Tokens are the atomic units that language models use to process text. Language models understand tokens rather than characters or bytes. Each token represents a single unit of meaning, like a word or a group of words. The Writer API processes all text input and output in terms of tokens. See the maximum number of tokens for each model in the models overview page.

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Tokenization

Tokenization is the process of breaking input text down into smaller units. Depending on the tokenization method the model uses, a single word can be represented by one token or may be broken down into multiple sub-word tokens. Here’s how different text types typically tokenize: 
"hello world"           → ["hello", " world"]           # 2 tokens
"API"                   → ["API"]                       # 1 token
"tokenization"          → ["token", "ization"]          # 2 tokens
"www.writer.com"        → ["www", "writer", "com"]      # 3 tokens

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Input and output token usage

Every API call consumes tokens for both the prompt you send and the response generated: 
# Example API call
response = client.chat.completions.create(
  model="palmyra-x5",
  messages=[
    {"role": "user", "content": "Write a product description for a cozy sweater"},  # ~8 tokens
  ],
  max_tokens=150,  # Maximum tokens to include in the response
)

# Total token usage = prompt tokens + response tokens

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Inspect token usage

You can inspect the usage information from the API response to see the token usage for each response.
The prompt tokens reported in the response include the tokens used for the model’s system prompt.
from writerai import Writer

# Initialize the client. If you don't pass the `api_key` parameter,
# the client looks for the `WRITER_API_KEY` environment variable.
client = Writer()

response = client.chat.chat(
  model="palmyra-x5",
  messages=[{"role": "user", "content": "Write a product description for a cozy sweater"}],
  max_tokens=150,
)

# Log token usage to understand patterns
print(f"Prompt tokens: {response.usage.prompt_tokens}")
print(f"Response tokens: {response.usage.completion_tokens}")
print(f"Total tokens: {response.usage.total_tokens}")
You can view your organization’s overall token usage and spend in the Consumption dashboard.