Tokenizers by Model Intermediate

Each model family uses its own tokenizer with different vocabulary sizes, algorithms, and efficiency characteristics. Understanding these differences helps you estimate costs and choose the right model for your use case.

OpenAI Tokenizers

OpenAI has released several tokenizer versions, each improving on the last:

Anthropic (Claude) Tokenizer

Anthropic uses a custom tokenizer for Claude models. While the exact details are not fully public, key characteristics include:

• Algorithm: Byte-level BPE variant
• Vocabulary size: Approximately 100,000+ tokens
• Strong multilingual support with good efficiency across languages
• Optimized for code with common programming patterns as tokens
• Consistent across Claude 3, 3.5, and 4 families

# Counting tokens for Claude using the Anthropic SDK
from anthropic import Anthropic

client = Anthropic()
# Token counting is available via the API
# The exact tokenizer is handled server-side
response = client.messages.count_tokens(
    model="claude-sonnet-4-20250514",
    messages=[{"role": "user", "content": "Hello, how are you?"}]
)
print(f"Input tokens: {response.input_tokens}")

Google (Gemini) Tokenizer

Google's Gemini models use a SentencePiece-based tokenizer with a large vocabulary optimized for multilingual performance:

• Algorithm: SentencePiece (Unigram + BPE hybrid)
• Vocabulary size: 256,000 tokens (Gemini 1.5+)
• Excellent multilingual efficiency - the largest vocabulary among major models
• Language-agnostic preprocessing - no language-specific rules

Meta (LLaMA) Tokenizer

Meta's LLaMA family has evolved its tokenizer significantly across versions:

Tokenizer Comparison

How to Check the Tokenizer for Any Model

# For OpenAI models
import tiktoken

# Get the tokenizer for a specific model
enc = tiktoken.encoding_for_model("gpt-4o")
print(f"Encoding: {enc.name}")  # o200k_base

enc = tiktoken.encoding_for_model("gpt-4")
print(f"Encoding: {enc.name}")  # cl100k_base

# For Hugging Face models (LLaMA, Mistral, etc.)
from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B")
print(f"Vocab size: {tokenizer.vocab_size}")

# Tokenize and inspect
tokens = tokenizer.tokenize("Hello, how are you?")
print(f"Tokens: {tokens}")