LLM Token Counter

Our LLM Token Counter provides a precise estimate of how many tokens your text will consume across various large language models (LLMs). Tokens are not the same as words; they represent pieces of words, punctuation, or characters that the AI model uses to process information. By pasting your prompt into our tool, we analyze it against the specific encoding rules for popular APIs like OpenAI's GPT series, Anthropic's Claude, and Google's Gemini.

We don't just count input tokens; we also allow you to factor in anticipated output length. This comprehensive analysis helps you understand the total computational load before running your prompt. Furthermore, our ability to search models on Hugging Face ensures that even niche or open-source models like Llama and Qwen are accurately assessed for token consumption.

To get the most accurate token count and optimize your prompts, structure your input logically. Instead of writing one massive block of text, break down multi-step tasks into distinct sections within your prompt.

Use our tool’s model selector to test the same prompt across different architectures (e.g., GPT-4 vs. Claude 3). You may find that a slightly rephrased prompt using bullet points consumes fewer tokens and achieves better results than dense paragraphs.

• Be Specific: Use clear instructions rather than vague requests.
• Iterate: Test the token count with your full prompt, plus a placeholder for the expected output (e.g., '... [Expected Output Length] ...').
• Use Role Definitions: Define the AI's role clearly ('You are an expert...') as this is part of the input context and helps focus the model.