Token Calculator for LLMs

Free AI Token Counter & API Cost Calculator for GPT-5.4, Claude Opus 4.6, Gemini 3 Pro and other LLMs

Token Calculator(Loading tokenizer...)

Tokens0
Words0
Characters (no spaces)0
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How to Use the AI Token Calculator

1

Paste Your Text

Enter or paste the text you want to analyze into the text area above.

2

View Token Count

See the instant token count using OpenAI's official tiktoken BPE tokenizer algorithm.

3

Compare API Costs

Review the pricing table to compare costs across GPT-5.3, Claude Opus 4.6, Gemini 3 Pro, and other models.

4

Optimize Your Prompts

Use the token visualization to identify opportunities to reduce token usage and API costs.

Understanding Tokenization

This tool functions as a universal tokenizer for OpenAI, Anthropic, and Google models. We use official Tiktoken libraries and model-specific tokenizers for 99% accuracy in token counting.

What is BPE (Byte-Pair Encoding)?

BPE is the tokenization algorithm used by GPT models. It breaks text into subword units by iteratively merging the most frequent character pairs. For example, "tokenization" might become ["token", "ization"]. This allows models to handle rare words efficiently while keeping vocabulary size manageable.

What is a Context Window?

The context window is the maximum number of tokens an LLM can process in a single request (input + output combined). GPT-5.3 offers 256K tokens, Claude Opus 4.6 provides 1M tokens, and Gemini 3 Pro supports up to 2M tokens. Exceeding this limit will cause truncation or errors.

What is Cached Input Pricing?

Cached input pricing offers significant discounts (up to 90% off) when you reuse the same prompt prefix across multiple API calls. This is ideal for system prompts, few-shot examples, or document analysis where the context remains constant while only the query changes.

Input vs Output Token Costs

Output tokens are typically 2-4x more expensive than input tokens because they require the model to perform sequential generation. To optimize costs, design prompts that get concise responses, use output length limits, and choose the right model for each task.

Word-to-Token Conversion Guide

Token counts vary significantly based on content type and language. Use this reference to estimate token usage before running your text through the calculator.

English Text

Example:Hello world
Ratio:~1.3 tokens/word
1000 words:~1,300-1,500

Standard prose averages 1.3 tokens per word

Code (Python/JS)

Example:def func():
Ratio:~2-3 tokens/word
1000 words:~2,000-3,000

Symbols, operators, and syntax increase token count

Chinese/Japanese

Example:你好世界
Ratio:~2+ tokens/char
1000 words:~2,000+

CJK characters often split into multiple tokens

Technical Writing

Example:API endpoint
Ratio:~1.5 tokens/word
1000 words:~1,500-1,800

Technical terms and abbreviations vary

JSON/XML Data

Example:{"key":"value"}
Ratio:~3-4 tokens/word
1000 words:~3,000-4,000

Structural characters add significant overhead

Compare Token Costs

Compare API pricing across all major LLM providers. Prices shown are per 1 million tokens. The calculated prices below reflect the cost for your current input text.

FAQ

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