What is the OpenAI API rate limit, and how does it work?

The OpenAI API rate limit controls how many requests you can make to the API within a specific timeframe, preventing abuse and ensuring reliable service for all users. Rate limits are applied in two ways: requests per minute (RPM) and tokens per minute (TPM). RPM restricts the number of API calls you can send each minute, while TPM limits the total tokens (units of text) processed across all requests in a minute. For example, the default tier for GPT-4 might allow 3,500 RPM and 90,000 TPM. These limits vary based on your account type, usage history, and the specific model you’re using. Tokens are counted for both input and output, so a request with a 1,000-token prompt and a 500-token response consumes 1,500 tokens toward your TPM limit.

Rate limits are enforced at the organization or project level, depending on how your API key is configured. If you exceed the limit, the API returns a 429 Too Many Requests error, and you’ll need to wait or adjust your request rate. For example, if your app sends 100 requests in 10 seconds to GPT-3.5, which has a 3,500 RPM limit, you might hit the cap quickly unless you space out requests. Tokens-per-minute limits also require balancing—a single large request (e.g., summarizing a long document) could consume most of your TPM, leaving little room for other tasks. To avoid errors, developers must track both RPM and TPM usage, often by checking the x-ratelimit-remaining-requests and x-ratelimit-remaining-tokens headers in API responses.

OpenAI allows users to request higher rate limits via their support team, but approval depends on factors like historical usage and safety compliance. To optimize within existing limits, developers can implement strategies like request queuing, caching frequent responses, or breaking large tasks into smaller chunks. For instance, instead of processing 10,000 tokens in one call, split it into ten 1,000-token requests spaced across a minute. Tools like exponential backoff (retrying failed requests with increasing delays) also help manage transient rate limit errors. Monitoring usage via OpenAI’s dashboard or custom logging ensures you stay within bounds and adjust workflows as needed.