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Completion Token Usage & Cost

By default LiteLLM returns token usage in all completion requests (See here)

However, we also expose some helper functions + [NEW] an API to calculate token usage across providers:

  • encode: This encodes the text passed in, using the model-specific tokenizer. Jump to code

  • decode: This decodes the tokens passed in, using the model-specific tokenizer. Jump to code

  • token_counter: This returns the number of tokens for a given input - it uses the tokenizer based on the model, and defaults to tiktoken if no model-specific tokenizer is available. Jump to code

  • create_pretrained_tokenizer and create_tokenizer: LiteLLM provides default tokenizer support for OpenAI, Cohere, Anthropic, Llama2, and Llama3 models. If you are using a different model, you can create a custom tokenizer and pass it as custom_tokenizer to the encode, decode, and token_counter methods. Jump to code

  • cost_per_token: This returns the cost (in USD) for prompt (input) and completion (output) tokens. Uses the live list from Jump to code

  • completion_cost: This returns the overall cost (in USD) for a given LLM API Call. It combines token_counter and cost_per_token to return the cost for that query (counting both cost of input and output). Jump to code

  • get_max_tokens: This returns the maximum number of tokens allowed for the given model. Jump to code

  • model_cost: This returns a dictionary for all models, with their max_tokens, input_cost_per_token and output_cost_per_token. It uses the call shown below. Jump to code

  • register_model: This registers new / overrides existing models (and their pricing details) in the model cost dictionary. Jump to code

  • Live token + price count across all supported models. Jump to code

πŸ“£ This is a community maintained list. Contributions are welcome! ❀️

Example Usage​

1. encode​

Encoding has model-specific tokenizers for anthropic, cohere, llama2 and openai. If an unsupported model is passed in, it'll default to using tiktoken (openai's tokenizer).

from litellm import encode, decode

sample_text = "HellΓΆ World, this is my input string!"
# openai encoding + decoding
openai_tokens = encode(model="gpt-3.5-turbo", text=sample_text)

2. decode​

Decoding is supported for anthropic, cohere, llama2 and openai.

from litellm import encode, decode

sample_text = "HellΓΆ World, this is my input string!"
# openai encoding + decoding
openai_tokens = encode(model="gpt-3.5-turbo", text=sample_text)
openai_text = decode(model="gpt-3.5-turbo", tokens=openai_tokens)

3. token_counter​

from litellm import token_counter

messages = [{"user": "role", "content": "Hey, how's it going"}]
print(token_counter(model="gpt-3.5-turbo", messages=messages))

4. create_pretrained_tokenizer and create_tokenizer​

from litellm import create_pretrained_tokenizer, create_tokenizer

# get tokenizer from huggingface repo
custom_tokenizer_1 = create_pretrained_tokenizer("Xenova/llama-3-tokenizer")

# use tokenizer from json file
with open("tokenizer.json") as f:
json_data = json.load(f)

json_str = json.dumps(json_data)

custom_tokenizer_2 = create_tokenizer(json_str)

5. cost_per_token​

from litellm import cost_per_token

prompt_tokens = 5
completion_tokens = 10
prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar = cost_per_token(model="gpt-3.5-turbo", prompt_tokens=prompt_tokens, completion_tokens=completion_tokens))

print(prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar)

6. completion_cost​

  • Input: Accepts a litellm.completion() response OR prompt + completion strings
  • Output: Returns a float of cost for the completion call


from litellm import completion, completion_cost

response = completion(
# pass your response from completion to completion_cost
cost = completion_cost(completion_response=response)
formatted_string = f"${float(cost):.10f}"

prompt + completion string

from litellm import completion_cost
cost = completion_cost(model="bedrock/anthropic.claude-v2", prompt="Hey!", completion="How's it going?")
formatted_string = f"${float(cost):.10f}"

7. get_max_tokens​

Input: Accepts a model name - e.g., gpt-3.5-turbo (to get a complete list, call litellm.model_list). Output: Returns the maximum number of tokens allowed for the given model

from litellm import get_max_tokens 

model = "gpt-3.5-turbo"

print(get_max_tokens(model)) # Output: 4097

8. model_cost​

  • Output: Returns a dict object containing the max_tokens, input_cost_per_token, output_cost_per_token for all models on community-maintained list
from litellm import model_cost 

print(model_cost) # {'gpt-3.5-turbo': {'max_tokens': 4000, 'input_cost_per_token': 1.5e-06, 'output_cost_per_token': 2e-06}, ...}

9. register_model​

  • Input: Provide EITHER a model cost dictionary or a url to a hosted json blob
  • Output: Returns updated model_cost dictionary + updates litellm.model_cost with model details.


from litellm import register_model

"gpt-4": {
"max_tokens": 8192,
"input_cost_per_token": 0.00002,
"output_cost_per_token": 0.00006,
"litellm_provider": "openai",
"mode": "chat"

URL for json blob

import litellm


Don't pull hosted model_cost_map
If you have firewalls, and want to just use the local copy of the model cost map, you can do so like this:


Note: this means you will need to upgrade to get updated pricing, and newer models.