Enter text to see how it's tokenized
A free, browser-based token counter and tokenizer for every major language model — GPT-4o, Claude, Gemini, Llama 3, and more. Paste your text, pick your model, and get an accurate token count in real time. No account. No API key. No data ever leaves your device.
Every token is a cost. Paste your prompt, select your model, and see the exact count before you hit the API. Catch over-length prompts early, trim the fat, and build more efficient pipelines — whether you're working with GPT-4o, Claude 3.5 Sonnet, or Gemini 1.5 Pro.
LLM APIs charge per token — input and output. Before sending a long document, a system prompt, or a retrieval-augmented context, use this token counter to know exactly what you're spending. Counting tokens upfront is the easiest way to avoid unexpected API bills.
GPT-4o supports 128k tokens. Claude 3 handles 200k. But a large context window doesn't mean you should fill it — long contexts slow responses and increase costs. Use the token counter to see how much of your context budget a prompt consumes, and keep your inputs lean.
Using ChatGPT, Claude, or Gemini to write, summarize, or edit content? Many AI tools silently truncate inputs that are too long. Paste your content here first to check whether it fits — before the model misses half your brief.
Studying how language models process text? The visual tokenizer shows exactly how your input is split — token by token — so you can see why "tokenization" isn't the same as "word count," why some words take multiple tokens, and how the model's vocabulary shapes what it understands.
When building fine-tuning datasets, token count directly impacts training time, cost, and whether your examples fit within the model's maximum sequence length. Audit each example with this tokenizer before you submit your dataset — it's much easier to fix limits before training starts.
A token is the basic unit of text that a language model reads, processes, and generates. Tokens aren't the same as words — they can be whole words, parts of words, spaces, punctuation, or even single characters, depending on the model's tokenizer. For example, the word tokenization might be split into token + ization. As a rough guide: 1 token ≈ 4 characters, or about 750 words per 1,000 tokens in standard English.
Roughly 1,300–1,500 tokens for typical English prose. Code, JSON, and technical content tend to use more tokens per word because of special characters and less-common vocabulary. Conversational text lands closer to the 1,300 end; dense technical writing closer to 1,500 or more.
Three reasons: cost, limits, and quality.
Cost: LLM APIs charge per token — both input and output. Knowing your count before each call helps you manage spend, especially at scale.
Limits: Every model has a context window — the maximum number of tokens it can handle in one request. Exceed it and your input is silently truncated.
Quality: Very long prompts can cause models to "lose focus" on early instructions. Tighter, well-counted prompts tend to get more precise responses.
A tokenizer is the algorithm that converts raw text into tokens before it's passed to the model. Each model family uses its own tokenizer — OpenAI's GPT-4o uses o200k_base (200k-token vocabulary), GPT-4 and Claude use cl100k_base, and older GPT-2 / Mistral models use the gpt2 tokenizer. Different tokenizers produce different counts for the exact same text, which is why model-specific counting matters.
GPT-4o uses the o200k_base encoding — a newer, larger vocabulary that tokenizes common words more efficiently. Claude 3 closely approximates cl100k_base (the same encoding as GPT-4). For most everyday English text the difference is small, but for multilingual text, code, or emoji-heavy content, the encodings can diverge significantly. LLM Token Counter lets you switch between models so you always check the right count.
Yes — completely. LLM Token Counter runs entirely in your browser. The tokenizer library is bundled locally; no text is ever uploaded to a server. It works even without an internet connection once the page has loaded.
Word count and token count measure different things. A 500-word article is typically 650–750 tokens, but the exact number depends on punctuation density, vocabulary complexity, and the model's tokenizer. LLM APIs charge and limit by tokens, not words — so word count alone is an unreliable guide to cost or context length.
The color-coded visualizer breaks your text into individual tokens and highlights each one in a different color. This shows exactly how the model "reads" your input — where word boundaries fall, how punctuation is handled, and why some vocabulary items span multiple tokens. It's a practical way to understand tokenization beyond just a number.
A few practical techniques: remove filler phrases and redundant context; use shorter synonyms where possible; for structured data, prefer compact JSON over verbose XML; trim whitespace and repeated newlines (each counts as tokens); and break long documents into focused chunks rather than sending everything at once. The visualizer makes it easy to spot where your prompt is token-heavy.