LLM Token Counter

What Is LLM Token Counting

Tokens are the fundamental units that large language models (LLMs) use to process text. Unlike words or characters, tokens are subword units determined by the model's tokenizer — a word might be a single token, or it might be split into multiple tokens depending on its frequency in the training data. Understanding token counts is essential for managing API costs, staying within context window limits, and optimizing prompt engineering.

Different LLM providers use different tokenizers, meaning the same text produces different token counts depending on the model. This tool counts tokens for popular models so you can estimate costs and ensure your prompts fit within context limits.

Tokenizer Comparison

How Tokenization Works

The text "Tokenization is important" might tokenize as:

Common English words are typically single tokens. Rare words, technical terms, and non-English text split into more tokens.

Common Use Cases

• Cost estimation: Calculate API costs before running requests. At $0.01/1K input tokens (GPT-4o), a 10,000-token prompt costs $0.10 per request.
• Context window management: Ensure your prompt plus expected response fits within the model's context window. Exceeding the limit causes truncation or errors.
• Prompt optimization: Identify wordy prompts that use excessive tokens and optimize them to reduce costs and latency.
• RAG pipeline tuning: Determine how many retrieved context chunks fit within the available context window alongside the system prompt and user query.
• Batch processing estimation: Before processing thousands of documents through an LLM API, estimate total token usage and costs.

Best Practices

1. Account for both input and output tokens — API costs include both the prompt (input) and the response (output). Budget for the maximum expected response length.
2. Include system prompts in your count — System prompts consume tokens on every request. A 500-token system prompt across 10,000 requests is 5 million tokens.
3. Use the correct tokenizer — Token counts vary between models. Always count using the tokenizer for your target model to get accurate estimates.
4. Monitor token usage in production — Track actual vs estimated token usage. Unexpected increases may indicate prompt injection attacks or inefficient prompts.
5. Optimize prompts for token efficiency — Remove redundant instructions, use concise language, and structure prompts to minimize token usage without sacrificing quality.