TOON (Token-Optimized Object Notation) is a data format designed specifically for AI and LLM applications. It reduces token usage by 30-60% compared to JSON while maintaining perfect data fidelity.
If you're building AI applications with ChatGPT, Claude, Gemini, or other LLMs, TOON can significantly reduce your API costs and help you fit more data within context windows.
What is TOON?#
TOON stands for Token-Optimized Object Notation. It's a data serialization format that:
- Eliminates redundant JSON syntax (braces, brackets, quotes, colons)
- Uses indentation to represent structure (like YAML)
- Converts arrays of objects to tabular format with headers
- Is 100% losslessly convertible back to JSON
Why tokens matter: LLMs like GPT-4 and Claude charge per token. They also have context limits (e.g., 128K tokens). By reducing token count, you:
- Save money — Pay less for API calls
- Fit more data — Include more context in prompts
- Get better results — Less noise means better AI focus
JSON (21 tokens):
{"name":"John","age":30,"city":"NYC"}
TOON (9 tokens):
name: John
age: 30
city: NYC
Savings: 57%Why TOON Was Created#
JSON was designed for web APIs — human-readable yet machine-parseable. But LLMs tokenize text differently than traditional parsers.
The problem with JSON for AI:
- Every
{,},[,],",:consumes tokens - Property names repeat for every object in an array
- Syntax characters add zero semantic value for the AI
TOON solves this by:
- Removing all unnecessary punctuation
- Using indentation (which tokenizes efficiently)
- Declaring array headers once, not per object
TOON Syntax Explained#
TOON uses a simple, indentation-based syntax similar to YAML:
Objects:
user
name: John Doe
email: john@example.com
age: 30
isActive: trueTOON Tabular Arrays#
The biggest token savings come from arrays of objects. Instead of repeating keys for each object, TOON uses a tabular format:
JSON (42 tokens):
[{"id":1,"name":"A"},{"id":2,"name":"B"},{"id":3,"name":"C"}]
TOON (15 tokens):
[3]
id, name
1, A
2, B
3, C
Savings: 64%Real Token Savings Analysis#
Here's how TOON performs across different data structures:
| Data Type | JSON Tokens | TOON Tokens | Savings |
|---|---|---|---|
| Simple Object | 21 | 9 | 57% |
| Nested Object | 35 | 14 | 60% |
| Array (3 objects) | 42 | 15 | 64% |
| API Response | 58 | 22 | 62% |
| Config File | 45 | 18 | 60% |
When to Use TOON#
USE TOON for:
- AI/LLM prompts and context data
- RAG (Retrieval-Augmented Generation) systems
- Fine-tuning datasets
- Embeddings input data
- AI agent tool responses
DON'T USE TOON for:
- Traditional web APIs (stick with JSON)
- Configuration files (JSON/YAML are standard)
- Browser localStorage (use JSON)
- Database storage (JSON is BSON-compatible)
Using TOON with Specific LLMs#
Different AI models may benefit from a brief instruction about the format:
For GPT-4 and o1 series:
OpenAI models handle TOON natively. For large datasets, add: "The following data is in TOON format (CSV-style with headers)."
For Claude 3.5 Sonnet:
Anthropic models excel with TOON's clean structure. Claude's training on documentation means it parses indentation-based formats very accurately.
Pro Tip: When using TOON with 100+ rows of tabular data, adding a brief format note improves parsing accuracy significantly.
Frequently Asked Questions
What does TOON stand for?▼
Is TOON conversion lossless?▼
How much money can I save with TOON?▼
Do AI models understand TOON format?▼
Is TOON an official standard?▼
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