Convert your data to Token-Oriented Object Notation (TOON). A cleaner, more compact format designed to reduce LLM latency and cut token costs by up to 60%.
TOON (Token-Oriented Object Notation) is a data serialization format specifically designed for Large Language Models like ChatGPT, Claude, and GPT-4. Unlike JSON, which wastes 30-50% of tokens on repetitive syntax, TOON uses a compact, tabular structure that dramatically reduces token consumption.
TOON removes repetitive keys in arrays, stripping away quotation marks and curly braces. For tabular data, this results in 40-60% fewer tokens compared to JSON.
Unlike CSV, TOON preserves nested structures and hierarchies through indentation, making it robust enough for complex data while remaining token-light.
Fewer tokens mean faster generation and processing times. Feeding TOON to GPT-4 or Claude results in snappier responses for data-heavy tasks.
[
{
"id": 1,
"name": "Alice",
"role": "Admin"
},
{
"id": 2,
"name": "Bob",
"role": "User"
}
]Repeated keys ("id", "name", "role")
Excessive punctuation (quotes, braces)
users[2,]{id,name,role}:
1,Alice,Admin
2,Bob,UserKeys defined once in header
Zero redundant punctuation
TOON (Token-Oriented Object Notation) is primarily used for optimizing data in LLM prompts. When working with AI models like ChatGPT, Claude, or GPT-4, every token counts toward your API costs and context limits. TOON reduces token usage by 30-60% compared to JSON, making it ideal for fine-tuning datasets, API calls, and structured prompts.
Our TOON converter analyzes your JSON structure and identifies uniform arrays of objects. Instead of repeating field names for every row, TOON declares them once in a header (like users[2,]{id,name}) and then lists values in a CSV-like format. This eliminates redundant quotation marks, brackets, and repeated keys.
Yes! Our tool supports CSV to TOON conversion. Simply paste your CSV data, select the "CSV" input format, and click Convert. The converter will parse your tabular data and generate optimized TOON output that's perfect for LLM consumption.
For structured, tabular data in AI prompts, TOON is significantly better than JSON. Benchmarks show 40-60% token savings on uniform datasets. This means faster response times, lower API costs, and more room in your context window for actual content. However, for deeply nested or non-uniform data, JSON may still be more suitable.
All major LLMs (ChatGPT, GPT-4, Claude, Gemini, etc.) can understand TOON format because it's designed to be human-readable and follows YAML-like conventions. You don't need special API support—simply include TOON-formatted data in your prompts, and the model will parse it naturally. The token savings apply regardless of the model you're using.