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

```python
from openai import OpenAI
client = OpenAI()

client.batches.cancel("batch_abc123")
```

```bash
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.

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

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

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

```python
from openai import OpenAI
client = OpenAI()

client.batches.list(limit=10)
```

```bash
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](https://platform.openai.com/docs/models) 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 two new 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](/settings/organization/limits).

There are no limits for output tokens or number of submitted requests 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.

```jsonl
{"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."}}
```