Analyze your text with our comprehensive multilingual word counter. Count words, characters, sentences, and paragraphs in seconds. Estimate tokens for modern AI models like GPT-4 and Claude. Perfect for content creators, developers, writers, and students.
Basic Stats:
Words: 0
Characters: 0
Reading Time: 0 min
Tokens (GPT4): 0
Detailed Statistics:
Characters (with spaces)
0
Characters (no spaces)
0
Words
0
Sentences
0
Paragraphs
0
Reading Time
0 min
GPT-4 Tokens
0
Claude Tokens
0
Help others discover this tool by sharing it on your favorite platforms
The average English word is 4.7 characters long, but the most commonly used words (like "the," "and," "to") are much shorter.
Chinese can express the same content with roughly 30% fewer characters than English, while languages like German often use longer compound words, affecting word counts.
Most adults read at 200-250 words per minute for casual reading, but comprehension drops significantly beyond 400 words per minute.
When writing for AI systems, understanding token count is crucial—GPT-4 processes text in chunks called tokens, averaging 3.8 characters per token in English, while Claude uses slightly different tokenization methods.
Modern AI language models like GPT-4 and Claude use sophisticated tokenization algorithms that split text into manageable pieces called tokens.
English typically averages 3.5-4 characters per token in these models, but this varies by language and context.
Tokenizers use a combination of common words, subwords, and character sequences optimized for efficiency. For example, common English words like "the" are single tokens, while rare words might be split into multiple tokens.
Unicode characters, especially in languages like Chinese or Japanese, often become individual tokens. This is why estimating token counts requires language-specific approaches rather than simple character or word counting.
Splits text at word boundaries (spaces, punctuation). Simple but struggles with compound words and morphology.
Treats each character as a token. Works across languages but creates very long sequences and loses word-level meaning.
Creates overlapping sequences of n characters or words. Useful for capturing patterns but generates many tokens.
Uses linguistic rules to identify meaningful units. Accurate for specific languages but requires extensive language-specific knowledge.
Used by GPT models, it iteratively merges the most frequent character pairs to form a vocabulary of subword units, balancing vocabulary size and representation efficiency.
Used by BERT, similar to BPE but uses a likelihood-based approach rather than frequency. It starts with characters and builds up common subwords.
Used by models like T5, treats the text as a sequence of Unicode characters and applies BPE or unigram language modeling. Works well across languages without requiring pre-tokenization.
OpenAI's optimized tokenizer for GPT models, designed for speed and consistency. It implements BPE with additional optimizations for handling special tokens and encoding efficiency.
Our Word Counter is a comprehensive text analysis tool that counts words, characters (with and without spaces), sentences, paragraphs, and estimates reading time. It also provides token estimates for AI models like GPT-4 and Claude across multiple languages, with visualizations to help you better understand your text composition.
Tokens are the units that AI models process text in. Our tool estimates tokens based on character count and language specifics. Different languages have different tokenization rates - for example, English averages about 3.8 characters per token in GPT-4, while Chinese uses roughly 1.3 characters per token. The tool provides separate estimates for GPT-4 and Claude models.
Languages vary in how information is encoded. Character-based languages like Chinese, Japanese, and Korean typically use fewer characters to express the same information compared to alphabet-based languages like English or Spanish. Our tool accounts for these differences with language-specific token divisors.
The reading time estimate is based on an average reading speed of 200 words per minute, which is a standard approximation for casual reading. Actual reading times may vary based on content complexity, reader familiarity with the subject, and individual reading speeds.
Yes, you can upload text files (.txt, .md, .rtf) directly to analyze their content. Simply click the upload icon in the toolbar, select your file, and the content will be loaded and analyzed automatically.
You can download the current text by clicking the download icon in the toolbar. This will save your text as a .txt file. You can also copy the entire text to your clipboard by clicking the copy icon.
Yes, our Word Counter works for all languages, with specialized token estimation for English, Chinese, Japanese, Korean, Arabic, Russian, Spanish, French, and German. For other languages, the tool will use default English-based estimates.
Token counting is essential when working with AI models like GPT-4 or Claude, which have input limitations based on token count. Understanding the token count helps you optimize your prompts and ensure you stay within model limits. It's particularly useful for developers, content creators, and anyone working with AI text generation.
Yes, all text analysis happens directly in your browser. Your text is never sent to our servers, ensuring complete privacy and security, especially for sensitive content.
Sentences are detected by splitting the text at common sentence-ending punctuation marks (periods, exclamation points, and question marks). Paragraphs are identified by looking for empty lines between blocks of text. This method provides a good approximation, though complex formatting may affect accuracy.
The doughnut chart visualizes the relative distribution of words, sentences, and paragraphs in your text. This visual representation helps you quickly understand the composition of your content and can be useful for assessing text structure and readability.
Absolutely! The Word Counter is perfect for SEO content creation, helping you optimize content length for search engines. It provides word and character counts which are useful metrics for analyzing title tags, meta descriptions, and overall content length.
Character count without spaces is often used in certain publishing contexts, programming, and SMS messaging where space characters aren't counted toward character limits. It provides a more accurate representation of the actual data size in some applications.
For academic writing, our Word Counter helps you meet specific word count requirements for essays, dissertations, and research papers. The sentence and paragraph counts can also help you analyze your writing structure and ensure appropriate segmentation of ideas.
Yes! Translation services often charge by word count. Our tool gives you an accurate word count across multiple languages, helping you estimate translation costs before submitting content to translation services.