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The Token Cost Calculator converts text into tokens and calculates the exact API cost for any LLM provider. It helps developers understand the relationship between text length, token count, and cost across different models and pricing tiers.

공식

Cost = (Token Count / 1,000,000) × Price per Million Tokens
W
Word Count (words) — Number of words in the text
T
Token Count (tokens) — Number of tokens after tokenization
P
Price per Million ($/1M tokens) — Model-specific per-million-token rate
C
Cost ($) — Total cost for the given token count

단계별 가이드

  1. 1Paste your text or enter a word/character count to estimate tokens
  2. 2Select the LLM provider and model for pricing
  3. 3Specify whether the text is input (prompt) or output (completion)
  4. 4View the token count, cost for this text, and cost per word

풀어진 예시

입력
1,000 words of English text as input to GPT-4o
결과
Approximately 1,333 tokens. Cost: 1,333 / 1,000,000 × $2.50 = $0.0033 (one-third of a cent).
입력
10,000 words of output from Claude 3.5 Sonnet
결과
Approximately 13,333 tokens. Cost: 13,333 / 1,000,000 × $15.00 = $0.20 (twenty cents).

피해야 할 일반적인 실수

  • Assuming 1 word = 1 token — in English, 1 token averages about 0.75 words or 4 characters
  • Forgetting that code, non-English text, and special characters often tokenize into more tokens per word
  • Not separating input and output costs when budgeting — they have very different rates

자주 묻는 질문

How many tokens is a typical ChatGPT conversation?

A typical conversation turn is 50-200 tokens for the user message and 100-500 tokens for the response. A 10-turn conversation accumulates approximately 2,000-5,000 total tokens. System prompts add 100-2,000 additional tokens per call depending on complexity.

Do different languages use different numbers of tokens?

Yes, significantly. English is most efficiently tokenized (~1.3 tokens per word). Chinese, Japanese, and Korean use 2-3 tokens per character. Arabic, Hindi, and other scripts may use 3-5 tokens per word. This means non-English API calls can be 2-4x more expensive per equivalent meaning.

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