What is a token?
A token is the fundamental unit of text that large language models (LLMs) process. Rather than reading individual characters or whole words, models like GPT-4, Claude, and Gemini break text into tokens — subword pieces that balance vocabulary size with sequence length. A single token might be a common word like "the", a word fragment like "un" or "ing", or a single punctuation mark.
Understanding token counts matters because API pricing, context window limits, and rate limits are all measured in tokens, not words or characters.
How token estimation works
This tool uses heuristic analysis to estimate token counts without requiring the actual tokenizer libraries (which are large and model-specific). The approach:
**For English prose:** approximately 4.3-4.5 characters per token. Common words often map to single tokens, while less common words get split into subword pieces.
**For code:** approximately 3.4-3.6 characters per token. Code tends to produce more tokens per character because of punctuation, operators, and less common identifier names.
**For mixed content:** approximately 3.9-4.0 characters per token, blending the prose and code ratios.
The tool automatically detects whether your input is prose, code, or a mix, and adjusts the estimation accordingly.
Context window reference
Knowing your token count helps you stay within each model's context window:
- **GPT-4o**: 128,000 tokens - **GPT-4 Turbo**: 128,000 tokens - **Claude 3.5 Sonnet**: 200,000 tokens - **Claude 3 Opus**: 200,000 tokens - **Gemini 1.5 Pro**: 1,000,000 tokens - **Gemini Ultra**: 32,000 tokens
If your prompt plus expected response exceeds a model's context window, you will need to trim your input or use a model with a larger window.
Who should use this
- **Developers** building applications with LLM APIs who need to estimate costs and stay within context limits - **Prompt engineers** optimizing prompts to fit within token budgets - **Content creators** preparing text for AI processing who want to understand how their content translates to tokens - **Researchers** comparing how different models tokenize the same input
Tips for reducing token count
1. Remove unnecessary whitespace and blank lines 2. Use concise language — shorter sentences use fewer tokens 3. Avoid repeating instructions in multi-turn conversations 4. For code, remove comments and use shorter variable names when sending to an API 5. Consider summarizing long documents before including them in prompts
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