AVAILABLE FOR WEB, FLUTTER & QA PROJECTS
How to use this tool
1. Enter or Paste Text
Type or paste your prompt, system instructions, or document into the interactive text editor, or click 'Load Sample Prompt'.
2. Inspect Live Token Metrics
Review instant calculations for word count, character count, estimated tokens, and token-to-word density.
3. Choose Model & Set Volume
Select your LLM provider and configure projected monthly request volumes and output generation lengths to estimate total API expenditure.
4. Copy or Reset
Copy the computed metrics breakdown to your clipboard for budget planning or reset the fields with one click.
Formula or logic used
Tokenization & Pricing Formula
Modern subword tokenizers (like Byte-Pair Encoding in cl100k_base / o200k_base) break English text into subword fragments. On average, 1 token ≈ 0.75 words (or 1 word ≈ 1.33 tokens) for natural English, while code and non-English scripts average 1.5 to 2.5 tokens per word.
Estimated Tokens = (Word Count × 1.33) + (Special Characters & Punctuation × 0.25)Prompt Input Cost = (Input Tokens / 1,000,000) × Input Price Per 1M TokensCompletion Output Cost = (Output Tokens / 1,000,000) × Output Price Per 1M TokensTotal Cost Per Request = Input Cost + Output CostMonthly Cost = Total Cost Per Request × Daily Requests × 30
Examples
Example 1: Short Customer Support Chat Prompt
Standard conversational customer tickets have low subword fragmentation and stay close to the 1.33 multiplier.
Example 2: Long Legal Document Summary
Dense legal vocabulary and formatting punctuation slightly elevate the token ratio to 1.35x words.
Common use cases
LLM API Budget Modeling
Forecast monthly software bills before deploying production AI agents or batch pipelines.
Prompt Engineering Optimization
Identify bloated system instructions and optimize token count to reduce latency and TTFT (time-to-first-token).
Context Window Guardrails
Verify whether extensive retrieval context fits comfortably within 8k, 32k, or 128k context ceilings.
Multi-Model Cost Benchmarking
Compare cost differentials between GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Flash side by side.
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Frequently asked questions
How many words is 1,000 AI tokens?
On average, 1,000 AI tokens equal approximately 750 English words. For technical documentation or code snippets, 1,000 tokens may correspond to 500 to 600 words due to subword splitting on punctuation, camelCase identifiers, and indentation.
Why do different models count tokens differently?
Every model family utilizes a specific tokenizer vocabulary. For example, OpenAI's GPT-4o uses the o200k_base tokenizer with a 200,000 token vocabulary, which compresses text and code up to 15-20% more efficiently than older cl100k_base tokenizers.
Does whitespace count as tokens in AI prompts?
Yes. Whitespace, line breaks, tabs, and indentation are encoded into tokens. In Python code or heavily indented YAML, whitespace can account for 10% to 25% of total token volume.
How can I reduce my OpenAI and Claude API token costs?
You can compress prompts by removing boilerplate examples, utilizing concise JSON schemas, adopting prompt caching for static system instructions (saving up to 90% on Claude and OpenAI cached inputs), and switching to smaller models (GPT-4o-mini or Gemini 1.5 Flash) for classification tasks.
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