LLM Token Counter
Count tokens for GPT-4o, GPT-4, Claude, and Llama 3
What Are LLM Tokens?
Tokens are the basic units that large language models (LLMs) use to process text. A token can be a word, part of a word, or a punctuation mark. Understanding token counts is essential for managing API costs, staying within context limits, and optimizing prompts.
On average, 1 token is roughly 4 characters or 0.75 words in English. However, this varies significantly by language, vocabulary, and the specific tokenizer used.
How to Use This Tool
- Paste or type your text into the input area
- Token counts update in real-time for each model family
- View character, word, and sentence counts in the stats bar
- Each model card shows the tokenizer encoding used and the exact token count
Token Counts by Model
| Model Family | Tokenizer | Accuracy | Vocab Size |
|---|---|---|---|
| GPT-4o / GPT-4o-mini | o200k_base | Exact | 200K tokens |
| GPT-4 / GPT-3.5 Turbo | cl100k_base | Exact | 100K tokens |
| Llama 3 / 3.1 / 3.2 | llama3-tokenizer | Exact | 128K tokens |
| Claude 3.5 / 3.7 | cl100k_base calibrated | Estimate | ~100K tokens |
This tool runs real tokenizer libraries directly in your browser for GPT and Llama models, producing the same token counts you’d get from the official APIs. For Claude, Anthropic has not released a public tokenizer, so we use a calibrated estimate based on cl100k_base. No text is sent to any server.
Why Token Counts Matter
API pricing: Most LLM APIs charge per token for both input and output. Knowing your token count helps estimate costs before making API calls.
Context window limits: Each model has a maximum context length (e.g., 128K tokens for GPT-4o, 200K for Claude). Long prompts or documents may need to be trimmed.
Prompt optimization: Shorter prompts that convey the same meaning save money and leave more room for model responses. Token counting helps you optimize prompt length.
Rate limiting: API rate limits are often measured in tokens per minute. Understanding your token usage helps you stay within limits.
Common Token Counts
| Text Type | Approximate Tokens |
|---|---|
| 1 sentence | 15-25 tokens |
| 1 paragraph | 50-100 tokens |
| 1 page (500 words) | 375-500 tokens |
| Blog post (1,500 words) | 1,100-1,500 tokens |
| Short book (50,000 words) | 37,000-50,000 tokens |
Frequently Asked Questions
Why do different models have different token counts for the same text?
Each model family uses a different tokenizer with its own vocabulary. The o200k_base tokenizer (GPT-4o) has a larger vocabulary than cl100k_base (GPT-4), so it often produces fewer tokens for the same text.
Are the Claude and Llama token counts exact?
Llama 3 counts are 100% accurate — we use the official Llama 3 tokenizer library with its full 128K vocabulary running directly in your browser. Claude counts are calibrated estimates based on cl100k_base, since Anthropic has not released a public tokenizer. The estimate is typically within 5-15% of the actual count.
How do special characters affect token count?
Special characters, code, and non-English text often use more tokens per character than standard English text. Emoji and Unicode characters can use 2-4 tokens each.
What’s the difference between input and output tokens?
Input tokens are your prompt text. Output tokens are the model’s response. Both count toward usage and billing, but some APIs charge different rates for each.
Try our free letter counter → to count characters, words, and sentences alongside your token analysis.