TOON: The Token-Efficient Data Format for LLM Applications - Complete Guide 2025

In the age of Large Language Models (LLMs), every token counts—literally. Whether you're building AI applications, optimizing API costs, or managing context windows, the way you structure your data can significantly impact both performance and expenses. Enter TOON (Token-Oriented Object Notation), a compact data format designed specifically to reduce token consumption when passing structured information to LLMs.

What is TOON?

TOON is a data serialization format that bridges the gap between JSON's programmatic utility and the need for token efficiency in LLM contexts. Unlike JSON, which was designed for machine-to-machine communication, TOON is optimized specifically as an input format for language models, achieving 30-60% fewer tokens compared to standard JSON formatting.

The core philosophy is simple: use JSON in your application code, convert to TOON when sending data to LLMs, then work with JSON results. Think of TOON as a translation layer that makes your AI applications more cost-effective and efficient.

How TOON Works: Three Key Strategies

TOON achieves its efficiency through three innovative approaches:

1. Minimal Syntax

TOON eliminates redundant punctuation that JSON requires:

• No curly braces for objects
• No square brackets for arrays (except length markers)
• Quotes only when necessary
• Simple key: value syntax

JSON Example:

{
  "name": "Alice",
  "age": 30,
  "city": "New York"
}

TOON Equivalent:

name: Alice
age: 30
city: New York

2. Indentation-Based Structure

Like YAML, TOON uses whitespace to represent nesting, making the structure clear without extra characters:

user:
  name: Alice
  profile:
    age: 30
    city: New York

3. Tabular Array Optimization

This is where TOON truly shines. For uniform arrays of objects (the most common use case with LLMs), TOON declares field names once and streams data as rows:

JSON:

[
  { "id": 1, "name": "Alice", "age": 30 },
  { "id": 2, "name": "Bob", "age": 25 },
  { "id": 3, "name": "Charlie", "age": 35 }
]

TOON:

[3,]{id,name,age}:
1,Alice,30
2,Bob,25
3,Charlie,35

The [3,] indicates array length, {id,name,age} declares fields once, and subsequent lines are pure data rows—no repetition, maximum efficiency.

Real-World Impact: The Numbers

The numbers speak for themselves. In benchmark tests with repository data:

• JSON: 15,145 tokens
• TOON: 8,745 tokens
• Reduction: 42.3%

Across four different LLM models, TOON achieved:

• 46.3% fewer tokens on average
• 70.1% accuracy on retrieval tasks (compared to JSON's 65.4%)
• Better comprehension despite using less space

Ideal Use Cases for TOON

TOON excels in scenarios where you're working with:

1. Uniform Data Collections

• Employee records
• E-commerce orders
• API response data
• Database query results
• Time-series analytics

2. LLM-Heavy Applications

• RAG (Retrieval-Augmented Generation) systems
• Data analysis with AI
• Automated report generation
• Chatbots handling structured data
• AI-powered data transformation

3. Cost-Sensitive Operations

• High-volume API calls to paid LLM services
• Applications hitting context window limits
• Real-time AI applications requiring fast processing

When to Stick with JSON:

For deeply nested, non-uniform data structures with irregular patterns, JSON remains more efficient. TOON is optimized for tabular and semi-structured data, not complex hierarchies.

Multi-Language Support: TOON Implementations

One of TOON's strengths is its growing ecosystem across programming languages:

JavaScript/TypeScript (Original)

The original implementation by Johann Schopplich provides the reference specification and includes comprehensive documentation and examples.

C# / .NET 9 (ToonSharp)

ToonSharp brings TOON to the .NET ecosystem with:

• Type-safe serialization/deserialization
• Async stream operations
• Integration with System.Text.Json
• High performance using Span<T> for zero-allocation parsing

var data = new { name = "Alice", age = 30 };
string toon = ToonSerializer.Serialize(data);

Go (GoTOON)

GoTOON provides a Go-idiomatic implementation with:

• Functional options pattern for configuration
• Deterministic output (sorted keys)
• Support for Go structs and maps

data := map[string]interface{}{"name": "Alice", "age": 30}
toon, _ := gotoon.Encode(data)

PHP (toon-php)

toon-php integrates seamlessly with PHP AI libraries:

• Multiple output formats (compact, readable, tabular)
• Helper functions for quick conversion
• Laravel integration support

$data = ['name' => 'Alice', 'age' => 30];
$toon = toon($data);

Python (python-toon)

python-toon maintains 100% compatibility with the official specification:

• CLI tool for JSON↔TOON conversion
• Strict and lenient parsing modes
• Pythonic API

import toon
data = {"name": "Alice", "age": 30}
toon_str = toon.encode(data)

Getting Started with TOON

Here's a quick example workflow in Python:

import toon
import json

# Your application data
employees = [
    {"id": 1, "name": "Alice", "department": "Engineering", "salary": 120000},
    {"id": 2, "name": "Bob", "department": "Marketing", "salary": 95000},
    {"id": 3, "name": "Charlie", "department": "Engineering", "salary": 110000}
]

# Convert to TOON for LLM input
toon_data = toon.encode(employees)

# Send to LLM
prompt = f"Analyze this employee data:\n{toon_data}\n\nWhat's the average salary by department?"

# Use fewer tokens, save money

Performance Considerations

TOON offers several advantages:

Token Savings: Typical reduction of 30-60% translates to:

• Lower API costs (many LLM APIs charge per token)
• Faster processing (less data to parse)
• More room in context windows

LLM Comprehension: Despite the compact format, LLMs handle TOON well because:

• The structure is still human-readable
• Indentation provides clear hierarchy
• Explicit length markers aid validation

Validation: TOON's explicit field declarations and length indicators make it easier for LLMs to verify data completeness and structure.

When Should You Use TOON?

Use TOON when:

• You're passing large datasets to LLMs regularly
• You're working with uniform, tabular data
• Token costs are a concern
• You need to maximize context window usage
• Your data has repetitive structure (arrays of similar objects)

Skip TOON when:

• Data is highly irregular or deeply nested
• You're only making occasional LLM calls
• Human readability of the raw data is critical
• Your application already has tight JSON integration

Frequently Asked Questions (FAQ)

Is TOON compatible with all LLMs?

Yes, TOON works with any LLM including OpenAI GPT models, Claude, Gemini, and open-source models. Since it's a text-based format, any model that can process structured text can handle TOON.

How much money can I save using TOON?

Savings depend on your usage patterns, but with 30-60% token reduction, if you're spending $1000/month on LLM API costs, you could potentially save $300-600/month.

Can I use TOON for LLM outputs?

TOON is designed as an input format. For outputs, it's better to use JSON since it's more widely supported by existing tools and libraries.

Does TOON affect LLM accuracy?

Benchmarks show TOON can actually improve accuracy (70.1% vs 65.4% for JSON) while using fewer tokens, likely due to reduced noise from punctuation.

What's the learning curve for TOON?

If you're familiar with JSON and YAML, you can start using TOON in minutes. The syntax is intuitive and well-documented across all implementations.

Conclusion

TOON represents a thoughtful optimization for the LLM era. By recognizing that data formats designed for machine-to-machine communication aren't always optimal for machine-to-AI communication, TOON provides a practical solution that reduces costs, improves efficiency, and maintains high comprehension rates.

With implementations across major programming languages and a clear specification, TOON is ready for production use in AI applications. Whether you're building a RAG system, creating AI-powered analytics tools, or simply trying to reduce your OpenAI API bills, TOON deserves a place in your toolkit.

The format proves that sometimes the best innovation isn't adding features—it's intelligently removing what you don't need.

Additional Resources

Official Implementations

• TOON Specification (JavaScript/TypeScript)
• ToonSharp (.NET/C#)
• GoTOON (Go)
• toon-php (PHP)
• python-toon (Python)

Next Steps

1. Choose the TOON implementation for your programming language
2. Start with a small dataset to test token savings
3. Integrate into your LLM pipeline
4. Monitor cost savings and performance improvements
5. Share your results with the TOON community