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AI Token Counter & Tokenizer

Count tokens for GPT-4, GPT-3.5, and other AI models. Visualize how text is tokenized in real-time.

AI Tokenizer

Count tokens for GPT-4, GPT-3.5, and other AI models. Visualize how text is tokenized in real-time.

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Tokens0
Characters44
Token Visualization

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What are tokens?

Tokens are pieces of words used by AI models like GPT. A token can be as short as one character or as long as one word. For example, "ChatGPT is great!" is encoded into six tokens: ["Chat", "G", "PT", " is", " great", "!"].

On average, 1 token ≈ 4 characters in English. 100 tokens ≈ 75 words.

Key Takeaways

  • Counts vary by model. GPT-4o uses a more efficient encoding (o200k) than GPT-4 (cl100k).
  • Costs are calculated per 1,000 tokens (1k tokens).
  • This tool uses js-tiktoken to run the exact tokenizer in your browser.

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Professional AI Token Counter

Our tokenizer helps developers, content creators, and AI enthusiasts understand how their text is processed by language models. Whether you're optimizing prompts for ChatGPT, staying within API limits, or learning about tokenization, this tool provides instant, accurate feedback.

Understanding Tokenization

Tokenization is the process of breaking down text into smaller units called tokens. These tokens are the fundamental building blocks that AI models use to understand and generate text.

Different models use different tokenization methods. GPT-4 and GPT-3.5 use the cl100k_base encoding, which is more efficient than older encodings. This means the same text might result in fewer tokens with newer models.

Why Token Count Matters

Most AI APIs charge based on token usage, not character count. Understanding your token consumption helps you optimize costs and stay within model limits.

GPT-4 has a context window of 8K or 32K tokens, while GPT-3.5 Turbo supports up to 16K tokens. Knowing your token count ensures your prompts fit within these limits.

AI Token Counter - Free GPT-4 & ChatGPT Tokenizer Tool

Got Questions? We have you covered

Our tokenizer provides estimated token counts based on common patterns. For exact counts matching OpenAI's implementation, you would need to use the official tiktoken library. However, our estimates are typically within 5-10% of the actual count.
We support GPT-4, GPT-4 32K, GPT-3.5 Turbo, and the Davinci series models. Each model uses a specific encoding (cl100k_base or p50k_base) that affects how text is tokenized.
Different models use different tokenization algorithms. Newer models like GPT-4 use more efficient encodings (cl100k_base) that can represent the same text with fewer tokens compared to older models.
On average, 1 token equals about 0.75 words in English. This means 100 tokens is roughly 75 words. However, this varies based on the text - technical terms, special characters, and non-English text may tokenize differently.
Yes! The tokenizer works with any language, though token efficiency varies. English typically has the best token-to-word ratio. Languages with different character sets (like Chinese or Arabic) may use more tokens per word.

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