Token Counter
AI token counter online. Count tokens for GPT-4, Claude, Gemini and more. Character count and cost estimation—free LLM token calculator.
Enter Text
Tokens 🔢
0
0.0% of context
Characters 📝
0
0 without spaces
Words 📄
0
0 lines
Context 📊
270K
tokens remaining
Context Window Usage 0 / 270K
💰 Estimated Cost approx.
openaiInput cost $0.00
Token price $2.5/1M in
Prices updated: 2026-07-08. Check provider sites for current rates.
About Token Counting
- • Tokens are pieces of text that AI models process (words, subwords, or characters)
- • Different models have different tokenization methods
- • Claude/Gemini counts are approximations using similar encoding
- • Context window = max tokens for input + output combined
Features
- Highly accurate token counting for modern LLMs including GPT-4o, Claude 3.5, and Gemini
- Real-time, instant character, word, and token calculations as you type
- Built-in API cost estimation based on the latest provider pricing tiers
- Support for multiple tokenizer encodings (cl100k_base for OpenAI, custom variants)
- Precise whitespace, punctuation, and special character parsing
- Pasting support for massive prompts without browser lag
Common Use Cases
- Accurately forecasting API budget consumption before launching large-scale LLM processing jobs
- Ensuring complex multi-shot prompts strictly adhere to strict context window limitations
- Comparing token efficiency between different languages (e.g., English vs. Japanese token density)
- Optimizing code snippets to minimize API spend by removing redundant indentation
- Evaluating text datasets to determine exact storage requirements for vector embeddings
Understanding Tokens vs. Words
Large Language Models (LLMs) do not read or generate text word-by-word or character-by-character. Instead, they process text in chunks called tokens. A token can be an entire word, a syllable, a single character, or even a space.
The Industry Standard Rule of Thumb: 1,000 tokens is approximately equal to 750 English words. This means, on average, a single token equates to roughly 4 characters or 0.75 words. However, this ratio degrades significantly for non-English languages and code, which require far more tokens per word.
Common Tokenizer Encodings:
- cl100k_base: The modern standard used by OpenAI for GPT-4, GPT-4o, and the
text-embedding-3models. It is highly efficient, utilizing a massive vocabulary of 100,000 unique tokens. - p50k_base / r50k_base: Older legacy encodings used for GPT-3 and early Codex models, which were less efficient at packing text into smaller token counts.
- Llama / Mistral Tokenizers: Open-source models use their own custom SentencePiece or BPE tokenizers, meaning a prompt sent to Llama 3 will yield a slightly different token count than the exact same prompt sent to GPT-4.
Examples
Valid - Common English Phrase
"Hello, world!"
Tokens: 4 (Hello)(,)( world)(!) Valid - Complex Multisyllabic Word
"Indivisibility"
Tokens: 3 (Ind)(ivis)(ibility) - Note how rare words are fragmented. Valid - Source Code Snippet
def hello_world():
print("Hello")
Code often consumes more tokens due to spaces, brackets, and underscores.Frequently Asked Questions
Why do token counts vary drastically between different AI models?
Different AI providers train their models using entirely different vocabularies (known as tokenizers). OpenAI uses `cl100k_base` for GPT-4, which has a 100,000-token dictionary, allowing it to efficiently map whole words to single tokens. Older models or open-source models with smaller dictionaries must break down the same words into multiple sub-word tokens, resulting in a higher total count.
Do invisible characters like whitespace and newlines count as tokens?
Yes, absolutely. Every single space, tab, and newline character is mapped to a token. In modern BPE (Byte-Pair Encoding) tokenizers, a leading space is often merged with the word that follows it (e.g., " apple"). However, consecutive spaces (like code indentation) can rapidly eat up your token budget.
How accurate is this online token calculator?
This tool leverages exact algorithmic ports of the official tokenization libraries (such as OpenAI's `tiktoken`), guaranteeing near 100% precision for supported models before you make costly API calls.
Why is non-English text so much more expensive to process?
Because tokenizers are primarily trained on English datasets, they have a dedicated single token for common English words (like "computer"). For languages like Japanese, Arabic, or Hindi, the tokenizer rarely has whole-word representations and must fall back to encoding the text character-by-character or byte-by-byte, heavily inflating the token count and API cost.
💡 Tips
- To drastically reduce your API spend, utilize a minifier on JSON data or code snippets before sending them to the LLM to strip out expensive whitespace tokens.
- If deploying to international users, budget for 2x to 3x higher token consumption for non-Latin character languages.