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token_counter.js ACTIVE
// Quick Presets:
// Prompt & Content Input 0 chars · 0 words
// Primary Tokenizer / Model Standard
// Estimated Tokens
0
GPT-4o (o200k_base)
// Words
0
0 characters
// Token / Word Ratio
0.00
~0.0 chars/token
// Est. Input Cost
$0.0000
Current Model Input
// Context Window Capacity 0.0% of 128,000 tokens
Plenty of headroom remaining in context window.
// Multi-Model Comparison & Cost Matrix
Model Est. Tokens Context Max Cost (Input / 1M) Est. Prompt Cost
// Token Visualizer (Alternating Segments)
Chunk A Chunk B Chunk C Chunk D
Enter text above to see visual token breakdown...
// Token Composition Breakdown
Alphabetic Words: 0 (0%)
Numeric Tokens: 0 (0%)
Punctuation & Symbols: 0 (0%)
Whitespace / Linebreaks: 0 (0%)
§ Guide How to Use the Token Counter
Paste or Upload Content
Enter text into the editor, choose a preset sample (Chat, Python, JSON, System Prompt), or upload local text files (.txt, .md, .py, .json) directly.
Select Target Model Family
Switch between OpenAI (o200k/cl100k), Anthropic Claude 3.5, Google Gemini 1.5/2.0, or Meta LLaMA 3. The token breakdown and context gauge update instantaneously.
Evaluate Pricing & Context Capacity
Check the cross-model cost comparison table to see how much your prompt costs across providers, and inspect the visual token highlighter to spot token-heavy punctuation or syntax.
⚡ Best Practices Prompt Token Optimization Tips

Minimize Redundant Whitespace & Indents

JSON minification and trimming trailing indentation in code snippets can cut token counts by 15% to 30%, especially in large batch prompts.

Prefer Markdown Over Verbose XML/HTML

Unless your prompt specifically trains an agent to parse complex XML trees, clean Markdown lists and headings token-compress much tighter than HTML tags.

Understand Sub-Word Splits

Common English words usually consume 1 token. Unusual names, hexadecimal IDs, UUIDs, and foreign alphabets often decompose into multiple sub-word chunks.

Cache System Instructions

Modern LLMs (Claude Prompt Caching, Gemini Context Caching, OpenAI Prompt Caching) discount repeated system prompts by up to 90%. Structure prefixes predictably.

? FAQ Frequently Asked Questions
Tokens are the atomic units that language models read and generate. A token can be as short as one character (like a single digit or punctuation mark) or as long as one word (such as "apple" or " thinking"). In standard English text, 1 token is roughly equivalent to 4 characters or 0.75 words.
Each LLM provider trains their own Byte-Pair Encoding (BPE) or WordPiece vocabulary. For example, OpenAI's GPT-4o uses the expanded o200k_base vocabulary (~200,000 tokens), which packs non-English text and code into fewer tokens than older cl100k_base (GPT-4) or Claude's custom tokenizers.
No. ToolHub executes all token estimation, regex chunking, and cost calculations 100% locally in your web browser. Neither your text, confidential code, nor prompts leave your machine or get saved in any database.
Cost estimates reflect current published API pricing per million prompt/input tokens across OpenAI, Anthropic, Google Cloud, and Groq/Together. Actual API bills may vary slightly based on prompt caching discounts, batch API tiers, or system overhead.