Rate limits

A rate limit is a restriction that an API imposes on the number of times a user or client can access the server within a specified period of time.

Rate limits are a common practice for APIs, and they're put in place for a few different reasons:

• They help protect against abuse or misuse of the API. For example, a malicious actor could flood the API with requests in an attempt to overload it or cause disruptions in service. By setting rate limits, OpenAI can prevent this kind of activity.
• Rate limits help ensure that everyone has fair access to the API. If one person or organization makes an excessive number of requests, it could bog down the API for everyone else. By throttling the number of requests that a single user can make, OpenAI ensures that the most number of people have an opportunity to use the API without experiencing slowdowns.
• Rate limits can help OpenAI manage the aggregate load on its infrastructure. If requests to the API increase dramatically, it could tax the servers and cause performance issues. By setting rate limits, OpenAI can help maintain a smooth and consistent experience for all users.

You can view the rate limits for your organization under the rate limits section of the account management page.

We enforce rate limits at the organization level, not user level, based on the specific endpoint used as well as the type of account you have. Rate limits are measured in three ways: RPM (requests per minute), RPD (requests per day), and TPM (tokens per minute). The table below highlights the default rate limits for our API.

It is important to note that the rate limit can be hit by either option depending on what occurs first. For example, you might send 20 requests with only 100 tokens to the Edit endpoint and that would fill your limit, even if you did not send 150k tokens within those 20 requests.

During the rollout of GPT-4, the model will have more aggressive rate limits to keep up with demand. You can view your current rate limits in the rate limits section of the account page. We are unable to accommodate requests for rate limit increases due to capacity constraints. We are prioritizing general access to GPT-4 first and will subsequently raise rate limits automatically as capacity allows.

If your rate limit is 60 requests per minute and 150k tokens per minute, you’ll be limited either by reaching the requests/min cap or running out of tokens—whichever happens first. For example, if your max requests/min is 60, you should be able to send 1 request per second. If you send 1 request every 800ms, once you hit your rate limit, you’d only need to make your program sleep 200ms in order to send one more request otherwise subsequent requests would fail. With the default of 3,000 requests/min, customers can effectively send 1 request every 20ms, or every .02 seconds.

In addition to seeing your rate limit on your account page, you can also view important information about your rate limits such as the remaining requests, tokens, and other metadata in the headers of the HTTP response.

You can expect to see the following header fields:

Rate limit errors look like this:

Rate limit reached for default-text-davinci-002 in organization org-{id} on requests per min. Limit: 20.000000 / min. Current: 24.000000 / min.

If you hit a rate limit, it means you've made too many requests in a short period of time, and the API is refusing to fulfill further requests until a specified amount of time has passed.

Each model we offer has a limited number of tokens that can be passed in as input when making a request. You cannot increase the maximum number of tokens a model takes in. For example, if you are using text-ada-001, the maximum number of tokens you can send to this model is 2,048 tokens per request.

The OpenAI Cookbook has a Python notebook that explains how to avoid rate limit errors, as well an example Python script for staying under rate limits while batch processing API requests.

You should also exercise caution when providing programmatic access, bulk processing features, and automated social media posting - consider only enabling these for trusted customers.

To protect against automated and high-volume misuse, set a usage limit for individual users within a specified time frame (daily, weekly, or monthly). Consider implementing a hard cap or a manual review process for users who exceed the limit.

One easy way to avoid rate limit errors is to automatically retry requests with a random exponential backoff. Retrying with exponential backoff means performing a short sleep when a rate limit error is hit, then retrying the unsuccessful request. If the request is still unsuccessful, the sleep length is increased and the process is repeated. This continues until the request is successful or until a maximum number of retries is reached. This approach has many benefits:

• Automatic retries means you can recover from rate limit errors without crashes or missing data
• Exponential backoff means that your first retries can be tried quickly, while still benefiting from longer delays if your first few retries fail
• Adding random jitter to the delay helps retries from all hitting at the same time.

Note that unsuccessful requests contribute to your per-minute limit, so continuously resending a request won’t work.

Below are a few example solutions for Python that use exponential backoff.

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import openai
from tenacity import (
    retry,
    stop_after_attempt,
    wait_random_exponential,
)  # for exponential backoff
 
@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
def completion_with_backoff(**kwargs):
    return openai.Completion.create(**kwargs)
 
completion_with_backoff(model="gpt-3.5-turbo-instruct", prompt="Once upon a time,")

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import backoff 
import openai 
@backoff.on_exception(backoff.expo, openai.error.RateLimitError)
def completions_with_backoff(**kwargs):
    return openai.Completion.create(**kwargs)
 
completions_with_backoff(model="gpt-3.5-turbo-instruct", prompt="Once upon a time,")

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# imports
import random
import time
 
import openai
 
# define a retry decorator
def retry_with_exponential_backoff(
    func,
    initial_delay: float = 1,
    exponential_base: float = 2,
    jitter: bool = True,
    max_retries: int = 10,
    errors: tuple = (openai.error.RateLimitError,),
):
    """Retry a function with exponential backoff."""
 
    def wrapper(*args, **kwargs):
        # Initialize variables
        num_retries = 0
        delay = initial_delay
 
        # Loop until a successful response or max_retries is hit or an exception is raised
        while True:
            try:
                return func(*args, **kwargs)
 
            # Retry on specific errors
            except errors as e:
                # Increment retries
                num_retries += 1
 
                # Check if max retries has been reached
                if num_retries > max_retries:
                    raise Exception(
                        f"Maximum number of retries ({max_retries}) exceeded."
                    )
 
                # Increment the delay
                delay *= exponential_base * (1 + jitter * random.random())
 
                # Sleep for the delay
                time.sleep(delay)
 
            # Raise exceptions for any errors not specified
            except Exception as e:
                raise e
 
    return wrapper
    
@retry_with_exponential_backoff
def completions_with_backoff(**kwargs):
    return openai.Completion.create(**kwargs)

Your rate limit is calculated as the maximum of max_tokens and the estimated number of tokens based on the character count of your request. Try to set the max_tokens value as close to your expected response size as possible.

The OpenAI API has separate limits for requests per minute and tokens per minute.

If you're hitting the limit on requests per minute, but have available capacity on tokens per minute, you can increase your throughput by batching multiple tasks into each request. This will allow you to process more tokens per minute, especially with our smaller models.

Sending in a batch of prompts works exactly the same as a normal API call, except you pass in a list of strings to the prompt parameter instead of a single string.

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import openai
 
num_stories = 10
prompt = "Once upon a time,"
 
# serial example, with one story completion per request
for _ in range(num_stories):
    response = openai.Completion.create(
        model="curie",
        prompt=prompt,
        max_tokens=20,
    )
    # print story
    print(prompt + response.choices[0].text)

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import openai  # for making OpenAI API requests
 
 
num_stories = 10
prompts = ["Once upon a time,"] * num_stories
 
# batched example, with 10 story completions per request
response = openai.Completion.create(
    model="curie",
    prompt=prompts,
    max_tokens=20,
)
 
# match completions to prompts by index
stories = [""] * len(prompts)
for choice in response.choices:
    stories[choice.index] = prompts[choice.index] + choice.text
 
# print stories
for story in stories:
    print(story)

Our default rate limits help us maximize stability and prevent abuse of our API. We increase limits to enable high-traffic applications, so the best time to apply for a rate limit increase is when you feel that you have the necessary traffic data to support a strong case for increasing the rate limit. Large rate limit increase requests without supporting data are not likely to be approved. If you're gearing up for a product launch, please obtain the relevant data through a phased release over 10 days.

Keep in mind that rate limit increases can sometimes take 7-10 days so it makes sense to try and plan ahead and submit early if there is data to support you will reach your rate limit given your current growth numbers.

A rate limit increase request is most often rejected because it lacks the data needed to justify the increase. We have provided numerical examples below that show how to best support a rate limit increase request and try our best to approve all requests that align with our safety policy and show supporting data. We are committed to enabling developers to scale and be successful with our API.

We understand the frustration that limited rate limits can cause, and we would love to raise the defaults for everyone. However, due to shared capacity constraints, we can only approve rate limit increases for paid customers who have demonstrated a need through our Rate Limit Increase Request form. To help us evaluate your needs properly, we ask that you please provide statistics on your current usage or projections based on historic user activity in the 'Share evidence of need' section of the form. If this information is not available, we recommend a phased release approach. Start by releasing the service to a subset of users at your current rate limits, gather usage data for 10 business days, and then submit a formal rate limit increase request based on that data for our review and approval.

We will review your request and if it is approved, we will notify you of the approval within a period of 7-10 business days.

Here are some examples of how you might fill out this form: