AI tokens are the fundamental atomic units of text and code processed by Large Language Models, governing pricing, context limits, and tokenization.
AI Tokens are the atomic fragments of characters, words, and sub-words that Large Language Models (LLMs)—such as OpenAI GPT-4o, Anthropic Claude 3.5 Sonnet, and Google Gemini—use to process, represent, and generate language and source code. Instead of reading text character-by-character or word-by-word, modern transformer architectures convert raw text into discrete integers using statistical tokenization algorithms like Byte-Pair Encoding (BPE). Tokens directly dictate context window capacity, execution latency, and API operational costs.
Count tokens across OpenAI, Anthropic, and Gemini models with our AI Token Counter or estimate API expenses using the AI Cost Calculator.
| Specification | Details |
|---|---|
| Primary Tokenizer Algorithms | Byte-Pair Encoding (BPE), WordPiece, SentencePiece, Unigram |
| Average Ratio (English) | 1 Token $\approx$ 0.75 Words (or $\approx$ 4 characters of standard English) |
| Vocabulary Size | Typically 32,000 to 128,000 unique token IDs per model family |
| Context Window Scope | Includes Input Tokens (Prompt + History) + Output Tokens (Completion) |
| API Pricing Basis | Billed per 1 Million tokens ($/M tokens), split into Input vs Output rates |
| Open Source Tooling | tiktoken (OpenAI), Hugging Face tokenizers, Google sentencepiece |
Large language models do not understand alphabetical letters directly. Tokenization bridges human text and neural network vector weights through a 3-step pipeline:
Human Text: "Tokenization is fast!"
│
▼ 1. Byte-Pair Splitting (Deconstruct into sub-words)
['Token', 'ization', ' is', ' fast', '!']
│
▼ 2. Vocabulary Lookup Table (Mapping strings to IDs)
[30219, 2134, 374, 3124, 0]
│
▼ 3. Vector Embeddings (Fed into Transformer Attention Layers)
[[0.024, -0.891, ...], [0.114, 0.442, ...], ...]
calculateTaxRateWithDeductions are split into known sub-words (['calculate', 'Tax', 'Rate', 'With', 'De', 'du', 'ctions']) without needing an infinite vocabulary dictionary." the", " and", " in") condense into a single token ID.| Model Family | Context Window Capacity | Input Cost (per 1M Tokens) | Output Cost (per 1M Tokens) |
|---|---|---|---|
| GPT-4o | 128,000 Tokens | ~$2.50 | ~$10.00 |
| Claude 3.5 Sonnet | 200,000 Tokens | ~$3.00 | ~$15.00 |
| Gemini 1.5 Pro | 2,000,000 Tokens | ~$3.50 | ~$10.50 |
| Llama 3.1 70B (Open Source) | 128,000 Tokens | ~$0.60 (Host dependent) | ~$0.80 |
Note: Output generation tokens are consistently priced 3x to 5x higher than input tokens because auto-regressive decoding requires one full transformer forward-pass per generated token.
tiktoken)import { encoding_for_model } from 'tiktoken';
export function calculatePromptTokens(text: string, model: 'gpt-4o' | 'gpt-4' = 'gpt-4o'): number {
// Obtain model-specific BPE encoding vocabulary
const enc = encoding_for_model(model);
// Encode string into Uint32Array of token IDs
const tokens = enc.encode(text);
const count = tokens.length;
// Free WebAssembly memory allocations
enc.free();
return count;
}
const prompt = "Format this JSON payload and output TypeScript interfaces.";
console.log(`Prompt Token Count: ${calculatePromptTokens(prompt)} tokens`);
Standard natural language BPE tokenizers are primarily trained on conversational web corpora. Source code featuring indentation spaces, brackets, camelCase identifiers, and syntax symbols often splits into multiple smaller 1-to-2 character tokens, resulting in a higher token-to-word ratio (frequently 1 token per 2–3 characters of code).
Input tokens comprise your system prompts, user questions, attached documents, and past conversation history. Output tokens are the words and code actively generated by the model during completion.
Paste your prompt or context documents into our AI Token Counter to inspect token breakdowns across providers, or estimate monthly inference bills with our AI Cost Calculator.
Free, browser-based utilities to test, generate, and inspect AI Tokens (LLM Tokenization & Context Windows) payloads directly.
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