As Large Language Models (LLMs) become the computation layer of modern AI systems, one invisible factor increasingly drives cost, latency, and performance i.e tokens.
Every {}, [], ", repeated key, and punctuation mark inside your JSON payload becomes a token when sent to a model. That means more cost, slower inference, and unnecessary noise for the model to parse — especially when dealing with large arrays or structured datasets.
JSON was designed for humans and machines in the early 2000s. It was not designed for LLM tokenizers.
This gap between traditional data formats and LLM-native needs is what gave rise to TOON (Token-Oriented Object Notation), a modern, compact, LLM-optimized format that cuts token usage by 30–60% for structured data, while improving consistency and comprehension.
Let’s dive into what makes TOON different, when you should use it, and why developers are beginning to combine JSON-internal systems with TOON-inference layers.
What We’ll Cover
1. Why We Needed Something Beyond JSON
JSON became the world’s default data format because it is:
Readable
Standardized
Easy to parse
Universally supported
Great for APIs, clients, storage, and backend systems
But in LLM workflows, JSON reveals a major weakness:
It’s too verbose for token-based models.
Problems highlighted across benchmark studies include:
Repeated keys inside arrays waste tokens
Every ", {}, [], :, and , becomes additional syntactic noise
Token usage scales poorly with large datasets
Slows down inference and increases cost
LLMs gain no reasoning benefit from the extra punctuation
JSON was created for browsers and servers and not for neural tokenizers.
The result is that prompt payloads become bloated, expensive, and slower, especially in agent frameworks, RAG pipelines, or multi-step workflows.
2. What Is TOON?
TOON (Token-Oriented Object Notation) is a serialization format designed explicitly for token efficiency when interacting with LLMs.
Its core idea is simple:
Represent structured data using the fewest tokens possible — without losing meaning or structure.
TOON borrows the best ideas from multiple formats:
CSV → tabular rows
YAML → indentation-based nesting
JSON → stays convertible and structured
Key strengths of TOON
30–60% fewer tokens for uniform/tabular datasets
Higher LLM comprehension: 73.9% vs 69.7% in structured reasoning tests
Optimized for large arrays (fields declared once)
Explicit structure: users[3]{id,name,role}
Readable by humans, efficient for models
Growing ecosystem: TypeScript, Python, Go, Rust, PHP
TOON is not trying to replace JSON everywhere but only in LLM-facing workflows
3. How TOON Works (With Examples)
3.1 Schema Declaration
A TOON block declares:
array name
length
fields
Example:
users[2]{id,name,role}:
Meaning:
users → array
[2] → 2 rows
{id,name,role} → fields shared across all rows
3.2 Tabular Rows
users[2]{id,name,role}:
1,Alice,admin
2,Bob,user
This replaces the JSON:
[
{ "id": 1, "name": "Alice", "role": "admin" },
{ "id": 2, "name": "Bob", "role": "user" }
]Token savings are typically ~50%.
3.3 Nesting
TOON supports nested structures through indentation:
users[1]:
- name: Team Alpha
members[2]{id,name,role}:
1,Alice,admin
2,Bob,userThis replaces the JSON:
[
{ "name": "Team Alpha", "members": [
{ "id": 1, "name": "Alice", "role": "admin" },
{ "id": 2, "name": "Bob", "role": "user" }
]
]4. What JSON Still Does Extremely Well
JSON is still the world’s most widely used data-interchange format:
Universal ecosystem: parsers everywhere
Strong schema validation (JSON Schema)
Built-in browser support
Ideal for APIs and storage
Handles any shape — deeply nested, irregular, or complex
Its drawback in the LLM context:
Verbose syntax
Repeated keys in arrays
High token usage
Slower inference
No added value for model reasoning
5. TOON vs JSON: Key Differences
Feature JSON TOON Target Web systems, APIs LLMs, AI agents Syntax {}, [], quotes, commas Indentation + tabular Token usage High Low (30–60% fewer) Repeated keys Yes No (declared once) Readability Good Excellent for humans + models Deep nesting Strong Less efficient Ecosystem Mature Growing fast
Feature | JSON | TOON |
|---|---|---|
Target | Web systems, APIs | LLMs, AI agents |
Syntax |
| Indentation + tabular |
Token usage | High | Low (30–60% fewer) |
Repeated keys | Yes | No (declared once) |
Readability | Good | Excellent for humans + models |
Deep nesting | Strong | Less efficient |
Ecosystem | Mature | Growing fast |
Benchmark example:
JSON prompt: 1344 tokens
TOON prompt: 589 tokens
~56% reduction, plus 5x faster in the LLM response.
6. Why TOON Is Better for LLMs
1. Significant Token Savings
TOON eliminates syntactic noise:
no braces
no quotes
no repeated field names
no dense punctuation
This translates into lower cost and faster inference.
2. Better Model Comprehension
Because TOON’s structure mirrors how LLMs internally group information, benchmarks show:
73.9% accuracy for TOON vs 69.7% for JSON
3. Cleaner Representation of Arrays
JSON repeats key names thousands of times for large datasets while TOON lists them once.
4. Perfect for AI Pipelines
TOON excels in:
prompt templates
structured tool calls
agent workflows
RAG preprocessing
fine-tuning datasets
7. When You Should NOT Use TOON
The data is deeply nested: TOON indentation becomes bulky and can even increase token count.
Objects in arrays have different shapes: TOON assumes uniform fields. JSON handles optional or varying structures better.
You need schema validation or type enforcement: JSON Schema + tooling → JSON wins.
Building public APIs or storage systems: Browsers, clients, databases → all expect JSON.
Long-term archival: JSON is stable; TOON is still evolving.
8. The Hybrid Best Practice: JSON + TOON Together
Instead of using either JSON or TOON, we can combine and use an hybrid approach. We can follow the following rule of thumb:
Use JSON inside your systems, use TOON when talking to the model
This hybrid architecture gives you:
JSON’s compatibility + validation + tooling
TOON’s token efficiency + model clarity
It’s the best of both worlds.
9. Final thought
JSON isn’t going anywhere — it remains the foundation of web APIs, distributed systems, and data storage.
But TOON is becoming the LLM-native format that trims cost, accelerates inference, reduces noise, and boosts structured reasoning. It reflects a broader shift in the AI era:
Efficiency isn’t only about better models — it’s also about speaking their language.
As AI applications scale, formats like TOON will increasingly shape how structured data flows through LLM-powered systems.
The future is not JSON vs TOON.
It is JSON + TOON, each in the role it was optimized for.
References
Thanks for reading! I appreciate you taking the time to dive into the world of TOON and explore how data formats are evolving for the LLM era.
