Llama Token Counter

Estimate how many tokens a piece of text uses with Meta's Llama models, and what a request will cost on a hosted endpoint. Llama 3 onwards uses a tiktoken-based BPE tokenizer with a 128,256-token vocabulary — much larger than Llama 2's 32K SentencePiece vocab, so the same text uses noticeably fewer tokens. Paste text, pick a model size, and adjust the per-million prices to match your provider. Everything is computed locally.

Default prices are representative Together AI rates for serverless Llama (≈ same in/out); Groq, Fireworks, and self-hosting differ. Edit the price fields to match your provider, and use the API's usage field for exact billing.

How to use the Llama Token Counter

Paste your text and choose a model size. The token estimate updates live, along with the characters-per-token ratio and how much of the model's 128K context window the input fills. Enter your expected output length and call volume, and set the input/output prices per million tokens for your host — the defaults are representative serverless rates, but open-weight Llama is sold by many providers at different prices, so always check yours.

Because Llama is open-weight, "cost" is whatever your endpoint charges, not a single official rate. If you self-host on your own GPU the marginal token cost is effectively zero (you pay for the hardware instead) — use the per-call and per-month figures here mainly to compare hosted providers or to budget against a self-hosted alternative.

How the Llama 3 tokenizer works

Llama 3, 3.1, and 3.3 all share the same tokenizer: a byte-pair-encoding (BPE) model built on the same tiktoken library OpenAI uses, with a vocabulary of 128,256 tokens. This was a big jump from Llama 2's 32,000-token SentencePiece vocabulary. A larger vocabulary means more whole words and common sub-words have their own single token, so a given English sentence is encoded in fewer tokens — Llama 3 is roughly 15% more token-efficient than Llama 2 on English text, and much more so on code and non-English languages.

In practice English text runs around 3.9 characters per token with the Llama 3 tokenizer, very close to GPT-4o's o200k_base. That ratio is what this tool uses to estimate counts. The real tokenizer is deterministic BPE — it greedily merges byte pairs according to a learned merge table — so exact counts depend on the specific words, whitespace, and punctuation, which is why an estimate is within a few percent rather than exact.

All current Llama 3.x models expose a 128K-token context window, the sum of your input tokens and the tokens the model generates. The 8B model suits cheap high-volume tasks, 70B (especially the improved 3.3 release) is the workhorse that rivals much larger closed models, and 405B is the frontier-class option. Because the tokenizer is identical across sizes, your token count is the same regardless of which model you pick — only the price and quality change.

Common use cases

  • Budgeting hosted Llama. Compare what a prompt will cost across providers by editing the price fields.
  • Context-window planning. Check whether a long document plus its expected answer fits inside 128K tokens.
  • Self-host vs API. Estimate the monthly API spend you would avoid by running Llama on your own GPU.
  • Prompt trimming. See how editing a system prompt changes the token count before you ship it.

Frequently asked questions

Is this an exact token count?
It is a close estimate. Llama 3 uses a deterministic tiktoken BPE tokenizer, but counting exactly requires running that tokenizer with its full merge table. This tool uses a character-and-punctuation heuristic calibrated to the Llama 3 vocabulary, accurate to within a few percent for English. For exact billing, read the usage field the API returns with each completion.

Do Llama 3.1 and 3.3 tokenize differently?
No. Llama 3, 3.1, and 3.3 all use the identical 128,256-token tokenizer, so the token count for a given text is the same across them. Only quality, speed, and price differ between the sizes and releases.

Why does Llama 3 use fewer tokens than Llama 2?
Llama 3 quadrupled the vocabulary from 32K to 128K tokens. A bigger vocabulary lets more words and sub-words map to a single token, so the same text encodes into fewer tokens — typically about 15% fewer for English and considerably fewer for code and other languages.

How much does Llama cost to run?
There is no single price because Llama is open-weight and sold by many hosts. Serverless providers charge per million tokens (often the same rate for input and output), dedicated deployments charge per GPU-hour, and self-hosting costs only your hardware and power. The editable price fields let you model whichever applies.

What is the context window?
All current Llama 3.x models support a 128,000-token context window, shared between your input and the generated output. This tool shows what percentage of that window your input text fills so you can tell whether a long prompt will fit.