LLM Tokenizer & Cost Estimator

Calculate tokens and estimate costs for top AI models (GPT-4, Claude 3, etc.) locally in your browser.

LLM Tokenizer

Depending on the model, 1,000 tokens is approximately 750 words.
0
Tokens
0
Characters

Cost Estimator

Input Cost (Prompt) $0.0000
Output Cost (Completion) $0.0000
Total Est. $0.0000
* Assumes equal output length for estimation if unknown, or you can adjust:
0% 100% (Input = Output) 200%

How it works

We use a standardized BPE (Byte Pair Encoding) tokenizer compatible with OpenAI GPT-4 and GPT-3.5 models (cl100k_base).

Pricing is updated based on official API docs (as of late 2024).

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Comprehensive Guide to LLM Tokenizer

Welcome to the W3D Network LLM Tokenizer & Cost Estimator. This tool is designed for efficient prompt engineering and budget planning when working with Large Language Models (LLMs) like GPT-4, Claude, or LLaMA.

1. What is a Token?

LLMs don't read text by "words" or "characters" like humans do. Instead, they digest text in chunks called tokens.

A token can be as short as one character or as long as one word.
Rule of Thumb: 1,000 tokens ≈ 750 words (in English).

  • Common words: Often 1 token (e.g., "apple").
  • Complex words: Split into multiple tokens (e.g., "hamburger" might be "ham", "bur", "ger").
  • Whitespace: Spaces and newlines are also counted as tokens.
2. Why Count Tokens?

Token counting is critical for three reasons:

  • Context Window Limits: Every model has a maximum memory (e.g., GPT-4-8k can handle ~8,192 tokens). If your prompt exceeds this, the model will crash or forget the beginning of the conversation.
  • Cost Estimation: API providers (OpenAI, Anthropic) charge by the token. Knowing your input/output size allows you to estimate monthly bills accurately.
  • Prompt Optimization: By reducing unnecessary tokens (fluff), you can make your prompts cheaper and faster without losing meaning.
3. Understanding BPE (Byte Pair Encoding)

This tool uses Byte Pair Encoding (BPE), specifically the cl100k_base encoding used by OpenAI's GPT-4 and GPT-3.5 Turbo.

Different models use different tokenizers (e.g., LLaMA 2 uses `sentencepiece`, Claude uses its own). While `cl100k_base` is the industry standard for estimation, exact counts may vary slightly across different model families.

4. Data Privacy & Security

When pasting proprietary code or private documents for estimation, security is non-negotiable.

  • 100% Client-Side: All tokenization happens in your browser using JavaScript libraries. Your text is never sent to OpenAI, W3D Network, or any other server.
  • Safe Analysis: You can safely calculate costs for sensitive legal contracts or medical data without risk of data leaks.
5. Best Practices for Prompt Engineering
  • Be Concise: "Explain the concept of quantum physics in simple terms" (9 tokens) is cheaper than "Can you please explain to me what quantum physics is all about using simple words?" (18 tokens).
  • Use Delimiters: Use distinct separators (like `###` or `---`) to help the model distinguish between instructions and data. These cost very few tokens but significantly improve output quality.
  • Pre-calculate: Before running a batch job on millions of rows, use this tool to estimate the total token load and avoid unexpected $1,000 bills.

Python (tiktoken)
import tiktoken

enc = tiktoken.get_encoding("cl100k_base")
tokens = enc.encode("Hello world")
print(len(tokens))