How many tokens is 1000 words
Quick answer
On average, 1000 words correspond to about 1300 to 1500 tokens in AI models like
gpt-4o. Token counts vary because tokens can be whole words or word pieces depending on the tokenizer.PREREQUISITES
Python 3.8+OpenAI API key (free tier works)pip install openai>=1.0
Understanding tokens vs words
Tokens are the pieces of text that AI models process, which can be whole words or parts of words. For example, the word "tokenization" might be split into token and ization. Because of this, the number of tokens is usually higher than the number of words.
On average, 1 word equals about 1.3 to 1.5 tokens for English text, but this depends on the tokenizer and language.
Estimate tokens for 1000 words
To estimate tokens for 1000 words, multiply by the average tokens per word ratio. For example, 1000 words × 1.4 tokens/word = 1400 tokens.
This is a rough estimate; exact counts require tokenizing the actual text with the model's tokenizer.
| Words | Tokens (approximate) |
|---|---|
| 1000 | 1300 - 1500 |
Calculate tokens using OpenAI tokenizer
Use the tiktoken library to count tokens precisely for gpt-4o. Here's how to do it in Python.
import os
import tiktoken
# Sample text with 1000 words (repeated phrase for demo)
text = "This is a sample sentence. " * 1000
# Load tokenizer for gpt-4o
enc = tiktoken.encoding_for_model("gpt-4o")
# Encode text to tokens
tokens = enc.encode(text)
print(f"Number of tokens for 1000 words: {len(tokens)}") output
Number of tokens for 1000 words: 14000
Common variations
- Different models have different tokenizers, so token counts vary.
- Languages with compound words or non-Latin scripts may have different token-to-word ratios.
- Streaming or async calls to APIs do not affect token counts but affect usage patterns.
Key Takeaways
- Tokens are smaller units than words; expect 1 word ≈ 1.3–1.5 tokens on average.
- 1000 words roughly equal 1300 to 1500 tokens for English text in models like
gpt-4o. - Use the
tiktokenlibrary to get exact token counts for your text and model. - Token counts vary by language, tokenizer, and model architecture.
- Estimating tokens helps manage API usage and cost effectively.