Convert JSON to see TOON output...
Token Efficient
Save ~40% tokens compared to JSON while maintaining 100% data fidelity and lossless round-trips.
LLM-Friendly
Explicit [N] lengths and {fields} headers give models clear schema guardrails for better parsing.
Tabular Arrays
Uniform arrays collapse into CSV-style tables with fields declared once and values streamed line by line.
JSON Compatible
Encodes the same objects, arrays, and primitives as JSON with deterministic, lossless conversion.
How to Use This Tool
Paste Your JSON
Copy your JSON data and paste it into the left panel. You can also select from the example dropdown to see different data structures in action.
See TOON Output
The TOON format appears instantly on the right. Watch the token counter to see how much you're saving compared to JSON.
Copy or Download
Use the copy button to grab the TOON output for your LLM prompts, or download it as a .toon file for later use.
💡 Pro Tips
- Format JSON: Click the format button or press Cmd/Ctrl + Shift + F to prettify messy JSON
- Quick Clear: Press Cmd/Ctrl + K to clear the input and start fresh
- Fast Copy: Press Cmd/Ctrl + Shift + C to copy TOON output to clipboard
- Best Results: TOON works best with uniform arrays of objects (like database records or API responses)
- Token Savings: Typical savings are 30-50% for structured data, perfect for reducing LLM API costs
Common Use Cases
Fit more data in your prompts while using fewer tokens
Save money on token-based pricing from OpenAI, Anthropic, etc.
Send datasets to LLMs for analysis with minimal overhead
Compact format for moving data between systems efficiently
About This Tool
This playground was built to make it easy to convert JSON to TOON format and see the token savings in real-time. TOON (Token-Oriented Object Notation) is a compact, human-readable encoding of JSON specifically designed to minimize tokens for Large Language Model input.
Whether you're working with ChatGPT, Claude, Gemini, or any other LLM, TOON helps you fit more data into your context window while reducing costs. The format combines YAML's indentation-based structure with CSV-style tabular arrays for maximum efficiency.
Built by Deep Shah
Solutions Architect