Principle:Openai Openai python Input Token Counting
| Knowledge Sources | |
|---|---|
| Domains | Cost_Management, NLP |
| Last Updated | 2026-02-15 00:00 GMT |
Overview
A pre-flight estimation technique that counts how many tokens an input will consume before sending it to the model for generation.
Description
Input token counting allows developers to estimate the cost and feasibility of a request before making it. By sending the same input, tools, and instructions to a counting endpoint, you receive the total token count without incurring generation costs. This is essential for cost management, context window budgeting, and input truncation decisions.
Usage
Use this principle before sending large or variable-length inputs to the Responses API. It helps determine if the input fits within the model's context window and estimate API costs before committing to a generation request.
Theoretical Basis
Token counting applies the model's tokenizer to the input without performing generation:
# Pre-flight token estimation
token_count = count_tokens(
input=same_input_as_request,
model=same_model,
tools=same_tools,
instructions=same_instructions
)
# Returns total_tokens: int
# Decision logic
if token_count > model_context_limit - desired_output_length:
truncate_or_split(input)
The count includes all tokens from input text, tool definitions, system instructions, and any special formatting tokens added by the API.