Use this free LLM token counter to quickly estimate GPT, Llama, and Gemini prompt size, track token usage, and understand API costs. Our privacy-first calculator works entirely in your browser.
Choose Your Model
Token Calculator
Supported AI Models & Tokenizers
GPT-4o Tokenizer
Uses the o200k_base encoding for OpenAI's most advanced model. Perfect for calculating GPT-4o API costs and context window usage.
GPT-3.5 / GPT-4 Tokenizer
Implements cl100k_base encoding used by ChatGPT, GPT-3.5-turbo, and GPT-4. Essential for OpenAI API development.
Llama 3 Tokenizer
Meta's Llama 3 tokenization for accurate token counting with open-source models including Llama 3.1 and Llama 3.2.
Gemini Tokenizer
Google's Gemini tokenization for Gemini Pro, Gemini Flash, and other Google AI models. Count tokens for Google AI Studio and Vertex AI.
Why Use Our Token Calculator?
Lightning Fast
Instant token counting with real-time updates as you type. No server delays or waiting times.
Client-Side Processing
All tokenization runs locally in your browser using WebAssembly. Your text never leaves your device.
Multi-Model Support
Support for GPT-4o, GPT-4, GPT-3.5, Llama 3, and Gemini tokenizers in one tool.
Common Use Cases
🔧 API Development
- • Calculate OpenAI API costs before sending requests
- • Optimize prompts to fit within context windows
- • Debug tokenization issues in your applications
- • Estimate costs for different model choices
📊 Research & Analysis
- • Compare token efficiency across different models
- • Analyze prompt engineering effectiveness
- • Study tokenization patterns in different languages
- • Benchmark model performance per token
💰 Cost Management
- • Budget AI project expenses accurately
- • Monitor token usage in production apps
- • Choose cost-effective models for your use case
- • Optimize prompts for better token efficiency
🎯 Prompt Engineering
- • Test different prompt formulations
- • Ensure prompts fit within model limits
- • Optimize for specific tokenization patterns
- • Compare prompt lengths across models
Token Counting FAQ
What is a token?
A token is a chunk of text used by LLMs. It can be as short as one character or as long as a word. Different models use different tokenization schemes - GPT models use byte-pair encoding (BPE), while others may use SentencePiece or custom tokenizers.
Why do token counts matter?
APIs charge per token and models have context limits, so knowing your token count helps manage costs and fit prompts. For example, GPT-4 has a 8k token limit in some variants, while GPT-4-turbo supports up to 128k tokens.
Does counting happen locally?
Yes. All tokenization runs entirely in your browser for maximum privacy. We use WebAssembly implementations of the official tokenizers to ensure accuracy without sending data to servers.
Which tokenizer should I use?
Use the tokenizer that matches your target model: o200k_base for GPT-4o, cl100k_base for GPT-3.5/GPT-4, Llama 3 tokenizer for Meta's models, and Gemini tokenizer for Google's models. Different tokenizers may produce different token counts for the same text.
How accurate are the token counts?
Our token counts are highly accurate as we use the same tokenization libraries that power the actual AI models. The counts should match exactly what you'd see when using the respective APIs.
Can I use this for production applications?
Absolutely! Our tool is perfect for development and production use. Since everything runs client-side, you can integrate it into your workflows without privacy concerns or rate limits.