---
title: Usage Pattern | LlamaIndex OSS Documentation
---

## Estimating LLM and Embedding Token Counts

In order to measure LLM and Embedding token counts, you’ll need to

1. Setup `MockLLM` and `MockEmbedding` objects

```
from llama_index.core.llms import MockLLM
from llama_index.core import MockEmbedding


llm = MockLLM(max_tokens=256)
embed_model = MockEmbedding(embed_dim=1536)
```

2. Setup the `TokenCountingCallback` handler

```
import tiktoken
from llama_index.core.callbacks import CallbackManager, TokenCountingHandler


token_counter = TokenCountingHandler(
    tokenizer=tiktoken.encoding_for_model("gpt-3.5-turbo").encode
)


callback_manager = CallbackManager([token_counter])
```

3. Add them to the global `Settings`

```
from llama_index.core import Settings


Settings.llm = llm
Settings.embed_model = embed_model
Settings.callback_manager = callback_manager
```

4. Construct an Index

```
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader


documents = SimpleDirectoryReader(
    "./docs/examples/data/paul_graham"
).load_data()


index = VectorStoreIndex.from_documents(documents)
```

5. Measure the counts!

```
print(
    "Embedding Tokens: ",
    token_counter.total_embedding_token_count,
    "\n",
    "LLM Prompt Tokens: ",
    token_counter.prompt_llm_token_count,
    "\n",
    "LLM Completion Tokens: ",
    token_counter.completion_llm_token_count,
    "\n",
    "Total LLM Token Count: ",
    token_counter.total_llm_token_count,
    "\n",
)


# reset counts
token_counter.reset_counts()
```

6. Run a query, measure again

```
query_engine = index.as_query_engine()


response = query_engine.query("query")


print(
    "Embedding Tokens: ",
    token_counter.total_embedding_token_count,
    "\n",
    "LLM Prompt Tokens: ",
    token_counter.prompt_llm_token_count,
    "\n",
    "LLM Completion Tokens: ",
    token_counter.completion_llm_token_count,
    "\n",
    "Total LLM Token Count: ",
    token_counter.total_llm_token_count,
    "\n",
)
```