AI Token Calculator
Count tokens instantly for GPT-5.4, Claude Opus 4.6, Gemini 3.1 Pro, Claude Sonnet 4.6, DeepSeek V3.2. See context window usage, estimated cost, and a visual token breakdown.
Updated
- Input
- $0.0025/1K tokens
- $0.00
- Output
- $0.015/1K tokens · same length as input
- $0.00
- Total
- $0.00
How It Works
This tool uses the open-source gpt-tokenizer library to run OpenAI's Byte Pair Encoding (BPE) algorithm entirely in your browser. When you type or paste text, the tokenizer splits it into subword units - the same way GPT models process text during inference. GPT-5.4 uses the o200k_base encoding, giving an exact token count. Claude, Gemini, and DeepSeek use proprietary tokenizers that are not available as client-side libraries; this tool uses cl100k_base as a close approximation for those models. All processing is local - zero network requests are made when counting tokens.
Understanding API Costs
Every LLM API provider charges per token - separately for input (your prompt) and output (the model's reply). Input tokens are what you send: the system prompt, conversation history, and the current message. Output tokens are what the model generates in response. Output is typically priced higher because generation is more compute-intensive than reading.
Pricing varies widely across providers and tiers. A model optimized for speed and cost (like a "mini" or "flash" variant) may be 10-50x cheaper than a flagship model. For cost-sensitive workloads, counting tokens upfront helps you compare options and pick the right model before you start paying.
Context Window & Limits
The context window is the maximum number of tokens a model can process in one request - your input plus its output combined. Think of it as the model's working memory: everything outside it is invisible to the model.
Practically, this means long conversations accumulate tokens fast. A multi-turn chat with a detailed system prompt can easily consume tens of thousands of tokens per request. If you exceed the limit, the API returns an error. Common strategies to stay within limits include summarizing older turns, trimming the system prompt, or switching to a model with a larger context window.
Output tokens are also bounded - most models have a separate max output limit (often 4K-16K tokens), regardless of how large the context window is.
Frequently Asked Questions
A token is the basic unit language models process text in. Roughly 1 token = 4 characters = 0.75 words in English. Words can be one token or split into multiple - 'tokenization' may become 'token' + 'ization'. Numbers and punctuation often get their own tokens.
APIs charge per token. Knowing your token count lets you estimate costs, avoid exceeding context window limits (which causes API errors), and optimise your prompts for better cost efficiency.
Completely. All tokenization runs in your browser. No text is sent to servers, stored, or logged. Open DevTools Network tab while typing to verify - you'll see zero outbound requests.
GPT-5.4 is exact - it uses OpenAI's o200k_base tokenizer directly. Claude, Gemini, and DeepSeek use proprietary tokenizers, so this tool uses cl100k_base as a proxy, typically within 5-10% of the actual count. For exact counts use Anthropic's API token counter, Google AI Studio, or DeepSeek's API.
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