When your team starts integrating artificial intelligence into daily workflows, there’s one detail that often gets overlooked: data format. Most developers default to JSON because it’s universal, familiar, and compatible. But here’s a question worth asking: Is JSON really the best choice for AI models?

A new format called TOON is starting to gain traction. Short for Token-Oriented Object Notation, it’s specifically designed for large language models. Today, we’ll explore why TOON might be a better choice than JSON in certain scenarios.

The Hidden Costs of Using JSON with AI

Let’s start with a real-world scenario.

Imagine you’re building an AI assistant for customer service that needs to analyze thousands of support tickets. Each ticket’s JSON data looks something like this:

{
  "ticket_id": 101,
  "customer": "Akhil",
  "issue": "Payment failed",
  "priority": "high"
}

Looks standard, right? But here’s the problem: When you’re processing hundreds or thousands of these records, the language model has to repeatedly read the same field names:

  • “ticket_id”
  • “customer”
  • “issue”
  • “priority”

These repeated field names don’t matter much to databases or APIs, but to large language models, they get converted into tokens. More tokens mean:

  • Higher costs – Token-based API calls become more expensive
  • Slower processing – Models need to process more data
  • Unnecessary redundancy – Models only need to understand the structure once

In other words, JSON’s structured design is an advantage for traditional system-to-system communication, but becomes a burden in AI consumption scenarios.

How TOON Eliminates Token Waste

TOON’s core concept is simple: If field names are repetitive, why not declare them just once?

The same customer ticket data in TOON format looks like this:

tickets[3]{ticket_id,customer,issue,priority}:
  101,Akhil,Payment failed,high
  102,Meera,Unable to login,medium
  103,John,App crashes on start,high

Notice the difference?

  • Structure declared once – tickets[3]{ticket_id,customer,issue,priority} tells the model the data structure
  • Data packed tightly – Each row contains only actual data, no repeated field names
  • Removes excess symbols – No braces, no quotes, no redundant punctuation

What does this format resemble? Exactly—a spreadsheet. And spreadsheets happen to be one of the most intuitive ways humans organize data.

Real-World Case: Employee Information Management

Let’s use a more relatable workplace example for comparison.

You need to send employee information to AI for analysis or report generation. In JSON format, one employee record looks like this:

{
  "id": 1,
  "name": "Riya",
  "department": "Engineering",
  "salary": 90000
}

If your company has 2,000 employees, the JSON file will contain 2,000 sets of repeated field names. Each field name gets counted as a token.

In TOON format:

employees[1]{id,name,department,salary}:
  1,Riya,Engineering,90000

When scaled to 2,000 employees, TOON format declares the field structure only once at the beginning, followed by pure data. This design shows clear advantages at scale.

TOON Format Syntax Rules

To understand TOON, we need to grasp its basic syntax:

Format Structure

dataset_name[record_count]{field1,field2,field3,...}:
  value1,value2,value3,...
  value1,value2,value3,...

Key Components

  1. Dataset name – Describes the data type (e.g., employees, tickets, orders)
  2. Record count – Number in brackets indicates data rows, helping models predict data scale
  3. Field definition – Comma-separated field names in curly braces
  4. Data rows – After the colon, one record per line with comma-separated values

Why This Design

  • Explicit structure declaration – Language models can understand data structure before processing content
  • Row-column alignment – Table-like organization allows more accurate model parsing
  • Token minimization – Removes all unnecessary formatting symbols

Performance Comparison: The Numbers Speak

Based on testing across multiple datasets, TOON format demonstrates consistent performance advantages:

Token Usage

  • Approximately 30-60% reduction in token consumption compared to JSON
  • Larger datasets show more significant savings
  • Particularly effective for tabular, repetitive data

Model Accuracy

  • Slightly improved accuracy when AI answers data-related questions
  • Clearer structure enables more precise model understanding
  • Reduces parsing errors caused by format complexity

Data Processing Capability

  • Better handling of large tabular datasets
  • More predictable model parsing with stable behavior
  • Ideal for batch data analysis scenarios

For workloads containing thousands of rows—customer logs, analytics exports, order data, event records—the cost savings from token reduction are tangible and substantial.

When Should You Use TOON

TOON isn’t meant to replace JSON, but rather to offer a better choice in specific scenarios.

Scenarios for Using TOON

Consider TOON when you encounter these situations:

  • Data is tabular and highly repetitive – Like employee rosters, product catalogs, transaction records
  • Passing large datasets to language models – Datasets containing hundreds to thousands of records
  • Token efficiency directly impacts cost or performance – Using pay-per-token AI APIs
  • Need clearer data retrieval prompts – Structured data queries and analysis
  • Frequent data updates – Regularly passing fresh data to models

Scenarios for Sticking with JSON

JSON remains the better choice when facing:

  • Building general-purpose APIs – Need to interact with various systems
  • Deeply nested or highly diverse data structures – Complex object relationships
  • Multi-system interoperability required – Teams using different technology stacks
  • Frequently changing data structures – Dynamic data with unfixed fields
  • Small-scale data transfer – Just a few or dozens of records

Practical Application Scenarios for TOON

Let’s examine several concrete use cases to help you determine if TOON is right for you.

Scenario 1: Customer Support Ticket Analysis

If your AI assistant needs to analyze 500 customer tickets from today to identify common issues and priority distribution, TOON format can significantly reduce API call costs.

support_tickets[500]{ticket_id,customer,issue_type,priority,status,created_at}:
  101,Akhil,Payment failed,high,open,2025-11-17 09:23
  102,Meera,Unable to login,medium,pending,2025-11-17 09:45
  103,John,App crashes on start,high,open,2025-11-17 10:12
  ...

Scenario 2: Monthly Sales Data Summary

When generating monthly sales reports, you need to pass the entire month’s order data to AI for analysis and insight extraction.

monthly_orders[1250]{order_id,product,quantity,revenue,region,date}:
  5001,Widget A,15,750,North,2025-11-01
  5002,Widget B,8,960,South,2025-11-01
  5003,Widget A,22,1100,East,2025-11-02
  ...

Scenario 3: Batch Employee Performance Evaluation

HR departments need AI assistance analyzing quarterly performance data for all company employees to generate team insights.

performance_data[2000]{employee_id,name,department,score,projects_completed,attendance}:
  1,Riya,Engineering,4.5,12,98
  2,Amit,Marketing,4.2,8,95
  3,Priya,Sales,4.8,15,100
  ...

How to Adopt TOON in Real Projects

If you’re interested in TOON and want to try it in your projects, follow these steps:

Step 1: Identify Suitable Data

Check which data in your AI workflows has these characteristics:

  • Fixed structure with repeated fields
  • Large volume (100+ rows)
  • Frequently passed to large language models
  • Cost or performance is a concern

Step 2: Convert Data Format

Transform existing JSON data to TOON format. You can:

  • Manually convert small test datasets
  • Write simple scripts for batch conversion
  • Modify data export logic to generate TOON directly

Step 3: Test Model Responses

Using identical prompts, test with both JSON and TOON formats:

  • Record token usage
  • Compare response quality
  • Measure processing time

Step 4: Measure Actual Benefits

Based on your usage, calculate:

  • Token savings percentage
  • Cost reduction amount
  • Performance improvement degree

Step 5: Gradual Rollout

If test results are positive:

  • Start with a single scenario
  • Establish internal usage guidelines
  • Collect team feedback
  • Expand to more scenarios

TOON vs Other Data Formats

For a comprehensive understanding of TOON’s positioning, let’s compare it with other common formats.

TOON vs JSON

  • JSON: Strong universality, mature ecosystem, but low token efficiency
  • TOON: AI-optimized, token-efficient, but specialized application scenarios

TOON vs CSV

  • CSV: More concise, but lacks data type information and structure declaration
  • TOON: Explicit structure definition, more accurate model understanding

TOON vs XML

  • XML: Severe tag repetition, higher token consumption
  • TOON: Minimalist design, optimized specifically for token efficiency

TOON vs Tables

  • Plain tables: Need additional explanation to understand column meanings
  • TOON: Self-contained structure declaration, no external explanation required

Frequently Asked Questions

Is TOON format difficult to learn?

Not at all. If you’re familiar with spreadsheets, you’ll quickly grasp TOON. Its syntax rules are straightforward—the core concept is simply “declare structure first, then fill in data.”

Should all data use TOON?

No. TOON suits tabular, repetitive, high-volume data. For complex nested structures, small-scale data, or scenarios requiring cross-system interaction, JSON remains the better choice.

Will TOON become the new standard?

TOON is an optimization solution for specific scenarios (AI data consumption), unlikely to replace JSON as a universal standard. However, in the AI field, it may well become an important supplementary format.

Do existing tools support TOON?

TOON is a relatively new format with an ecosystem still in development. Currently, it’s mainly handled through custom conversion and parsing scripts. As adoption increases, expect more tool support.

Where can I find TOON learning resources?

Since TOON is an emerging format, dedicated learning resources are still limited. Start by understanding its design philosophy, then practice through real projects. Community discussions and practical cases will gradually increase.

A Framework for Data Format Selection

When choosing between JSON and TOON, ask yourself these questions:

  1. Who consumes the data? If primarily AI models, consider TOON; if multiple systems, choose JSON
  2. How much data is there? Dozens of records—either works; hundreds to thousands—TOON shows significant advantages
  3. What’s the data structure? Flat tabular suits TOON; complex nested suits JSON
  4. How cost-sensitive are you? If token cost is a major consideration, TOON is worth trying
  5. What about maintenance complexity? Consider team familiarity and long-term maintenance costs

Implementation Recommendations and Best Practices

If you decide to adopt TOON, these suggestions will help you succeed:

Keep Field Naming Clear

  • Use descriptive field names, even though TOON is already concise
  • Avoid abbreviations unless they’re industry-standard terminology
  • Maintain consistent naming style (like uniform underscores or camelCase)

Control Record Count Reasonably

  • Recommend no more than 5,000 rows per transmission
  • Consider batch processing for very large datasets
  • Operate within model context window limits

Combine with Clear Prompts

  • Tell the model you’re using TOON format
  • Explain the business meaning of the data
  • Specify the type of analysis you need

Establish Internal Documentation

  • Document TOON usage scenarios and conversion rules
  • Provide examples and templates
  • Share success stories and lessons learned

Future Outlook: Data Formats in the AI Era

TOON’s emergence reflects an important trend: As AI becomes the primary consumer of data, we need to rethink data format design principles.

Traditional data formats optimize for:

  • Cross-system compatibility
  • Human readability
  • Parser implementation simplicity

AI-era data formats need to optimize for:

  • Token efficiency
  • Model understanding accuracy
  • Cost and performance balance

TOON may be just the beginning. We’ll likely see more data formats and protocols designed specifically for AI. The key is maintaining an open mindset and choosing the most appropriate tool for your actual scenario.

Conclusion

TOON format offers AI engineers and data teams a practical improvement. It’s not about revolutionizing JSON, but providing a more efficient choice in specific scenarios.

Core Advantages

  • 30-60% reduction in token usage
  • Improved model understanding accuracy
  • Lower API call costs
  • Clearer data structure

Suitable Scenarios

  • Tabular repetitive data
  • Large-volume data transmission
  • Cost-sensitive AI applications
  • Structured data retrieval

For any AI workflow processing large amounts of row-based data, TOON deserves evaluation and experimentation. Start with small-scale testing, measure actual benefits, then decide whether to expand based on results.

In today’s era of increasingly widespread AI applications, data format optimization may seem minor, but produces significant impact at scale. TOON provides a practical perspective: When the consumer is AI, we can organize data in a more concise and efficient way.

Will you try TOON in your next AI project?