Llama Token Counter -- Meta LLM Tokens

Llama Token Counter

Llama Token Counter estimates how many tokens your text will use with Meta's open-source Llama models. Llama 3+ uses SentencePiece BPE with a 128K vocabulary (~4.0 characters per token). Since Llama is open-source, pricing depends on your hosting provider.

Hosting Providers and Pricing

Since Llama is open-source, you can choose where to run it. Here is pricing from major API providers (per 1M tokens):

Llama Model Overview

How Llama Tokenization Works

Llama uses SentencePiece BPE (Byte Pair Encoding) for tokenization. The most significant change between Llama versions was the vocabulary size increase from 32K (Llama 2) to 128K (Llama 3+).

Llama 2 vs Llama 3 vs Llama 4 Tokenization

• Llama 2 (32K vocab): ~3.5 characters per token. The smaller vocabulary means more text gets split into subword pieces, resulting in higher token counts.
• Llama 3/3.1/3.3 (128K vocab): ~4.0 characters per token. The 4x larger vocabulary captures more common words and phrases as single tokens, reducing overall token count by ~15%.
• Llama 4 (128K+ vocab): Similar efficiency to Llama 3 with a Mixture-of-Experts architecture. Scout supports an extended 10M token context window.

Counting Tokens Locally

Since Llama is open-source, you can count tokens locally using the transformers library:

from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.3-70B")
tokens = tokenizer.encode("Hello, world!")
print(len(tokens))  # exact count

Frequently Asked Questions

Is Llama free to use?

The Llama model weights are free to download and use under Meta's license. However, running inference requires GPU hardware. You can use hosted API providers like Groq or Together AI for pay-per-token access, or self-host on your own GPUs.

What tokenizer does Llama use?

Llama 3 and newer use SentencePiece BPE with a 128K token vocabulary. Llama 2 used a smaller 32K vocabulary. The larger vocabulary makes Llama 3+ about 15% more token-efficient for English text.

What is the difference between Llama 2 and Llama 3 tokenization?

Llama 3 quadrupled the vocabulary from 32K to 128K tokens. This means the same text uses ~15% fewer tokens with Llama 3, resulting in faster inference and more efficient context usage. Llama 2 averages ~3.5 chars/token vs Llama 3's ~4.0 chars/token.

How to count Llama tokens locally?

Use the transformers Python library: pip install transformers, then load the tokenizer with AutoTokenizer.from_pretrained("meta-llama/Llama-3.3-70B"). This gives exact token counts matching what the model actually uses.

What is Llama 4 Scout vs Maverick?

Both are Mixture-of-Experts (MoE) models with 17B active parameters. Scout uses 16 experts and supports an extended 10M token context window. Maverick uses 128 experts for higher quality output. Both have a native 128K context window.

How much does it cost to run Llama?

API costs vary by provider. Groq offers Llama 4 Maverick at $0.20/1M input and $0.60/1M output. Together AI charges $0.27/$0.85. Self-hosting is free for the model but requires GPU hardware (A100/H100 GPUs for larger models).

Token Counters by Provider