In the booming era of artificial intelligence, large language models (LLMs) are transforming how we interact with data. Yet, a challenge remains—how do we efficiently send structured data to these models without inflating token usage and cost? Traditionally, JSON (JavaScript Object Notation) has been the universal data format for transmitting information. But a rising alternative called TOON (Token-Oriented Object Notation) is redefining the game with clever token efficiency designed specifically for AI.
The JSON Legacy
JSON’s simplicity and flexibility made it the staple format for data exchange. Its key-value pairs, arrays, and nested objects suit a wide variety of applications—from APIs to configuration files. However, JSON’s verbosity, with repeated keys, braces, quotes, and commas, leads to bloated token counts when used as input for LLMs. Each token sent to an AI model costs time and money, and JSON often inflates that cost unnecessarily.
Example: Sending User Data in JSON
Consider this simple list of users:
{
“users”: [
{ “id”: 1, “name”: “Alice”, “role”: “admin” },
{ “id”: 2, “name”: “Bob”, “role”: “user” },
{ “id”: 3, “name”: “Carol”, “role”: “user” }
]
}
Notice how the keys “id”, “name”, and “role” repeat three times, and punctuation characters add to the overall size. Each of these elements consumes tokens and adds to the cost of processing.
TOON: Designed for LLMs
TOON simplifies this by cutting down on redundancy and structural overhead. It adopts a lightweight, tabular format that declares headers once, followed by rows of values. This approach minimizes tokens while keeping the data structured and easy to understand.
The Same User Data in TOON
users[3]{id,name,role}:
1,Alice,admin
2,Bob,user
3,Carol,user
With TOON, the keys are declared a single time, followed by compact rows of comma-separated values. No quotes, no braces, and no repeated keys—just the essential data.
Why TOON Excels in LLM Data Transmission
• Token Efficiency: TOON reduces token usage by 30–60%, significantly lowering API costs and speeding up model response time.
• Better Model Parsing: The explicit header row helps LLMs understand and process data with improved accuracy over verbose JSON.
• Human Readability: The tabular style of TOON makes data easier to read and debug in context, especially for large datasets.
• Ideal Use Cases: TOON shines with bulk, flat datasets like user lists, logs, product inventories, and analytics—where repetitive structures make JSON costly.
When to Use JSON
• For deeply nested or complex data structures requiring broad compatibility.
• When existing toolchains and APIs demand standard JSON.
• For irregular data that doesn’t fit TOON’s tabular format.
As LLMs continue to shape the future, TOON offers a smart, specialized alternative to JSON—one that optimizes token usage, reduces operational costs, and improves clarity for AI-powered workflows. By adopting TOON where appropriate, developers and AI practitioners can unlock faster, cheaper, and more accurate interactions with language models.
If you’re working with large, structured datasets in AI, the choice between JSON and TOON can have a material impact. TOON’s elegant design ensures your data travels light—perfectly tailored for the token-conscious world of large language models.
Ratnesh Dubey- Engineering & AI Insights 