Introduction

The evolution of data formats tells a fascinating story about how technology adapts to meet our changing needs. From the humble .INI files that powered early configurations to the verbose but structured XML, the lightweight JSON, the human-friendly YAML, and now TOON a token-optimized format built for the AI era each has emerged to solve the challenges of its time.

Today, as Large Language Models (LLMs) reshape how we process and exchange information, efficiency at the token level has become a new frontier. Let’s explore how TOON (Token-Oriented Object Notation) compares with JSON, and why TOON might become the preferred format for GenAI developers.

A Brief History of Data Formats

INI Files

The .INI format was one of the earliest ways to store configurations. Simple and direct, it used key-value pairs grouped into sections:

[database] host=localhost port=5432 username=admin password=secret

Despite their simplicity, INI files remain popular for configurations and Windows systems due to their no-nonsense approach.

XML

Then came XML (eXtensible Markup Language), offering structure, validation, and hierarchy. It became the backbone for early web services, SOAP APIs, and document systems. However, its verbosity came at a cost.

XML’s strictness made it powerful — and painful — for many developers.

JSON

Enter JSON (JavaScript Object Notation): lightweight, human-readable, and easy for machines to parse. It hit the sweet spot between structure and simplicity, quickly becoming the standard for APIs and data exchange.

JSON became the universal language of web data simple enough for developers, efficient enough for servers.

YAML

As systems and automation grew, developers wanted something even more readable. YAML (YAML Ain’t Markup Language) embraced indentation and minimal punctuation, becoming the go-to for configuration files and CI/CD pipelines.

While YAML was great for humans, it wasn’t always ideal for machines — indentation errors and parsing quirks were common pain points.

TOON: The New Era

Now, as AI models process and reason over text, a new challenge emerged token efficiency. Every character counts in LLMs, directly affecting cost and performance.

This led to the birth of TOON (Token-Oriented Object Notation) a format built for the LLM age.

users[1]{id,name,role}: 1,Sreeni,admin

TOON is not just another serialization format. It’s a data format for the AI generation compact, structured, and optimized for how language models “think.”

The Modern Challenge

Traditional formats like JSON are still great but in LLM-driven workflows, verbosity equals cost. When every token matters, using 50% fewer tokens to represent the same data can significantly cut expenses and processing time.

This brings us to our main event: TOON vs JSON.

What is JSON?

JSON is a lightweight text-based format that represents structured data using key-value pairs. Originally derived from JavaScript, it’s now language-independent and universally supported.

Key Characteristics:

1. Syntax: Uses {}, [], :, and ,
2. Readable: Easy for humans and machines
3. Flexible: Supports complex nesting
4. Compatible: Supported everywhere
5. Verbose: Repetitive keys can increase size

Example:

What is TOON?

TOON (Token-Oriented Object Notation) is a next-generation format tailored for AI and LLM applications. It aims to make structured data token-efficient, reducing the cost of processing data within language models.

Key Characteristics:

1. Syntax: Indentation-based with tabular structure
2. Efficiency: Uses 30–60% fewer tokens than JSON
3. Compactness: Removes redundant symbols and keys
4. Readability: Clean, spreadsheet-like representation
5. Optimization: Purpose-built for AI data flows

Example:

users[3]{id,name,role,email}: 1,Sreeni,admin,sreeni@example.com 2,Krishna,admin,krishna@example.com 3,Aaron,user,aaron@example.com

metadata{total,last_updated}: 3,2024-01-15T10:30:00Z

TOON vs JSON: Key Differences

1. Syntax and Structure

JSON: Braces {}, brackets [], colons, commas.

TOON: Indentation and column headers cleaner, less noise.

2. Token Efficiency

LLMs charge by tokens, so structure matters.

Format Tokens Savings JSON ~89 — TOON ~45 ~50% fewer tokens

3. Readability

JSON is familiar and tooling-rich.

TOON feels new but becomes intuitive especially for structured, repetitive data (like CSV meets JSON).

4. Use Cases

Real-World Comparison

JSON

![ ](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/p7grgbeaaz10ti82hefl.png

TOON

Token Count:

JSON ≈ 180 tokens TOON ≈ 85 tokens Savings: ~53%

When to Use Each Format

Use JSON when:

1. You need compatibility and standardization
2. Building REST APIs or web apps
3. Using well-established toolchains
4. Team familiarity is critical

Use TOON when:

1. Working with LLMs and AI agents
2. Token cost and efficiency matter
3. Handling large or repetitive datasets
4. Building systems that communicate with AI models
5. Implementation & Libraries

JSON Support

1. Universal across languages
2. Extensive tooling (linters, validators)
3. Built-in browser and backend support

TOON Support

1. JavaScript/TypeScript: TOON on GitHub. https://github.com/toon-format/toon
2. Python: toon-py https://pypi.org/project/toon-py/

Conclusion

Both JSON and TOON have earned their place in modern development

JSON remains the universal workhorse ideal for APIs, configurations, and web services.

TOON is the rising star of the LLM era built for cost efficiency, clarity, and performance in AI-driven systems.

As AI applications continue to expand, the need for token-optimized formats like TOON will only grow. Yet, JSON’s universal compatibility ensures it won’t disappear anytime soon.

In the future, developers may use both formats side by side JSON for interoperability, TOON for AI efficiency. The key is knowing when to choose which, and how to leverage their strengths.

Thanks Sreeni Ramadorai