LLM Token & Prompt Cost Counter
Estimate exact token counts, context window utilization, and API input costs across GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5/2.0.
Drop file to count words locally
100% processed in browser. Supports .docx, .pdf, .txt, .md
Keyword Frequency & Density
Identify recurring terms and calibrate your SEO phrase balance.
| PHRASE | OCCURRENCES | DENSITY |
|---|---|---|
| Type or paste text above to calculate keyword density. | ||
Set Word Goal
Set a budget to calibrate live progress as you write.
Understanding Tokens vs. Words for LLM Context Windows
Large language models (LLMs) do not read text in words or characters; they process sequences of tokens—frequently occurring chunks of characters derived through Byte-Pair Encoding (BPE). While simple English words like "cat" or "the" form a single token, code snippets, JSON objects, URLs, and non-English text frequently split into 2 to 4 tokens per word.
Monitoring prompt token count is critical when engineering RAG (Retrieval-Augmented Generation) pipelines, calibrating system prompts, or budgeting high-volume API automation.