UnderstandingTokens in LLMs
Learn what tokens are, how they work, and why they matter for working with Large Language Models.
By Skytells AI Research Team
Last updated: April 20, 2025
Count tokens for your text inputs and optimize your prompts with our free tokenizer tool.
Token CalculatorWhat Are Tokens?
Tokens are the fundamental units that Large Language Models (LLMs) like GPT process. They are chunks of text that the model treats as a single unit — not exactly words, but often parts of words or even individual characters, depending on the tokenization scheme used.
Understanding tokens is essential for effectively working with LLMs, as they directly impact:
- How models process and understand text
- Maximum input and output lengths (context windows)
- Pricing for API usage (most AI providers charge per token)
- Optimizing prompts for efficiency and performance
How Text Gets Tokenized
Different LLMs use different tokenization methods. Here are examples of how common text might be tokenized using GPT-based models:
Hello, world! How are you doing today?
Machine learning models use tokenization to process text efficiently.
🚀 Emojis and special characters can be tokenized differently.
人工智能是计算机科学的一个分支
Note: These examples are simplified. Actual tokenization may vary between different models and implementations. Use our token calculator for precise tokenization with specific models.
Key Concepts in Tokenization
Most modern LLMs use subword tokenization, breaking words into common subunits rather than whole words. This helps handle large vocabularies and rare words efficiently.
Tokenizers for multilingual models are designed to handle various languages, though they typically use more tokens for non-English text, which impacts costs and context limits.
A common tokenization algorithm that iteratively merges the most frequent pairs of characters or character sequences to form a limited vocabulary of subword units.
API-based LLMs typically charge per token for both input and output. Understanding tokenization helps estimate costs and optimize usage for cost-effectiveness.
Programming code often tokenizes differently than natural language, with special characters and syntax each taking separate tokens, making code processing potentially more token-intensive.
The maximum number of tokens a model can process at once defines its context window. Larger windows allow for more context but may increase computational costs and memory usage.
Token Optimization Best Practices
Optimize Your LLM Prompts
- 1
Be Concise but Clear
Remove unnecessary words and phrases, but maintain clarity. Overly terse prompts might save tokens but could reduce effectiveness.
- 2
Prefer Common Words
Common words often tokenize as single tokens, while rare or technical terms may split into multiple tokens.
- 3
Batch Related Requests
When processing multiple similar items, batch them together rather than making separate API calls to reduce overhead tokens.
- 4
Use Efficient Formats
Structure data efficiently. JSON may use more tokens than simplified key-value formats for simple data.
- 5
Leverage System Messages
For models that support system messages (like GPT), use them to set context rather than repeatedly stating it in user messages.
Why Understanding Tokens Matters
As LLMs become increasingly central to AI applications, understanding tokenization is a crucial skill for developers, researchers, and businesses using these technologies. Effective token management can:
- Reduce costs for API-based models by optimizing token usage
- Improve response quality by maximizing the effective use of context windows
- Enable processing of longer documents through efficient tokenization strategies