Tokenとは何か
tiktokenライブラリを使って実際のテキストをtokenizeし、単語、句読点、空白がモデルごとにどのようなtoken列に対応するかを確認します。
「Tokenとは何か」はCoddyKit上の無料AI Engineering Academyレッスンです。 これはレッスン1/4です。 下記で完全なレッスンを無料で読むことができます。その後、ブラウザ内の組み込みコードエディタと24時間対応のAIチューターでハンズオン演習できます。 これはAI Engineering Academy学習パスの一部であり、ウェブとCoddyKitアプリ全体で進捗が同期されます。 AI Engineering Academyコースには全4レッスンが含まれています。
このレッスンの一部はまだ翻訳されておらず、英語で表示されています。
LLMs Process Tokens, Not Words
When you send text to an LLM, the model does not see characters or words — it sees tokens. A token is a chunk of text that the model's vocabulary maps to a single integer ID. Tokens can be whole words, parts of words, punctuation, or whitespace depending on the tokenizer.
Understanding tokens is practically important because OpenAI's pricing is per token, the context window is measured in tokens, and the maximum response length is controlled by max_tokens. Surprises in cost and behavior almost always trace back to misunderstanding how your text tokenizes.
How Tokenization Works: BPE
GPT models use Byte Pair Encoding (BPE) tokenization. BPE starts with a vocabulary of individual bytes and iteratively merges the most frequent adjacent pairs until it reaches the desired vocabulary size. OpenAI's GPT models use a vocabulary of about 100,000 tokens.
The result is that common words become single tokens (hello is one token), while rare or made-up words are split into multiple subword tokens (subaqueous might become three tokens: sub, aque, ous). This lets the model handle any text, even words it has never seen, by composing them from smaller familiar pieces.
Using tiktoken to Count Tokens
OpenAI provides the tiktoken library to tokenize text locally without making an API call. This is essential for counting tokens before sending a request to estimate cost and verify you are within the context window.
import tiktoken
# Get the encoding for a specific model
enc = tiktoken.encoding_for_model('gpt-4o')
text = 'Hello! How many tokens does this sentence use?'
tokens = enc.encode(text)
print(f'Text: {text}')
print(f'Token count: {len(tokens)}')
print(f'Token IDs: {tokens}')
# You can also decode tokens back to text
decoded = enc.decode(tokens)
print(f'Decoded: {decoded}')
# Inspect individual token strings
for token_id in tokens:
token_str = enc.decode([token_id])
print(f' Token {token_id}: "{token_str}"')Practical Token Counts
A useful rule of thumb: 1 token ≈ 4 characters of English text, or about 0.75 words. So 1,000 tokens is roughly 750 words or 3-4 pages of text. However, this ratio varies significantly:
- Common English words: ~1 token each
- Uncommon technical terms: 2-4 tokens each
- Numbers: often 1 digit per token (so '12345' is 5 tokens)
- Code: varies widely, but Python is typically efficient at ~1-2 tokens per symbol
- Non-Latin scripts (Chinese, Arabic): often 1 token per character, making them much more expensive per word than English
Tokenizing Different Content Types
Let us explore how token counts differ dramatically between content types using tiktoken.
import tiktoken
enc = tiktoken.encoding_for_model('gpt-4o')
examples = {
'English sentence': 'The quick brown fox jumps over the lazy dog.',
'Number sequence': '1234567890',
'Python code': 'def fibonacci(n):\n if n <= 1:\n return n\n return fibonacci(n-1) + fibonacci(n-2)',
'Technical jargon': 'autoregressive transformers tokenization subword BPE',
'URL': 'https://api.openai.com/v1/chat/completions',
'Chinese text': '大型语言模型使用令牌处理文本',
}
for label, text in examples.items():
count = len(enc.encode(text))
ratio = len(text) / count
print(f'{label}: {count} tokens ({ratio:.1f} chars/token)')How Chat Messages Are Tokenized
The total tokens in a chat API call include not just the text content of your messages but also the formatting overhead added by the chat template. Every message has a few extra tokens for the role label and delimiters. OpenAI's documentation specifies the formula:
- Each message adds approximately 4 tokens for formatting overhead
- Every reply starts with 3 additional tokens for the assistant role priming
For short conversations this overhead is negligible, but for systems managing long conversations it adds up. The tiktoken library provides helper functions to count tokens for a full message array correctly.
import tiktoken
def count_messages_tokens(messages, model='gpt-4o'):
enc = tiktoken.encoding_for_model(model)
# 4 tokens per message (role + content structure), 3 for reply priming
total = 3
for msg in messages:
total += 4
for key, value in msg.items():
total += len(enc.encode(str(value)))
return total
messages = [
{'role': 'system', 'content': 'You are a helpful assistant.'},
{'role': 'user', 'content': 'What is the capital of France?'},
{'role': 'assistant', 'content': 'The capital of France is Paris.'},
{'role': 'user', 'content': 'And what is the population?'},
]
print(f'Total tokens: {count_messages_tokens(messages)}')Token Limits by Model
Each model has a maximum context window measured in tokens. As of 2025, representative limits include:
- gpt-4o: 128,000 tokens context, up to 16,384 output
- gpt-4o-mini: 128,000 tokens context, up to 16,384 output
- Claude 3.5 Sonnet: 200,000 tokens context
- Gemini 1.5 Pro: 1,000,000 tokens context
The sum of input tokens plus output tokens must not exceed the context window. If you exceed it, you get a context_length_exceeded error. Always verify token count before sending requests for long documents.
Tokens and Pricing
OpenAI charges separately for input tokens (the prompt you send) and output tokens (the response you receive). Output tokens are typically 3-4x more expensive than input tokens because they require sequential generation. As of early 2025, illustrative pricing for gpt-4o-mini is approximately $0.15 per million input tokens and $0.60 per million output tokens.
This means a 2,000-token prompt with a 500-token response costs approximately ($0.15 × 2/1000 + $0.60 × 0.5/1000) = $0.00060 per request. At 10,000 requests per day that is $6/day — manageable, but it scales with usage. Caching, model routing, and token minimization all become important at production scale.
Reducing Token Count Without Losing Meaning
Shorter prompts cost less and leave more room for the model's response. Common token-reduction techniques include:
- Remove redundant instructions: 'Please be so kind as to...' → 'Please...'
- Compress examples: Use the minimum number of words in each few-shot example
- Abbreviate field names in structured prompts: Use 'Q:' and 'A:' instead of 'Question:' and 'Answer:'
- Use JSON rather than prose for structured context: JSON is more token-efficient than prose descriptions of the same data
Run tiktoken before and after any compression to verify you actually saved tokens — some 'compressions' counterintuitively increase token count.
Special Tokens and Control Tokens
In addition to text tokens, the model's vocabulary includes special tokens used to structure inputs. Common examples are <|endoftext|> (marks the end of a document), <|im_start|> and <|im_end|> (chat message delimiters in the ChatML format used internally).
You do not need to manage these directly when using the OpenAI API — the SDK handles them for you. But awareness of their existence explains why sometimes a request with 'empty' messages still consumes a few tokens. Special tokens are also why you should never construct raw strings with <|...|> patterns in user input without sanitizing, since they could interfere with the model's input formatting.
Always Count Before You Send
The practical takeaway from this lesson: always count tokens before sending any request that involves dynamically assembled prompts. Write a small helper function that wraps tiktoken, and call it at the point where you assemble the messages array. Log the token count so you can monitor it over time.
For RAG systems, this is especially important: the retrieved chunks you inject into the prompt can vary widely in size, and you need to ensure the total stays within the model's context window. We will build exactly this kind of token-aware context assembly in the RAG pipeline lessons.
import tiktoken
def token_count(text, model='gpt-4o'):
enc = tiktoken.encoding_for_model(model)
return len(enc.encode(text))
def safe_assemble_prompt(system, user_content, max_tokens=120000):
combined = system + user_content
count = token_count(combined)
if count > max_tokens:
raise ValueError(
f'Prompt too long: {count} tokens (limit {max_tokens})'
)
return [{'role': 'system', 'content': system},
{'role': 'user', 'content': user_content}]Quick Check
Test your understanding of AI Engineering concepts from this lesson.
Lesson Recap
In this lesson you learned: LLMs process text as tokens, not words, using BPE tokenization with a vocabulary of ~100,000 entries, the tiktoken library lets you count tokens locally before making API calls to estimate cost and check context limits, and pricing is per token with output tokens costing significantly more than input tokens. Next up we explore context windows: what they are, how they limit your application, and how different models compare.
よくある質問
「Tokenとは何か」レッスンは無料ですか？
はい。「Tokenとは何か」の完全なテキストはこのウェブで無料で読めます。インタラクティブに演習し（組み込みコードエディタと24時間対応のAIチューター）、AI Engineering Academyコースの残りをアンロックするには、CoddyKit PROにアップグレードしてください。 AI Engineering Academyコースには全4レッスンが含まれています。
「Tokenとは何か」で何を学びますか？
tiktokenライブラリを使って実際のテキストをtokenizeし、単語、句読点、空白がモデルごとにどのようなtoken列に対応するかを確認します。 ブラウザで直接実行するハンズオンコードでAI Engineering Academyを演習し、24時間対応のAIチューターがレッスンを進める中での質問に答えます。
AI Engineering Academyを始めるのに経験は必要ですか？
事前経験は必要ありません。CoddyKitのAI Engineering Academyは初級者から上級者向けに構成されているため、ここから始めるか最初から始めて、自分のペースで進むことができます。 これはレッスン1/4です。
「Tokenとは何か」レッスンにはどのくらい時間がかかりますか？
ほとんどのCoddyKitレッスンは約5～10分かかります。各レッスンはコンパクトでインタラクティブなので、着実に進歩し、ウェブとアプリ全体で正確に前回の場所から再開できます。
このAI Engineering Academyレッスンでコードを書いて実行できますか？
はい。すべてのAI Engineering Academyレッスンに組み込みコードエディタが含まれているため、ブラウザでリアルコードを書いて実行し、即座のAIフィードバックを取得できます。ローカル設定は不要です。