AI Token Counter & Subword Tokenizer
Calculate exact token counts, inspect colorized Byte-Pair Encoding chunks, and analyze context window headroom across Claude Sonnet 5, GPT-6 Astra, Gemini 3, and DeepSeek.
Universal AI Token Counter
Zero-latency, client-side BPE tokenization with real-time multi-model benchmarks.
How LLM Tokenizers Process Text
Large Language Models do not read characters or whole words. Instead, an algorithm converts raw UTF-8 strings into discrete token IDs. Common words like "the" or "engineering" occupy a single token, whereas rare terminology, variable names (getUserAccountById), and non-Latin alphabets are split into multiple subword chunks.
Frontier LLM Tokenizer Architectures
Verified 2026| Tokenizer Family | Associated Frontier Models | Vocabulary Size | Algorithm Type | Compression Efficiency |
|---|---|---|---|---|
| o200k_base | OpenAI (GPT-6, GPT-5.6, o3, o4-mini) | 200,000 tokens | Byte-Pair Encoding (BPE) | High (~3.7 chars/token) |
| claude | Anthropic (Claude Sonnet 5, Opus 5, Haiku 4.5) | ~65,000 tokens | BPE with byte fallback | Optimized for code & English (~3.9 chars/token) |
| gemini | Google (Gemini 3.8 Flash, 3.1 Pro) | 256,000 tokens | SentencePiece unigram | Superior multilingual compression (~3.4 chars/token) |
| deepseek | DeepSeek (DeepSeek-V4.1-Flash) | 100,000 tokens | Byte-level BPE | High efficiency for math and code |
| llama4 | Meta (Llama 4 Scout, Llama 4 Maverick) | 128,000 tokens | Tiktoken BPE | High code and multilingual coverage |
| mistral | Mistral AI (Mistral Large 3, Small 4) | 131,072 tokens | Tekken BPE | Tekken tokenizer optimized for 100+ languages |
Standard English prose averages ~1.33 tokens per word (3.9 to 4.2 characters per token).
A typical single-spaced 12pt document contains approximately 350 to 400 words (~500 tokens).
Syntax indentation, brackets, and camelCase symbols create higher token density than natural prose.
Frequently Asked Questions About Token Counting
How many words are in 1,000 tokens?
As a rule of thumb for standard English prose, 1,000 tokens is approximately 750 words (about 1.33 tokens per word). For technical documentation, JSON payloads, or source code, token counts increase to roughly 1.5 to 2.2 tokens per word due to punctuation, indentation, and variable naming conventions.
Why do Claude and OpenAI produce different token counts for the exact same text?
Every AI provider trains its tokenizer with a different vocabulary dictionary. OpenAI's o200k_base features a 200,000 token vocabulary, while Anthropic Claude uses an ~65,000 vocabulary. A larger vocabulary encodes longer character sequences into a single integer ID, producing fewer total tokens for common phrases and non-English scripts.
Does TokenMath send my prompts to a server?
No. TokenMath's Token Counter is 100% client-side. All tokenization, BPE chunk parsing, and character statistics run directly in your browser's WebAssembly and JavaScript threads. Zero prompts or code are ever transmitted, saved, or logged.
What is context headroom?
Context headroom is the remaining token capacity between your input prompt and the maximum context window supported by a model. For instance, if your prompt is 45,000 tokens and you use Claude Sonnet 5 (200,000 token window), you have 155,000 tokens of headroom remaining for multi-turn chat history, tool calls, and model generation.