A token is the fundamental unit an AI language model uses to process text. Models use a tokenizer (like OpenAI's BPE algorithm) to split your input into tokens before processing. On average, 1 token = ~4 English characters or ~0.75 words. Punctuation, spaces, and special characters each count as tokens too. Code and non-English text tend to use more tokens per word because the tokenizer's vocabulary was trained more heavily on English.

This tool uses a BPE (Byte Pair Encoding) approximation that closely mirrors OpenAI's tiktoken library — the same algorithm powering GPT-3.5, GPT-4, and GPT-4o. For English text, accuracy is typically 97–99%. For multilingual or code-heavy prompts, counts may vary by 2–5% because each model uses a slightly different vocabulary. For production-critical cost estimation, verify with the official API's token counter in your language.

No. This is a 100% client-side tool. All counting happens in your browser using JavaScript — no text is sent to any server. Your prompts are completely private. No account, no API key, no login required.

AI APIs like OpenAI and Anthropic charge based on token usage because processing tokens requires GPU compute resources. You're charged for input tokens (what you send) and output tokens (what the AI generates back). Output tokens are typically 2–3x more expensive than input tokens because generation is computationally heavier than reading. Monitoring your token count helps you control API costs at scale.

A context window is the maximum number of tokens an AI model can "see" at once — your message, the conversation history, the system prompt, and the AI's response all count toward this limit. If you exceed it, the model can't process your full input. GPT-3.5 has a 16K token limit, while Claude 3.5 and Gemini 1.5 support up to 200K and 1M tokens respectively, making them better for analyzing long documents.

Yes, code typically uses 20–40% more tokens per line than equivalent English prose. This is because code contains many special characters (brackets, semicolons, underscores), whitespace/indentation, and long variable names that each consume tokens. Comments in code are especially expensive. For large codebases, consider stripping comments and minifying before sending to an API.

A token is the smallest unit of text that an AI language model processes. Think of it as the "atom" of language for AI. Models don't read character by character or word by word — they read token by token, and each token is a chunk of text their vocabulary recognizes.

Tokens matter for three reasons: cost (you pay per token on every API), speed (more tokens = slower generation), and context limits (every model has a maximum number of tokens it can process at once).

For English text: 1 token ≈ 4 characters ≈ 0.75 words. So 1,000 tokens ≈ 750 words ≈ a 1.5-page typed document.

This tool uses a BPE approximation algorithm that mirrors the logic of OpenAI's tiktoken library — the official tokenizer used by GPT-3.5, GPT-4, and GPT-4o. For standard English prose, accuracy is 97–99%.

Where it may differ slightly: multilingual content (2–5% variance), heavy code with unusual symbols (3–6% variance), and text with many emojis or special Unicode characters. This happens because each model vendor uses a slightly different vocabulary (called a vocabulary file or "vocab.json").

For mission-critical production cost budgeting, validate with the provider's official API. For planning, estimation, and prompt optimization, this tool is fully reliable.

No. This is a 100% client-side tool. Every calculation — token counting, cost estimation, character analysis — happens entirely in your browser using JavaScript. Your text never leaves your device.

The only data stored locally is your session history, which is saved to your browser's localStorage (on your device only) and can be cleared at any time from the History tab. No account, no API key, no tracking.

Reading input (called "prefill" or "encoding") is computationally cheap because the model processes all input tokens in parallel using matrix operations on GPUs.

Generating output is fundamentally different — it must produce one token at a time in sequence, each step requiring a full forward pass through the model. This autoregressive generation is 3–5x more compute-intensive than reading. That cost difference is reflected in pricing: GPT-4o charges $5/1M input tokens but $15/1M output tokens.

A context window is the maximum number of tokens a model can "see" at once — your system prompt, the entire conversation history, your new message, and the model's response all count toward this limit simultaneously.

When you exceed it, one of two things happens depending on the API: either the request is rejected with an error, or the oldest tokens are silently dropped (the model "forgets" earlier parts of the conversation). This is why very long chats eventually lose context about what was discussed earlier.

Current limits: GPT-3.5 = 16K, GPT-4o = 128K, Claude 3.5 = 200K, Gemini 1.5 Pro = 1M tokens.

Yes, significantly more. Code typically consumes 30–50% more tokens per line than equivalent English prose. Here is why:

• Special characters — brackets, semicolons, colons, underscores, operators — each consume tokens and appear far more in code than prose.

• Indentation — every indent level (spaces or tabs) eats tokens. Four-space indentation in deeply nested code adds up fast.

• Long variable names — descriptive names like calculateMonthlyRecurringRevenue split into many tokens.

• Comments — often the most expensive part of a code file. Strip comments before sending large files to an API.

ChatGPT's internal system prompt (the hidden instructions that define its behavior) is estimated at 1,500–2,500 tokens based on responses to adversarial prompting. This is consumed on every single conversation turn, which is why conversations have a slightly lower effective context limit than the stated maximum.

For your own custom GPTs or API deployments: a minimal system prompt is 100–300 tokens, a detailed persona with instructions is 500–1,500 tokens, and enterprise-grade system prompts with examples can reach 3,000–8,000 tokens. Use the "Add System Prompt" button above to count yours accurately.

Prompt caching is a feature offered by Anthropic (Claude), OpenAI, and Google that dramatically reduces costs for repeated content. When you use the same system prompt or context document across many API calls, the provider caches the KV (key-value) computation for those tokens and serves them at 80–90% discount on subsequent calls.

Anthropic's Claude charges only $0.30/1M tokens for cached reads vs $3.00/1M for fresh input — a 10x saving. OpenAI offers similar caching for prompts over 1,024 tokens. This means if you have a 10,000-token system prompt used 1,000 times per day, caching can reduce that specific cost by 90%.