is there an API to check my API usage limits on my openAI account, so that I dont need to go to the website to check my API usage?
#is there an API to check my API usage limits on my openAI account?
thin zodiac
twin craterBOT
Hi @thin zodiac. I'm kapa.ai, your friendly support bot. I will try to answer your question by searching through the knowledge base. :wave:
I'm sorry, but based on the provided knowledge sources, there doesn't appear to be a direct API endpoint to check your API usage limits on your OpenAI account. However, you can check your token usage through the API. Token usage information is included in responses from OpenAI's endpoints under the usage key. Here's an example:
{
"id": "chatcmpl-abc123",
"object": "chat.completion",
"created": 1677858242,
"model": "gpt-3.5-turbo",
"usage": {
"prompt_tokens": 13,
"completion_tokens": 7,
"total_tokens": 20
},
"choices": [
{ "message": { "role": "assistant", "content": "\n\nThis is a test!" }
... ]
...
}
For a more comprehensive view of your usage, you would need to visit the usage dashboard (https://beta.openai.com/account/usage) or the limits page (https://platform.openai.com/account/limits) in your account settings on the OpenAI platform.
Please note that all API usage is subject to rate limits, and if you exceed your current quota, you may receive a 429 error. If you encounter this error, you can check your current usage and compare it to your account's limits, and consider upgrading to a paid plan for higher limits if necessary.
Source (https://help.openai.com/en/articles/6614209-how-do-i-check-my-token-usage)
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
thin zodiac
@twin crater in python how do i check my token usage?
twin craterBOT
Let me check the knowledge base for you. One moment! :books:
In Python, you can check your token usage by examining the usage field in the API response. Here's an example:
response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=example_messages,
temperature=0,
max_tokens=1
)
print(f'{response.usage.prompt_tokens} prompt tokens used by the API call.')
print(f'{response.usage.completion_tokens} completion tokens used by the API call.')
print(f'{response.usage.total_tokens} total tokens used by the API call.')
In this example, client is an instance of the OpenAI API client, and example_messages is a list of messages that you're sending to the API. The usage field in the response contains the number of prompt tokens, completion tokens, and total tokens used by the API call.
Please note that you need to replace example_messages with your actual messages.
Source (https://platform.openai.com/docs/guides/text-generation)
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
thin zodiac
@twin crater what model of gpt4 is the cheapest for making text
twin craterBOT
Let me check the knowledge base for you. One moment! :books:
The cost of using GPT-4 models depends on the context length of the model. According to the provided knowledge sources, the pricing for GPT-4 models is as follows:
- For models with 128k context lengths (e.g.
gpt-4-1106-previewandgpt-4-1106-vision-preview), the price is $10.00 / 1 million prompt tokens (or $0.01 / 1K prompt tokens) and $30.00 / 1 million sampled tokens (or $0.03 / 1K sampled tokens). - For models with 8k context lengths (e.g.
gpt-4andgpt-4-0314), the price is $30.00 / 1 million prompt tokens (or $0.03 / 1K prompt tokens) and $60.00 / 1 million sampled tokens (or $0.06 / 1K sampled tokens). - For models with 32k context lengths (e.g.
gpt-4-32kandgpt-4-32k-0314), the price is $60.00 / 1 million prompt tokens (or $0.06 / 1K prompt tokens) and $120.00 / 1 million sampled tokens (or $0.12 / 1K sampled tokens).
Based on this information, the cheapest GPT-4 model for generating text would be the ones with 128k context lengths, such as gpt-4-1106-preview and gpt-4-1106-vision-preview.
Please note that the actual cost will depend on the number of tokens used in your API calls. You can manage your costs by reducing the number of tokens used, for example, by using shorter prompts or fine-tuning models.