LLM Token Counter
Estimate token counts for GPT-4o, Claude, Llama, and Gemini models. Compare usage across providers in real time.
Text Stats
Features
- ✓Estimate token counts for 14 popular LLM models across 5 providers
- ✓Real-time counting as you type with instant updates
- ✓Compare token usage across OpenAI, Anthropic, Google, Meta, and DeepSeek models
- ✓See what percentage of each model's context window your text uses
- ✓Copy full summary with all model estimates to clipboard
- ✓Includes character, word, and line counts alongside token estimates
How to Use
- 1Paste or type your text — a prompt, system message, or any content you plan to send to an LLM
- 2View the text stats section for character, word, and line counts
- 3Check the token estimates table to see approximate token usage per model
- 4Compare the "% Used" column to understand how much of each model's context window your text occupies
- 5Click "Copy Summary" to copy all estimates as plain text for documentation or cost calculations
Examples
Input
You are a helpful assistant that answers questions about software development.
Output
GPT-5.4: ~18 tokens | Claude Opus 4.6: ~21 tokens | Llama 4 Scout: ~20 tokens
Input
function binarySearch(arr: number[], target: number): number {
let left = 0;
let right = arr.length - 1;
while (left <= right) {
const mid = Math.floor((left + right) / 2);
if (arr[mid] === target) return mid;
}
return -1;
}Output
GPT-5.4: ~58 tokens | Claude Opus 4.6: ~66 tokens | Llama 4 Scout: ~63 tokens
Why Estimate LLM Token Counts?
Large language models process text as tokens — chunks of characters that typically represent common words, subwords, or individual characters. Understanding how many tokens your text uses is essential for managing costs, staying within context limits, and optimizing prompt engineering workflows.
Different model providers use different tokenizers. OpenAI models use o200k_base, Anthropic uses their own tokenizer, Meta's Llama models use SentencePiece, Google's Gemini has its own approach, and DeepSeek uses a custom vocabulary. This means the same text produces different token counts depending on the model. This tool estimates counts for all major providers so you can compare side by side.
Token counts directly impact API costs. Most providers charge per token for both input and output. A prompt that uses 1,000 tokens on GPT-5.4 costs differently than the same prompt on Claude Sonnet 4.6. By estimating tokens before sending requests, you can choose the most cost-effective model for your use case and avoid unexpected bills.
Context window limits determine how much text a model can process in a single request. These range from 128K tokens for DeepSeek models to 10M for Llama 4 Scout. If your system prompt, few-shot examples, and user message exceed the context window, the API call will fail. This tool shows what percentage of each model's context window your text occupies, helping you design prompts that fit comfortably within limits.
All estimation happens in your browser using average character-to-token ratios per model family. While actual tokenizer output may vary slightly — especially for code, non-English text, or unusual formatting — these estimates are accurate enough for planning and cost estimation. For exact counts, use each provider's official tokenizer.