Gemma Token Counter

Estimate tokens and cost for Google's open Gemma 2 models. Gemma shares its tokenizer with Gemini: a SentencePiece model with a very large 256,128-token vocabulary. That big vocabulary means the same text usually encodes into fewer tokens than with Llama or GPT, so the characters-per-token ratio is higher. Paste text, choose a size, and the token count, context-window usage, and cost update live in your browser.

Gemma is open-weight: it is free to run in Google AI Studio and on your own hardware. Default prices are representative third-party serverless rates (e.g. Together AI) — edit to match your host.

How to use the Gemma Token Counter

Paste your text and pick a model size. The token estimate, characters-per-token ratio, and context-window usage update as you type. Because Gemma's context window is only 8K tokens — small by 2026 standards — the context-usage meter is the most important number here for long prompts. Set your expected output and monthly volume, and adjust the prices to match your provider.

Gemma is open-weight, so there is no single official per-token price. You can run it free in Google AI Studio, self-host the weights, or use a third-party serverless host that bills per million tokens. The editable price fields let you model whichever path you take; leave them at zero if you self-host and only care about the token count.

Gemma's tokenizer and its small context window

Gemma uses the same SentencePiece tokenizer as Google's Gemini models, with a vocabulary of 256,128 tokens. This is one of the largest vocabularies in any widely used model — roughly double Llama 3's and eight times the 32K vocabularies of older open models. A vocabulary that large captures many whole words, word pieces, and multilingual fragments as single tokens, so text encodes into fewer tokens overall. In practice that pushes the English ratio up to around 4.3–4.5 characters per token, higher than the ~3.9 of Llama and GPT-4o, which is the ratio this estimator uses.

The trade-off for a big vocabulary is a larger embedding table and slightly more memory, but for token-counting purposes the practical effect is simply that the same prompt costs fewer tokens on Gemma than on a small-vocabulary model. If you are porting prompts from Llama, expect the Gemma token count to come out somewhat lower for the same text.

The most important practical limit for Gemma 2 is its 8,192-token context window. That is small compared with the 128K windows of Llama 3 and Qwen, and it is shared between the prompt and the output. Long documents, lengthy chat histories, or large retrieved contexts can overflow it quickly, so this tool highlights the percentage of the 8K window your input fills — the quickest way to catch a prompt that will not fit before you send it.

Common use cases

  • Fitting the 8K window. Gemma 2's context is small — check your prompt fits before sending.
  • Porting from Llama. See how Gemma's large vocabulary lowers the token count for the same text.
  • Self-hosting budgets. Count tokens for throughput planning when running Gemma on your own GPU.
  • Serverless costing. Estimate per-call cost on a hosted Gemma endpoint by editing the prices.

Frequently asked questions

Why does Gemma use fewer tokens than Llama?
Gemma's SentencePiece vocabulary has 256,128 tokens — about double Llama 3's 128K. A larger vocabulary maps more words and word-pieces to single tokens, so the same text encodes into fewer tokens. The practical English ratio is around 4.4 characters per token versus Llama's ~3.9.

What is Gemma 2's context window?
Gemma 2 models have an 8,192-token context window, shared between input and output. That is small relative to the 128K windows common in 2026, so long prompts, chat histories, or retrieved documents can overflow it. This tool shows what fraction of the 8K window your input uses.

Is the token count exact?
No — it is a heuristic estimate calibrated to about 4.4 characters per token for English, within a few percent. Exact counts require the real SentencePiece tokenizer. In Google AI Studio you can also use the count-tokens endpoint for an authoritative number.

How much does Gemma cost?
Gemma is open-weight, so it is free to run in Google AI Studio and free to self-host aside from hardware. Third-party serverless hosts bill per million tokens at varying rates. The editable price fields let you model any of these; set them to zero if you only want the token count.

Does Gemma share Gemini's tokenizer?
Yes. Gemma uses the same SentencePiece tokenizer and 256K vocabulary as Google's Gemini models, so token counts are comparable between them for the same text, even though Gemini offers much larger context windows.