JSON to TOON Converter

Convert JSON to TOON format online. Transform JSON data to human-readable TOON configuration files. Free JSON to TOON converter with customizable options and instant results.

Input JSON

Paste or type your JSON data
πŸ”§ All JSON Tools

TOON Output

Your converted TOON

Why Use JSON to TOON for LLM Applications?

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30-60% Token Reduction

Our JSON to TOON converter achieves proven token savings across all major LLM providers including OpenAI, Anthropic, and Google. Reduce your API costs significantly.

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LLM-Optimized Format

TOON format is specifically designed for language models. The JSON to TOON converter creates output that LLMs parse more efficiently.

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Human-Readable Output

Despite being optimized for machines, TOON format remains readable. The JSON to TOON converter produces clean, indented output.

Features

πŸ“‹ Advanced TOON Conversion

  • Convert JSON to token-efficient TOON format
  • Real-time token count comparison
  • Sort object keys for consistent output
  • Automatic table format for uniform arrays

πŸ€– LLM Integration

  • Reduce API costs for ChatGPT, Claude, Gemini
  • Optimize prompts and context windows
  • Lower latency with smaller payloads
  • Download as .toon files for AI workflows

⚑ Developer Experience

  • Real-time token savings calculation
  • Privacy-focused: all processing client-side
  • Mobile-responsive design
  • Copy to clipboard for immediate use

Complete Guide to JSON to TOON Conversion

What is TOON (Token-Oriented Object Notation)?

TOON (Token-Oriented Object Notation) is a revolutionary data serialization format specifically designed to reduce token consumption in Large Language Model (LLM) applications. Unlike traditional JSON with its verbose bracket syntax, TOON uses indentation-based structure and table notation for arrays, achieving 30-60% token reduction while maintaining full data integrity and human readability.

The TOON format was created by the open-source community to address the growing costs of LLM API usage. By eliminating redundant syntax characters like {}, [], and excessive quotes, TOON creates more compact representations that LLMs can parse equally wellβ€”or betterβ€”than JSON. This JSON to TOON converter implements the official TOON specification from github.com/toon-format/spec.

How TOON Reduces LLM Token Consumption

TOON achieves dramatic token savings through several key innovations:

  • Indentation-Based Structure: Eliminates braces {} and brackets [], using meaningful whitespace instead
  • Table Notation for Arrays: Uniform arrays become CSV-style tables with [count]{fields}: syntax, avoiding repeated key names
  • Minimal Quoting: Only quotes strings containing special characters, reducing unnecessary quote tokens
  • Compact Key-Value Pairs: Simple key: value notation without braces or commas between entries
  • Optimized for Tokenizers: Structure aligns with how GPT, Claude, and other LLMs tokenize text

When to Convert JSON to TOON Format

  • LLM API Cost Reduction: Reduce costs for ChatGPT, Claude, Gemini, and other LLM APIs by 30-60%
  • Prompt Engineering: Fit more data in context windows by using token-efficient TOON format
  • AI Training Data: Optimize training datasets and fine-tuning data for language models
  • RAG Systems: Store and retrieve more documents within token limits for Retrieval-Augmented Generation
  • Agent Frameworks: Reduce token usage in LangChain, AutoGPT, and other AI agent frameworks
  • API Responses: Convert JSON API responses to TOON before sending to LLMs for processing
  • Configuration Files: Create human-readable configs that LLMs can understand with fewer tokens
  • Data Serialization: Exchange data between systems where token efficiency matters

TOON vs JSON: Token Comparison Example

JSON (96 tokens)

{
  "users": [
    {"id": 1, "name": "Alice"},
    {"id": 2, "name": "Bob"}
  ]
}

TOON (59 tokens - 39% reduction)

users[2]{id,name}:
1,Alice
2,Bob

Notice how TOON's table notation eliminates repeated keys and brackets, resulting in 37 fewer tokens for this simple example. The savings compound dramatically with larger datasets!