TOON vs JSON: A Modern Data Format Showdown

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,[e-mail]
2,Krishna,admin,[e-mail]
3,Aaron,user,[e-mail]

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/
3. https://scalevise.com/json-toon-converter
4. mywebutils.com/json-to-toon

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

Comments

[Ali Farhat]

Thank you for the article, would you mind adding our JSON to TOON tool in your article?
scalevise.com/json-toon-converter

[Seenivasa Ramadurai]

Hi Ali ,
Sure , will do .

Thanks
Sreeni

[Seenivasa Ramadurai]

Hi Ali ,
I have added your TOON tool in my blog .
Thanks
Sreeni

[Ali Farhat]

Thanks a million 🙏🏼🙌

[Seenivasa Ramadurai]

You are welcome.

[Elanat Framework]

In my opinion, INI is more powerful than JSON:

[core]
key = app
config = {"id":"coolapp","mode":"prod","lang":"en","threads":4}

[auth]
key = security
settings = {"oauth2":true,"token_ttl":3600,"jwt":"${ENV:JWT_SECRET}"}

[modules]
key = features
state = {"active":["analytics","cache"],"inactive":["debug"]}

[cache]
key = redis
connection = {"host":"127.0.0.1","port":6379,"ttl":300}

[Seenivasa Ramadurai]

Well, it’s kind of a yes and no. The .INI file format is definitely more familiar to Windows users, and it’s simple enough for basic configuration needs.

But that simplicity can be a double-edged sword .INI files can get messy and error-prone, especially when you try to handle complex or nested settings. That’s why most developers eventually moved on to XML, JSON, and YAML formats that are more structured, flexible, and platform-independent.

That said, if .INI fits your use case, go for it. But when it comes to AI or agentic applications, where efficiency and cost really matter, TOON is the smarter choice it’s lightweight and cost-effective.

Thanks,
Sreeni

[btullBMC]

It loosk like a CSV.
Why not just use a CSV?

[Seenivasa Ramadurai]

Hi There ,
The idea is to reduce the tokens ... so if we use CSV format then comma counted as the token too. And for LLM we can pass any suitable format with optimized tokens .

Thanks
Sreeni

[david duymelinck]

TOON has commas too, the only difference with tabular data is that TOON's header line has more characters.

TOON: users[3]{id,name,role,email}:
CSV: id,name,role,email

What happens when the data has a comma in it does it keep the quotes to prevent the data comma to be seen as a new column?

[richardevcom]

I'll just leave this here for people still taking this seriously 👀

[Seenivasa Ramadurai]

SLOP Sreeni Learned Only Post .. hope this SLOP makes sense for you

[Kelvyn Thai]

dev -> json, dev-ops -> yaml, TOON -> let LLM's guys debate =))

[Seenivasa Ramadurai]

The point is simple: JSON → TOON transformations exist because transformers (LLMs) need them.
Every application or technology requires its own perspective on the data.

[Seenivasa Ramadurai]

SLOP Share the Link Of that Post .. hope this slop makes more sense

[Nguyen]

It's look like another CSV inherit for me

[Seenivasa Ramadurai]

Exactly, you’re right. The main goal is to reduce the token count while preserving the essential, structured, and meaningful information

[Krishna Vijayaram]

Drastically reduces the tokens and file size.

[Seenivasa Ramadurai]

Well , TOON Definitely reduces the tokens ... what you mean file size ... in AI Application we just pass the context to LLM to inference ... i do not think we save this in file .. however your statement is valid

[Hatix Ntsoa]

toontools.vercel.app

The official converter with all the known formats

[Seenivasa Ramadurai]

i am not following , what you mean all formats ?. we need final target as TOON

[Seenivasa Ramadurai]

Nice point. However, since LLMs operate on tokens, our goal should be to target TOON it helps reduce the number of tokens (and therefore the data size). Moreover, since most APIs and MCP servers already use JSON, a single conversion to TOON should be sufficient in my opinion.

[Hatix Ntsoa]

Yes and I hope they'll implement it soon