Input vs. Output Tokens

Reviewed by Proof · Updated

Definition

Input tokens are the tokens you send to a model, and output tokens are the tokens it writes back.

Key takeaways

  • Input tokens (also called prompt tokens) are everything in your request.
  • Output tokens (also called completion tokens) are everything in the model's reply.
  • API responses report both counts in the usage field.
  • Many providers charge more for output than for input. luv13 charges the same rate for both.
  • You control input size with what you send, and output size with max_tokens.

Input tokens

These are the tokens in your request: the system message, the chat history, your new message, and any tool definitions or tool results. Because a model has no memory between calls, a long chat re-sends its history each time, so input tokens grow with every turn.

Output tokens

These are the tokens the model generates in its reply, including any tool calls it makes. You can set a ceiling with the max_tokens parameter. If the reply hits that ceiling, it stops early, and finish_reason is length instead of stop.

Reading the counts

Each response reports both counts:

"usage": {
  "prompt_tokens": 850,
  "completion_tokens": 120,
  "total_tokens": 970
}

These numbers are an example. prompt_tokens is input, completion_tokens is output, and total_tokens is the sum.

How pricing uses them

Your cost for a request is the input tokens times the input price plus the output tokens times the output price. On luv13 the input and output prices are the same, a flat $0.33 per 1 million tokens on every model, so you can just multiply total_tokens by that one rate. See Pricing.

For the example above, 970 total tokens cost 970 / 1,000,000 x $0.33, or about $0.00032.

Example

This request caps the reply at 100 output tokens. The usage field in the response shows both counts.

curl https://api.luv13.ai/v1/chat/completions \
  -H "Authorization: Bearer $LUV13_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "luv13/glm-5.3-flash",
    "messages": [{"role": "user", "content": "Explain tokens in two sentences."}],
    "max_tokens": 100
  }'