As Large Language Models (LLMs) become core infrastructure, the economics of data serialization have fundamentally changed. In the past, formats were judged based on:
- universality
- bandwidth size
- parsing speed
But today, LLMs are billed by tokens, not bytes. This means the top priority is minimizing token count, not minimizing file size.
Two formats now dominate this discussion:
JSON Universal, stable, best for APIs
TOON Token-efficient format built for LLMs
TOON consistently reduces token usage by 30–60%, making it significantly cheaper for high-volume LLM workloads. JSON, however, remains the best choice for interoperability, complex structures, and external system communication.
Best-practice architecture:
- Use JSON at system boundaries (APIs, external integrations)
- Use TOON internally for sending structured data into LLMs
This hybrid strategy maximizes interoperability while reducing LLM operational costs.
Table of Contents
Why Serialization Matters in the LLM Era
The Role of Data Formats
Data formats define how systems communicate. For years, JSON dominated because it is:
- simple
- expressive
- widely supported
- easy to parse
Its symbol-driven structure ({}, [], :) works perfectly for APIs and server communication.
The Token Constraint of LLMs
LLMs don’t process text as bytes they process tokens. Every {, }, "key", :, and repeated field becomes another token.
If JSON wasn’t verbose, this wouldn’t matter. But JSON repeats:
- every key
- every delimiter
- every hierarchical symbol
On each record. This makes JSON expensive for LLM prompts.
The bottleneck has moved:
- from network bandwidth → to LLM context window
- from KB size → to token count
TOON was created as a direct solution to this new bottleneck.
The Economics Behind Token Minimization
TOON exists because JSON’s structural repetition creates:
- higher LLM billing costs
- faster context window saturation
- more prompt noise
By eliminating repeated keys and symbols, TOON reduces token usage by 30–60%, making it ideal for structured, repetitive data sent into LLMs.
Structural Breakdown: JSON vs TOON
JSON: The Standardized, Universal Format
JSON follows strict specifications (RFC 8259, ECMA-404). Key characteristics:
- uses objects (
{}), arrays ([]), and key/value pairs - keys are repeated for every object
- universally supported across languages
- optimized for parsing speed
- excellent for deeply nested and heterogeneous data
JSON’s strengths:
- standardization
- interoperability
- tooling maturity
- compatibility with every modern system
TOON: Token-Oriented Object Notation
TOON is designed for one purpose only: ➡️ maximize token efficiency for LLM workflows
How TOON works
- indentation-based (like YAML)
- tabular structure with column headers
- keys defined once
- rows contain only values
- minimal structural noise
Why TOON saves tokens
Example (simplified):
JSON:
[
{"id": 1, "score": 8.2},
{"id": 2, "score": 6.5}
]
TOON:
id score
1 8.2
2 6.5
No:
- braces
- quotes
- brackets
- repeated keys
This format exploits the fact that LLMs tokenize every visible symbol.
Built-in validation
TOON optionally includes:
- length declarations
- explicit field definitions
This increases accuracy and reduces hallucinations in LLM agents.
TOON’s Limitations
TOON is not a universal replacement. Its structure makes it poor for:
- deep nesting
- mixed data
- complex object graphs
- APIs
- inconsistent schemas
TOON is a specialist format for flat, repetitive, structured arrays.
Token Efficiency: The Real Cost Advantage
Why Tokens Matter More Than Bytes
LLM providers (OpenAI, Anthropic, etc.) charge per token, not per byte. So repeated JSON keys = repeated token cost.
Reducing tokens:
- lowers API bills
- reduces latency
- increases throughput
- frees up context window space
TOON optimizes the LLM layer, not the network layer.
Quantitative Comparison
Token Count Example
| Format | Token Count (Example) | Savings |
|---|---|---|
| JSON | ~180 tokens | – |
| TOON | ~85 tokens | ~53% fewer |
Across larger datasets, these savings compound massively.
Reliability and Accuracy Benefits
TOON reduces hallucinations because:
- structure is clearer
- validation rules guide the model
- context is cleaner
- noise is reduced
Precision → consistency → better LLM behavior.
Readability, Developer Experience, and Performance
Human Readability
TOON is easier for humans when:
- viewing tables
- reviewing batches
- scanning repetitive data
JSON is easier when:
- debugging errors
- viewing nested structures
- using IDE tooling
Parsing Overhead
- JSON has extremely fast parsing (optimized libraries everywhere).
- TOON requires indentation-sensitive parsing (slower, more complex).
But the LLM savings usually outweigh the local parsing cost.
Structural Suitability Comparison
| Data Type | JSON | TOON |
|---|---|---|
| Deeply nested | ⭐⭐⭐⭐⭐ | ⭐ |
| Flat repetitive lists | ⭐⭐⭐ | ⭐⭐⭐⭐⭐ |
| Mixed data | ⭐⭐⭐⭐⭐ | ⭐⭐ |
| Tooling support | ⭐⭐⭐⭐⭐ | ⭐ |
| LLM token efficiency | ⭐⭐ | ⭐⭐⭐⭐⭐ |
Ecosystem and Adoption
JSON: Mature Ecosystem
JSON has:
- top-tier tooling
- native language support
- reliable libraries
- strong community adoption
Zero risk.
TOON: Early-Stage Ecosystem
TOON:
- requires custom tooling
- has limited library support
- introduces whitespace sensitivity
Best suited for internal LLM pipelines only.
Comparison with YAML and TOML
To be clear:
- YAML/TOML = human-friendly config formats
- TOON = machine-friendly token optimization format
They solve different problems.
Strategic Recommendations
Format Selection Matrix
| Goal | Data Profile | Format | Why |
|---|---|---|---|
| High-volume LLM input | Repetitive lists | TOON | 30–60% token savings |
| Web APIs | Complex, interoperable | JSON | Standardized + universal |
| Complex config | Deep nesting + comments | YAML | Readable & flexible |
| Simple configs | Flat settings | TOML | Clean + minimal |
| Mixed, unpredictable data | Heterogeneous | JSON | Most robust |
Architecture Recommendations
Use TOON for internal LLM loops
You instantly gain:
- huge token savings
- reduced error rates
- cleaner context windows
Keep JSON at external boundaries
APIs, webhooks, integrations JSON still wins.
Build a serialization layer
Implement:
- JSON → TOON converter
- TOON → JSON converter
This hybrid system gives you both:
- JSON’s interoperability
- TOON’s token efficiency
Use TOON only where it fits
Avoid TOON for:
- deep nesting
- mixed data
- non-uniform schemas
TOON is perfect only when structure is predictable.
FAQ JSON vs TOON
What is TOON format?
TOON (Token-Oriented Object Notation) is a lightweight, indentation-based format designed to minimize token usage in LLM prompts by removing repeated JSON keys and symbols.
How much token savings does TOON offer compared to JSON?
TOON generally reduces token usage by 30–60%, especially for repetitive, structured data.
Is TOON a replacement for JSON?
No. JSON remains essential for external APIs, complex hierarchies, and interoperability. TOON is best for LLM-specific workflows.
When should I avoid TOON?
Avoid TOON for deep nesting, mixed data types, or any scenario requiring rich object relationships.
Why does token reduction matter?
Token reduction lowers LLM API cost, reduces latency, increases throughput, and frees up context window space.
How do I integrate TOON into my system?
Use a hybrid architecture: JSON at boundaries → convert to TOON → feed LLM → convert back if needed.
At End
JSON and TOON are not competitors they are complementary tools designed for different layers of modern AI systems.
JSON remains the best choice for APIs, complex data, and interoperability. TOON is the new powerhouse for optimizing structured data fed into LLMs, offering 30–60% token savings and improved reliability in AI agent workflows.
A hybrid architecture JSON at the boundaries, TOON inside the LLM pipeline delivers the best balance of performance, cost efficiency, and long-term maintainability.