JSON to TOON Conversion: Cut Your LLM Token Usage by 30-60%

If you're working with Large Language Models (LLMs), you know that token usage directly impacts your API costs and performance. Every token counts—literally. But here's something you might not know: the way you format your data can dramatically affect how many tokens you're consuming.

Enter TOON (Token-Oriented Object Notation), a revolutionary data format specifically designed to optimize data exchange with AI models. By converting JSON to TOON, you can reduce token usage by 30-60% while actually improving LLM comprehension accuracy.

What Is TOON?

TOON is a compact, human-readable data serialization format engineered specifically for AI applications. Unlike JSON, which was designed for general-purpose data exchange, TOON optimizes for the way LLMs process information.

Think of TOON as JSON's efficient cousin—it conveys the same information but strips away unnecessary verbosity that wastes tokens without adding value for AI comprehension.

The JSON Verbosity Problem

Let's look at a typical JSON response from an API:

{ "users": [ {"id": 1, "name": "Alice", "role": "admin", "salary": 75000}, {"id": 2, "name": "Bob", "role": "user", "salary": 65000}, {"id": 3, "name": "Charlie", "role": "user", "salary": 70000} ] }

Token count: 257 tokens

Notice the problem? Every object in the array repeats the same keys: "id", "name", "role", "salary". For three users, those keys appear nine times. It's like starting every sentence in a paragraph with the same words—technically correct, but wasteful.

The TOON Solution

Here's the same data in TOON format:

users[3]{id,name,role,salary}: 1,Alice,admin,75000 2,Bob,user,65000 3,Charlie,user,70000

Token count: 166 tokens (35% reduction!)

TOON declares the keys once in the header users[3]{id,name,role,salary}: and then streams just the values. The structure is preserved, the data is identical, but the representation is dramatically more efficient.

Why TOON Outperforms JSON for AI

1. Fewer Tokens, Lower Costs

With 30-60% fewer tokens, your API costs drop proportionally. For high-volume applications making thousands of LLM calls daily, this translates to substantial savings.

2. Better LLM Accuracy

Surprisingly, TOON doesn't just save tokens—it improves accuracy. Benchmarks show TOON achieves 73.9% accuracy compared to JSON's 69.7% across multiple models including GPT-4, Claude, and Gemini.

3. More Context in Your Window

With the same context window, you can fit significantly more data. This means richer prompts, more examples, and better AI responses.

4. Optimized for Tabular Data

TOON's tabular array syntax is perfect for database results, API responses, and analytics data—exactly the kind of structured information you typically feed to LLMs.

Real-World Token Savings

Here's a comparison across different formats for the same dataset:

When comparing different data formats for the same dataset, the differences are striking. TOON leads the pack with just 2,744 tokens and the highest accuracy at 73.9%, making it the most efficient choice overall. Minified JSON performs reasonably well with 3,081 tokens and 70.7% accuracy, offering a good balance for general use. YAML comes in at 3,719 tokens with 69.0% accuracy, providing fair efficiency but falling behind in both metrics. Standard JSON requires 4,545 tokens while achieving 69.7% accuracy, making it less efficient despite being widely used. XML performs worst across the board, consuming 5,167 tokens with only 67.1% accuracy.

TOON uses 39.6% fewer tokens than standard JSON while delivering higher accuracy—a rare combination of efficiency and quality.

When Should You Use TOON?

Perfect Use Cases:

Database query results with uniform rows
API responses containing lists of similar objects
Analytics data for LLM analysis
RAG systems where context window optimization is critical
High-volume applications where token costs add up
Chatbot contexts requiring structured data

When to Stick with JSON:

Deeply nested, irregular structures
Existing pipelines where migration costs outweigh savings
Pure tabular data (CSV might be even better)
Systems requiring universal compatibility

Converting JSON to TOON: Quick Start

Try It Online

The easiest way to see TOON in action is with our JSON to TOON converter. Simply paste your JSON and instantly see the TOON equivalent along with token savings.

Programmatic Conversion

Python:

from py_toon_format import encode, decode data = {"users": [{"id": 1, "name": "Alice"}]} toon_string = encode(data)

JavaScript/Node.js:

import { encode, decode } from '@toon-format/toon'; const data = {users: [{id: 1, name: 'Alice'}]}; const toonString = encode(data);

Command Line:

npx @toon-format/cli data.json -o data.toon --stats

TOON Syntax: The Essentials

Simple Objects

Keys and values separated by colons, just like YAML:

name: Alice age: 30 city: Boston

Nested Objects

Indentation-based hierarchy:

user: name: Alice contact: email: alice@example.com phone: 555-1234

Arrays of Objects (The Power Feature)

Tabular format with header declaration:

products[3]{id,name,price}: 1,Laptop,999 2,Mouse,29 3,Keyboard,79

The syntax products[3]{id,name,price}: tells us:

Array name: products
Length: [3] items
Columns: {id,name,price}
Values follow on subsequent indented lines

Handling Special Cases

Data with commas? Use pipe delimiters:

addresses[2]{street,city,country}|: 123 Main St, Suite 100|Boston|USA 456 Oak Ave, Apt 5B|Seattle|USA

Data with pipes? Use tab delimiters:

items[2 ]{sku qty description}: A1 2 Widget (Blue|Red) B2 1 Gadget (Multi-use)

Measuring Your Savings

Before converting your entire pipeline, measure the impact:

Take a representative JSON payload
Convert it to TOON using our converter tool
Check the token comparison statistics
Calculate projected cost savings based on your API volume

For example, if you're sending 10,000 API calls per day with an average of 1,000 tokens of JSON data, a 40% reduction means:

Before: 10,000,000 tokens/day
After: 6,000,000 tokens/day
Savings: 4,000,000 tokens/day

At typical LLM pricing, that can translate to hundreds or thousands of dollars per month.

Best Practices for JSON to TOON Conversion

1. Start with high-volume endpoints: Convert the API calls you make most frequently first for maximum impact.

2. Validate conversions: Always verify that converted TOON data round-trips correctly back to JSON.

3. Use hybrid approaches: Keep JSON for storage and APIs, convert to TOON only for LLM communication.

4. Cache conversions: Convert once and reuse the TOON strings rather than converting repeatedly.

5. Test with your specific model: Run benchmarks with your actual LLM and prompts to verify improvements.

TOON isn't trying to replace JSON everywhere—it's solving a specific problem: inefficient data representation when communicating with LLMs. If you're working with AI models and dealing with structured data, converting JSON to TOON is one of the easiest optimizations you can implement.

With 30-60% token savings, improved accuracy, and growing library support across major languages, TOON represents the future of LLM-optimized data formats.

Ready to cut your LLM token costs? Try our free JSON to TOON converter and see how much you could save.