AI Token Counter
Count tokens for GPT-4o, Claude, Llama, Gemini, and more. See costs instantly.
Cost Estimation
How to Use This Tool
- Paste or type your text into the input area above. The token count updates in real time as you type.
- Select the AI model you want to estimate tokens for from the dropdown menu. Different models use different tokenizers.
- View your results: token count, character count, word count, and estimated API cost are displayed instantly.
- Optionally enter expected output tokens to calculate the full round-trip API cost for your prompt.
- Click 'Copy' to copy the token summary to your clipboard for documentation or cost planning.
Understanding AI Tokens: A Developer's Guide
Tokens are the fundamental units that large language models (LLMs) process. When you send text to an AI API like OpenAI's GPT-5, Anthropic's Claude, or Google's Gemini, the text is first broken into tokens using a tokenizer. Each model family uses a different tokenizer, which is why the same text can produce different token counts across models.
In English, a token roughly corresponds to 3-4 characters or about 0.75 words. However, this ratio varies significantly for other languages — Chinese, Japanese, and Korean text typically requires more tokens per character. Code also tokenizes differently than natural language, with common programming patterns often mapping to single tokens.
Understanding token counts is critical for AI developers because API pricing is based on tokens processed. Input tokens (your prompt) and output tokens (the model's response) are often priced differently. For example, GPT-4o charges $2.50 per million input tokens and $10.00 per million output tokens. Accurate token counting helps you estimate costs, stay within context window limits, and optimize your prompts for efficiency.
The context window is the maximum number of tokens a model can process in a single request (prompt + response combined). DeepSeek V4, Gemini Pro, and other long-context models can reach 1M tokens, but long prompts still affect cost and latency. For models with cached-input pricing, this tool shows both cache-miss and cached-input estimates.
Last updated: April 2026
FAQ
What is a token?
A token is a chunk of text that LLMs process. It can be a word, part of a word, or a punctuation mark. On average, 1 token ≈ 4 characters in English or ≈ 0.75 words.
Why do token counts differ between models?
Different models use different tokenizers. GPT-4o uses the o200k_base tokenizer while Claude uses its own. The same text may produce different token counts across models.
How accurate is this counter?
For OpenAI models, we use the official tiktoken tokenizer — 100% accurate. For other models (Claude, Llama, Gemini), we use calibrated estimates that are typically within 5% of the actual count.
How do tokens affect API costs?
AI API pricing is based on the number of tokens processed. Each provider charges per million tokens, with input tokens typically cheaper than output tokens. For example, if your prompt is 1,000 tokens and the response is 500 tokens, you pay for 1,000 input tokens + 500 output tokens. Use the token counter alongside our Pricing Calculator to estimate monthly costs.
How does this help with DeepSeek V4 costs?
DeepSeek V4 splits input into cache-hit and cache-miss tokens. When you select DeepSeek V4 Flash or Pro, the tool shows the normal input estimate and a cached-input estimate so you can see how much repeated repository context or system prompts may cost.
What is the context window limit?
The context window is the maximum combined size of input + output tokens a model can handle in one request. GPT-4o supports 128K tokens, Claude Opus 4.6 supports up to 1M tokens for eligible beta access, and Gemini 2.5 Pro supports up to 2M tokens. Exceeding this limit results in an API error. Always check your prompt fits within the model's context window before making API calls.
How do tokens work for non-English languages?
Non-English languages typically use more tokens per character than English. Chinese, Japanese, and Korean can use 2-3x more tokens for the same semantic content. This affects both cost and context window usage. If you work with multilingual text, use this tool to check token counts for each language.