TOML was designed as a human-friendly configuration format, while TOON was built for LLM token efficiency. Both prioritize readability, but serve different purposes. Let's explore when each format shines and how they compare for AI applications.

The Contenders

TOML

Pros:

Designed specifically for configuration files.
Clear section headers with [brackets].
Native datetime support.
Popular in Rust, Python (pyproject.toml), and Go.

Cons:

Verbose for arrays of objects (tables).
Complex nested structure syntax.
Higher token overhead than TOON.
Not optimized for data serialization.

TOON

Pros:

Optimized for token efficiency (44% savings).
Tabular format perfect for arrays of objects.
Clean indentation-based syntax.
Designed for LLM data interchange.

Cons:

No native datetime type (uses strings).
Not a configuration format standard.
Smaller ecosystem than TOML.

Syntax Comparison

The difference in philosophy becomes clear when comparing equivalent data structures.

TOML Example (182 bytes, ~90 tokens):

[[users]]
id = 1
name = "Alice"
role = "admin"

[[users]]
id = 2
name = "Bob"
role = "user"

[[users]]
id = 3
name = "Charlie"
role = "user"

TOON Example (72 bytes, ~50 tokens - 44% reduction):

users[3]{id,name,role}:
  1, Alice, admin
  2, Bob, user
  3, Charlie, user

TOML repeats the [[users]] header and all field names for each entry. TOON declares fields once and streams values as compact rows.

Configuration vs Data Serialization

This is the key distinction between these formats:

Use Case	TOML	TOON
App configuration	Excellent	Good
Package metadata	Industry standard	Usable
Large datasets	Verbose	Optimized
LLM prompts	High token cost	Token efficient
API payloads	Uncommon	Ideal

Nested Structure Comparison

TOML uses section headers for nesting. TOON uses indentation.

TOML Nested Config:

[server]
host = "localhost"
port = 8080

[server.ssl]
enabled = true
cert = "/path/to/cert.pem"

[database]
host = "db.example.com"
port = 5432

[database.pool]
min = 5
max = 20

TOON Nested Config:

server:
  host: localhost
  port: 8080
  ssl:
    enabled: true
    cert: /path/to/cert.pem
database:
  host: db.example.com
  port: 5432
  pool:
    min: 5
    max: 20

For configuration, TOML's explicit section headers are clearer. For data, TOON's compact nesting is more efficient.

Arrays of Tables: The Big Difference

This is where TOON significantly outperforms TOML in token efficiency.

TOML (100 products = ~3,500 tokens):

[[products]]
id = 1
name = "Widget"
price = 29.99
category = "Electronics"

[[products]]
id = 2
name = "Gadget"
price = 49.99
category = "Electronics"

# ... 98 more entries with repeated headers

TOON (100 products = ~1,960 tokens - 44% savings):

products[100]{id,name,price,category}:
  1, Widget, 29.99, Electronics
  2, Gadget, 49.99, Electronics
  # ... compact rows continue

Performance Benchmarks

Metric	TOML	TOON	Savings
Config file (20 settings)	180	145	19%
User list (100 users)	3,500	1,960	44%
Product catalog (1000 items)	35,000	18,200	48%
Monthly cost (10K requests)	$350	$182	$2,016/yr

Type System Comparison

Type	TOML	TOON
Strings	Yes (basic, literal, multiline)	Yes (minimal quoting)
Integers	Yes (with underscores)	Yes
Floats	Yes (with inf/nan)	Yes
Booleans	Yes	Yes
Datetime	Native (RFC 3339)	String representation
Arrays	Yes	Yes (with tabular optimization)

When to Use Which?

Stick with TOML if:

You're writing application configuration files.
You need pyproject.toml or Cargo.toml compatibility.
Datetime handling is critical.
Section headers improve organization for your use case.
You're not concerned about token costs.

Switch to TOON if:

You're sending data to LLMs for processing.
Your data includes arrays of objects (tables).
Token costs are a significant concern.
You want faster LLM response generation.
You're converting from JSON for API optimization.

Final Verdict

TOML excels as a configuration format where human editability and clear section organization matter most. For LLM applications with tabular data, TOON delivers 44-48% token savings while maintaining readability.

For advanced features like querying and schema validation, check our TOML vs TONL comparison. You can also explore API cost optimization or see our complete format comparison guide.

TOML vs TOON: Configuration vs Token-Optimized Data Formats