What is TOON and Why It Matters for AI Developers

In the rapidly evolving world of Large Language Models (LLMs), efficiency is currency. Every token sent to an API like OpenAI's GPT-4 or Anthropic's Claude costs money and adds latency. Enter TOON (Token-Oriented Object Notation), a new data format designed specifically to address the inefficiencies of JSON when used with LLMs.

The Problem with JSON in the AI Era

JSON (JavaScript Object Notation) has been the de facto standard for data interchange for over a decade. It's human-readable, widely supported, and perfect for traditional web applications. However, when it comes to LLMs, JSON has a significant drawback: token bloat.

LLMs process text in chunks called "tokens." Common words might be a single token, but punctuation and structural characters often count as separate tokens. JSON is full of structural overhead:

• Repeated keys in arrays of objects
• Quote marks around every key and string value
• Curly braces and brackets for nesting
• Commas and colons for separation

For a large dataset, this structural syntax can account for 30-50% of the total token count. When you're paying per million tokens, you're effectively paying a "JSON tax" on every API call.

Enter TOON: Designed for Tokens

TOON is designed to minimize this overhead without sacrificing readability or structure. It borrows concepts from YAML, Markdown, and other concise formats to represent data in a way that is "token-dense." Learn more about the TOON specification and see how it compares to JSON or TONL.

Key Features of TOON:

1. Implicit Typing: No quotes required for strings that don't contain special characters.
2. Whitespace Significance: Uses indentation to denote structure, eliminating the need for closing braces and brackets.
3. Header-Row Arrays: For lists of objects, TOON defines keys once at the top, similar to CSV, rather than repeating them for every item.

Real-World Savings

Let's look at a simple example. Consider a list of users in JSON:

[
  { "id": 1, "name": "Alice", "role": "admin" },
  { "id": 2, "name": "Bob", "role": "user" },
  { "id": 3, "name": "Charlie", "role": "user" }
]

JSON Token Count: ~55 tokens (depending on the tokenizer)

Now, the same data in TOON:

| id | name    | role
| 1  | Alice   | admin
| 2  | Bob     | user
| 3  | Charlie | user

TOON Token Count: ~25 tokens

That's a 54% reduction in tokens for the exact same data. When scaled up to thousands of records or frequent API calls, the cost savings are substantial.

Conclusion

As we build more complex AI agents and applications, data efficiency becomes a critical optimization path. TOON offers a simple yet powerful way to reduce your AI infrastructure costs and improve performance by simply changing how you structure your data.

Ready to try it out? Use our converter tool to see how much you can save on your own data. For more details, check out our TOON documentation, learn how to optimize API costs, or see the complete format comparison.