Question 1

Which tokenizer does Meta use?

Accepted Answer

Llama 3 models use the official open Llama 3 tokenizer, which this tool runs in your browser for exact counts. Newer Meta models such as Llama 4 and Muse Spark use a different tokenizer that is not available in the browser, so those counts are estimated with OpenAI's cl100k_base encoding.

Question 2

Are these Meta token counts exact or estimated?

Accepted Answer

Mixed. Counts for Llama 3 models are exact because the official Llama 3 tokenizer is open and runs in your browser. Counts for other Meta models are estimates made with the cl100k_base encoding and are marked with a tilde (~) in the results.

Question 3

How is Meta API cost calculated?

Accepted Answer

Cost equals tokens divided by one million, multiplied by the model's per-million-token rate. Each Meta model has separate input and output rates, and this page shows both: what your text would cost as a prompt (input) and what the same number of tokens would cost as a response (output).

Question 4

Why do token counts matter for Meta API costs?

Accepted Answer

Meta bills API usage per token, not per word or character. A token is roughly 4 characters or about three quarters of a word in English. Knowing the token count of your prompts lets you predict spend, stay inside context windows, and compare Meta models like Muse Spark 1.1, Llama 4 Maverick and Llama 4 Scout before you commit.

Model	Tokens	Input Cost	Output Cost
Muse Spark 1.1	—	—	—
Llama 4 Maverick	—	—	—
Llama 4 Scout	—	—	—
Llama 3.3 70B	—	—	—

Model	Input /1M	Output /1M	Context
Muse Spark 1.1	$1.25	$4.25	1.0M
Llama 4 Maverick	$0.270	$0.850	1.0M
Llama 4 Scout	$0.080	$0.300	1.0M
Llama 3.3 70B	$0.180	$0.180	131K

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