Frequently Asked Questions

A token is the basic unit of text that Large Language Models process. Tokens can be words, parts of words (subwords), or even individual characters. For example, "tokenization" might be split into ["token", "ization"], while "cat" is typically a single token. The exact tokenization depends on the model's tokenizer algorithm and vocabulary.

Token counts matter for three main reasons:

• Cost: LLM providers charge per token, not per word or character. Accurate token counts help you estimate and control costs.
• Context Limits: Each model has a maximum token limit (context window). Exceeding this causes errors or truncation.
• Performance: Fewer tokens mean faster processing and lower latency in your applications.

There's no fixed ratio. On average, one English word is approximately 1.3 tokens, but this varies significantly. Common words like "the" or "is" are usually 1 token, while rare or long words can be 3-5 tokens or more. Non-English languages, technical terms, and special characters also affect the ratio. That's why using an accurate tokenizer is important!

Yes! LLM Tokenizer is completely free with no registration required, no usage limits, and no hidden fees. You can use it as much as you need for personal or commercial projects.

No. Your text is processed in real-time and immediately discarded after tokenization. We never store user input on our servers. The only data we collect is anonymous usage analytics (like page views) to improve the service. See our Privacy Policy for full details.

Different model families use different tokenizers with unique vocabularies and algorithms:

• OpenAI (GPT-4, GPT-3.5): Uses tiktoken with BPE (Byte Pair Encoding)
• Google (Gemini): Uses SentencePiece tokenizer
• Meta (Llama): Uses SentencePiece with different vocabulary
• Anthropic (Claude): Uses a custom tokenizer

The same text can have different token counts across these systems because they split words differently and have different vocabulary sizes.

For OpenAI models (GPT-4, GPT-3.5), we use the official tiktoken library, so our counts are 100% accurate and match what you'll be billed for. For other models, we use the best available approximations based on their documented tokenization methods. Our counts are typically within 1-2% of actual API usage.

Tiktoken is OpenAI's fast BPE (Byte Pair Encoding) tokenizer library. It's the official tokenizer used by OpenAI's GPT models. We use tiktoken to ensure our token counts for OpenAI models exactly match what you'll see in API usage and billing. It's open-source and available on GitHub.

These are different encoding schemes used by OpenAI models:

• cl100k_base: Used by GPT-4 and GPT-3.5-turbo. Has ~100,000 tokens in vocabulary. More efficient for modern use cases.
• p50k_base: Used by older GPT-3 models (Davinci, Curie, Babbage, Ada). Has ~50,000 tokens in vocabulary.

The newer cl100k_base encoding is generally more efficient and produces fewer tokens for the same text.

Currently, we only offer a web interface. However, if you need programmatic access, you can use the official tokenizer libraries directly in your code:

• OpenAI: pip install tiktoken
• HuggingFace: pip install transformers
• SentencePiece: pip install sentencepiece

LLM providers charge separately for input and output tokens:

• Input tokens: The prompt you send to the model
• Output tokens: The response generated by the model

Prices are typically listed per 1 million tokens (e.g., $0.03/1M input tokens). Our tool calculates: (input_tokens × input_price) + (output_tokens × output_price) = total_cost

Generating output requires significantly more computation than processing input. The model must generate each output token sequentially, running the entire neural network for each token. Input tokens are processed in parallel, which is much faster and cheaper. Typically, output tokens cost 2-3x more than input tokens.

We monitor LLM provider pricing announcements and update our database regularly. Prices typically change when providers announce new models or pricing tiers. If you notice outdated pricing, please email us at [email protected] and we'll update it promptly.

It depends on your use case! Use our Model Comparison feature to see costs for your specific input. Generally:

• Cheapest: GPT-3.5-turbo, Gemini Flash, Claude Haiku
• Mid-range: GPT-4, Gemini Pro, Claude Sonnet
• Premium: GPT-4 Turbo, Claude Opus, Gemini Ultra

Remember: cheaper models may require more tokens or multiple calls to achieve the same quality, so total cost depends on your specific needs.

You can input up to 100,000 characters at once. This is sufficient for most use cases, including long documents, code files, and complex prompts. If you need to analyze larger texts, you can split them into chunks and analyze each separately.

Yes! You can paste any code (Python, JavaScript, Java, etc.) into the text area. The tokenizer will process it just like any other text. This is useful for estimating costs when using LLMs for code generation, review, or analysis. Different programming languages may tokenize differently, so always test with your specific code.

This depends on your expected response length:

• Short answers (50-200 tokens): Yes/no questions, simple classifications
• Medium responses (200-1000 tokens): Explanations, summaries, short code snippets
• Long responses (1000-4000 tokens): Detailed articles, long code, comprehensive analyses
• Very long (4000+ tokens): Full documents, extensive code generation

Providing a range (min/max) gives you cost estimates for different scenarios.

Each model has a maximum context window (total tokens it can process). The context usage percentage shows: (input_tokens + max_output_tokens) / context_window × 100. If this exceeds 100%, your request will fail or be truncated. Keep it under 90% for safety, as some overhead is needed for special tokens and formatting.

Currently, we don't offer result saving or history features to maintain privacy and simplicity. You can take screenshots or copy the results you need. We may add optional account features in the future if there's demand.

If you're seeing discrepancies:

• Check the model: Make sure you selected the exact same model
• Special tokens: APIs may add system tokens for formatting (e.g., message boundaries in chat models)
• Whitespace: Ensure you're comparing the exact same text, including spaces and newlines
• API version: Older API versions might use different tokenizers

For OpenAI models, our counts should match exactly. For others, small differences (1-2%) are normal due to approximations.

Try these steps:

• Refresh the page (Ctrl+F5 or Cmd+Shift+R for hard refresh)
• Clear your browser cache and cookies
• Try a different browser
• Check if you have ad blockers that might interfere
• Ensure JavaScript is enabled

If problems persist, please contact us at [email protected] with details about your browser and the error message.

Yes! Our tool is fully responsive and works on smartphones and tablets. The interface adapts to smaller screens while maintaining all functionality. For the best experience on mobile, use landscape orientation when viewing detailed results.