AVAILABLE FOR WEB, FLUTTER & QA PROJECTS
How to use this tool
1. Input Words or Paste Content
Type a target word count (e.g., 500, 2000, 5000) or paste your actual drafted article or prompt.
2. Choose Tokenizer Profile
Select OpenAI GPT-4o / GPT-4 (o200k/cl100k), Anthropic Claude, or Google Gemini.
3. Analyze Context Window Capacity
View the percentage of context utilized across 8k, 32k, 128k, and 1M token windows.
4. Copy Token Specs
Export the min, average, and max token boundaries to inform your API max_tokens parameter.
Formula or logic used
Words-to-Tokens Multiplier Logic
Converting words to tokens requires applying a statistical expansion multiplier. In English prose, each word contains roughly 1.33 tokens. Specialized vocabularies and punctuation push this factor higher.
General Prose: Tokens = Words × 1.333 (1,000 words ≈ 1,333 tokens)Technical Writing & Markdown: Tokens = Words × 1.45 (1,000 words ≈ 1,450 tokens)Programming Code: Tokens = Words × 1.80 (1,000 words ≈ 1,800 tokens)Context Window Utilization % = (Estimated Tokens / Context Window Limit) × 100Character Estimation = Words × 5.1 characters per word (including spaces)
Examples
Example 1: 1,000 Words to Tokens
Standard editorial content reliably conforms to the 1.33x multiplier in OpenAI and Anthropic tokenizers.
Example 2: 5,000 Words Comprehensive Guide
Technical terminology and markdown heading syntax increase tokenization density to ~1.45x.
Common use cases
API Parameter Setting (`max_tokens`)
Determine safe max_completion_tokens boundaries so LLM outputs are not prematurely cut off.
Batch Processing Cost Planning
Calculate total tokens for 10,000 database articles before running automated summarization.
Context Window Fit Verification
Verify whether customer support transcripts exceed prompt token budgets.
Translation & Multilingual Planning
Anticipate token expansion when translating English source documents into other languages.
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Frequently asked questions
How many tokens is 1,000 words in GPT-4o?
1,000 English words in GPT-4o is approximately 1,300 to 1,350 tokens. Thanks to GPT-4o's o200k_base tokenizer, it uses roughly 5% fewer tokens than older GPT-3.5 tokenizers on standard English text.
How many words can fit in a 128k context window?
A 128k token context window can accommodate approximately 96,000 English words. This equals roughly 190 single-spaced pages or an entire medium-length book.
Why does prompt text take more tokens than pure word count?
Tokenizers do not split solely on whitespace. Punctuations, markdown symbols (##, ```, **), contractions (don't -> do + n't), and numbers are tokenized individually, raising token counts above word counts.
How many tokens should I set for a 1,000-word output?
For a 1,000-word output, configure max_tokens to at least 1,500 to 1,800 tokens. This prevents the LLM from truncating mid-sentence if it provides detailed examples or formatting.
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