LLM Token Counter

What Are LLM Tokens?

An LLM token is a subword unit that language models use to process text. Large language models like GPT-4o, Claude, and Gemini break text into tokens — pieces that can be as short as a single character or as long as an entire word, depending on how common they are in the training data. This AI token counter helps you see exactly how your text gets tokenized.

In English, one token is roughly 4 characters or about three-quarters of a word. The sentence "The quick brown fox" is 4 words but typically 4-5 tokens. Less common words get split into multiple tokens: "tokenization" might become "token" + "ization" — two tokens for one word. Non-Latin scripts like Chinese, Japanese, Korean, Arabic, and Cyrillic generally produce 2-3x more tokens per character.

Tokens matter because they directly determine two things: cost and capacity. Every API call is billed based on the number of tokens processed (input) and generated (output). And every model has a maximum number of tokens it can handle in a single request — the context window. Understanding your token count helps you budget API costs and ensure your prompts fit within the model's limits.

The tokenization process is called Byte Pair Encoding (BPE). It starts with individual bytes and iteratively merges the most frequent pairs into new tokens. Different models use different BPE vocabularies: OpenAI's GPT-4o uses o200k_base with 200,000 tokens in its vocabulary, while GPT-4 uses cl100k_base with 100,000 tokens. Larger vocabularies generally produce fewer tokens for the same text, which means lower costs and more room in the context window.

How Token Counting Works

This token calculator uses gpt-tokenizer — the same tokenizer that OpenAI's API uses internally. When you paste text, the tokenizer applies the model's BPE encoding rules to split your text into tokens and returns the exact count. For OpenAI models (GPT-4o, GPT-4, GPT-3.5), the count is exact — identical to what the API would report.

For non-OpenAI models like Claude (Anthropic), Gemini (Google), Llama (Meta), and Qwen (Alibaba), we use OpenAI's tokenizer as a proxy since these providers do not publish their tokenizer libraries for browser use. The estimate is typically accurate within 10-20%. We clearly mark these counts as estimates so you know the precision level.

The tokenizer runs entirely in your browser. When you select a model, the tool downloads the encoding data (1-3 MB depending on the vocabulary size) and caches it locally. Subsequent text changes are counted instantly without any network requests. Your text never leaves your device — you can also use our Word Counter for basic word and character counts.

Token counting happens in real time as you type or paste. For most text lengths, the count updates within milliseconds. For very large texts (over 500 KB), the tool uses a slight delay to keep the interface responsive. Character and word counts always update instantly since they do not require the tokenizer.

Understanding AI Model Pricing

AI model providers charge based on the number of tokens processed. Most models have separate pricing for input tokens (your prompt) and output tokens (the model's response). Output tokens are typically 2-5x more expensive than input tokens because generating text requires more computation than reading it.

Pricing is quoted per million tokens. For example, GPT-4o charges $2.50 per million input tokens and $10.00 per million output tokens. A 1,000-token prompt to GPT-4o costs $0.0025 — a fraction of a cent. But costs add up quickly at scale: processing 10 million tokens per day at GPT-4o input rates costs $25/day or $750/month. See OpenAI's pricing page for the latest rates.

Budget-friendly models like GPT-4o mini ($0.15/1M input) and Gemini 2.0 Flash ($0.10/1M input) offer dramatically lower costs — often 10-25x cheaper than premium models. Open-weight models like Qwen3 and Llama 3.1 are free to run locally. Our tool shows cost estimates for all models so you can compare options.

How to Optimize Token Usage

Reducing token count directly lowers your API costs and helps you fit more content within context windows. Here are proven strategies:

1. Be concise in system prompts. Remove filler phrases like "I want you to" or "Please make sure to." Direct instructions use fewer tokens.
2. Use structured formats. Bullet points and numbered lists typically use fewer tokens than equivalent paragraph text.
3. Minimize few-shot examples. Each example adds tokens. Use the minimum number of examples needed for good results, or switch to zero-shot if the model handles it well.
4. Compress context. Summarize long documents before including them in prompts. A 10,000-token document can often be summarized to 500 tokens without losing the information the model needs.
5. Choose the right model. Models with larger vocabularies (like GPT-4o with o200k_base) produce fewer tokens for the same text than older models (GPT-3.5 with cl100k_base).
6. Cache and reuse. If you send the same system prompt repeatedly, consider OpenAI's prompt caching to reduce costs by up to 50%.

Use this token counter to measure your prompts before and after optimization. Even small reductions in token count compound significantly at scale — a 20% reduction in a prompt sent 100,000 times per day can save hundreds of dollars monthly.

AI Model Pricing Comparison

All 26 models supported by this token counter. Prices per million tokens. Last updated April 2026.