AI Token Tools
Llama Token Counter — Count Tokens Online Free
Count how many tokens your text uses with Llama. Paste or type below and the token count updates live — perfect for staying inside the Llama context window, trimming prompts and estimating API cost before you send a request.
- Developer
- Meta
- Context window
- 128K tokens
- Tokenizer
- Llama BPE (estimated here)
- Token accuracy here
- Close estimate
About this tool
A token is the basic unit a language model reads. It is usually a short piece of a word — in English, one token is roughly 4 characters or about ¾ of a word, so "tokenizer" might be two or three tokens. Llama measures everything in tokens: both the context window (how much text fits in one request) and API billing are counted this way.
Knowing the Llama token count before you send a prompt has three big benefits: you avoid hitting the context-window limit and getting truncated, you can predict the cost of an API call, and you can trim long prompts so the model has more room to answer.
This Llama token counter runs entirely in your browser — your text is never uploaded or stored. OpenAI (GPT) counts use the exact tiktoken encoding; Llama is shown as a close estimate because its official tokenizer is not published to run client-side. For ordinary prose the estimate is typically within a few percent of the real value.
How Llama turns text into tokens
Llama does not read words or letters directly — it splits text into tokens using a sub-word tokenizer (Llama BPE (estimated here)). Common words often become a single token, while rare or long words, emoji and code are split into several. Spaces and punctuation count too, which is why "hello world" and "helloworld" can produce different token counts.
Non-English text usually uses more tokens per character. Chinese, Japanese, Korean and Thai are especially dense — a single character can be one or more tokens — so the same meaning can cost noticeably more tokens than in English.
Llama context window and token limit
Llama has a context window of about 128K tokens, shared between your input (prompt, system message, history and attachments) and the model's output. If the total exceeds the window, the oldest content is dropped or the request is rejected — counting tokens first prevents that.
A practical tip: leave headroom for the answer. If you need a long reply, keep the prompt well under the limit so the model has tokens left to respond.
Tips to use fewer Llama tokens
Remove redundant instructions and boilerplate, summarise long context instead of pasting it whole, drop unnecessary examples, and avoid repeating the same system prompt every turn. Trimming tokens both speeds up responses and lowers your bill.
How to use
- Paste your text — Type or paste any prompt, document or code into the box.
- Read the token count — The big number is the Llama token count, updating live, with characters and words next to it.
- Compare models — Use the table to compare the count against other models side by side.
- Copy or clear — Copy your text to use elsewhere, or clear it and start again.
Frequently asked questions
How many tokens is my text in Llama?
Paste it into the box above — the counter shows the Llama token count instantly, along with the character and word counts.
Is the Llama token count exact?
It is a close estimate. Llama's official tokenizer is not published to run in the browser, so we approximate it; for normal text it is usually within a few percent. OpenAI/GPT counts in the table are exact.
What is the Llama context window?
About 128K tokens, shared between your input and the model's output. Keep the total below this limit to avoid truncation.
How do I count Llama tokens online for free?
This page is a free online Llama token counter — no account, no install. Everything is computed in your browser.
Why do tokens matter for Llama?
Llama bills per token and limits requests by tokens, so the token count decides both whether your prompt fits and what it costs.
Is my text sent to a server?
No. Counting happens entirely in your browser — nothing is uploaded, logged or stored.
How can I reduce my token usage?
Shorten prompts, summarise long context, remove repeated instructions and trim examples. Fewer tokens means faster, cheaper requests.