litellm/cookbook/LiteLLM_Completion_Cost.ipynb

Use LiteLLM to calculate costs for all your completion calls

In this notebook we'll use litellm.completion_cost to get completion costs

In [ ]:

!pip install litellm==0.1.549 # use 0.1.549  or later

Calculating costs for gpt-3.5 turbo completion()

In [6]:

from litellm import completion, completion_cost
import os
os.environ['OPENAI_API_KEY'] = ""

messages = [{ "content": "Hello, how are you?","role": "user"}]
response = completion(
            model="gpt-3.5-turbo",
            messages=messages,
)

print(response)

cost = completion_cost(completion_response=response)
formatted_string = f"Cost for completion call: ${float(cost):.10f}"
print(formatted_string)

got response
{
  "id": "chatcmpl-7vyCApIZaCxP36kb9meUMN2DFSJPh",
  "object": "chat.completion",
  "created": 1694050442,
  "model": "gpt-3.5-turbo-0613",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Hello! I'm an AI and I don't have feelings, but I'm here to help you. How can I assist you today?"
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 13,
    "completion_tokens": 28,
    "total_tokens": 41
  }
}
Cost for completion call: $0.0000755000

Calculating costs for Together Computer completion()

In [7]:

from litellm import completion, completion_cost
import os
os.environ['TOGETHERAI_API_KEY'] = ""

messages = [{ "content": "Hello, how are you?","role": "user"}]
response = completion(
            model="togethercomputer/llama-2-70b-chat",
            messages=messages,
)

print(response)

cost = completion_cost(completion_response=response)
formatted_string = f"Cost for completion call: ${float(cost):.10f}"
print(formatted_string)

{
  "choices": [
    {
      "finish_reason": "stop",
      "index": 0,
      "message": {
        "content": "Hello! I'm doing well, thanks for asking. I hope you're having a great",
        "role": "assistant",
        "logprobs": null
      }
    }
  ],
  "created": 1694050771.2821715,
  "model": "togethercomputer/llama-2-70b-chat",
  "usage": {
    "prompt_tokens": 12,
    "completion_tokens": 18,
    "total_tokens": 30
  }
}
Cost for completion call: $0.0000900000

Calculating costs for Replicate Llama2 completion()

In [8]:

from litellm import completion, completion_cost
import os
os.environ['REPLICATE_API_KEY'] = ""

messages = [{ "content": "Hello, how are you?","role": "user"}]
response = completion(
            model="replicate/llama-2-70b-chat:2796ee9483c3fd7aa2e171d38f4ca12251a30609463dcfd4cd76703f22e96cdf",
            messages=messages,
)

print(response)

cost = completion_cost(completion_response=response)
formatted_string = f"Cost for completion call: ${float(cost):.10f}"
print(formatted_string)

{
  "choices": [
    {
      "finish_reason": "stop",
      "index": 0,
      "message": {
        "content": " Hello! I'm doing well, thanks for asking. How about you? Is there anything you need help with today?",
        "role": "assistant",
        "logprobs": null
      }
    }
  ],
  "created": 1694050893.4534576,
  "model": "replicate/llama-2-70b-chat:2796ee9483c3fd7aa2e171d38f4ca12251a30609463dcfd4cd76703f22e96cdf",
  "usage": {
    "prompt_tokens": 6,
    "completion_tokens": 24,
    "total_tokens": 30
  },
  "ended": 1694050896.6689413
}
total_replicate_run_time 3.2154836654663086
Cost for completion call: $0.0045016771