TOON vs JSON: Comprehensive Comparison for LLM Applications

When working with Large Language Models, the choice of data format has a real impact on API costs, processing speed, and model accuracy. This guide compares TOON (Token-Oriented Object Notation) with JSON (JavaScript Object Notation) to help you choose the right format for your use case.

Quick Comparison

Feature	JSON	TOON
Token Efficiency	Baseline (100%)	40-70% of JSON
LLM Accuracy	69.7%	73.9%
Best for	APIs, storage, web	LLM prompts, AI data
Structure	Nested objects	Tabular + nested
Readability	High	Very High
Validation	Schema optional	Built-in length markers
File Size	Medium	30-60% smaller
Browser Support	Native	Requires library

The Fundamental Difference

JSON: Designed for Web APIs (2001)

JSON was created for data interchange between web servers and browsers. It prioritizes:

• Universal browser support via JSON.parse() and JSON.stringify()
• Easy debugging in browser dev tools
• Simplicity over efficiency
• One-size-fits-all structure

It's been the standard for 20+ years, and for good reason. It works everywhere, everyone knows it, and it's simple to implement.

TOON: Designed for LLMs (2024)

TOON was created specifically for the age of Large Language Models. It prioritizes:

• Token efficiency (fewer tokens = lower costs)
• LLM comprehension (clearer structure = better accuracy)
• Data validation (built-in length markers)
• Cost optimization

TOON isn't trying to replace JSON everywhere—just in the specific context of LLM prompts where token efficiency matters.

Real-World Performance Benchmarks

Benchmark 1: Employee Database (100 records)

JSON

{
  "employees": [
    {
      "id": 1,
      "name": "Alice Johnson",
      "department": "Engineering",
      "salary": 120000,
      "hireDate": "2020-01-15"
    },
    {
      "id": 2,
      "name": "Bob Smith",
      "department": "Marketing",
      "salary": 95000,
      "hireDate": "2021-03-22"
    }
    // ... 98 more records
  ]
}

• Tokens: 3,245
• Characters: 8,420
• File Size: 8.2 KB

TOON

employees[100]{id,name,department,salary,hireDate}:
  1,Alice Johnson,Engineering,120000,2020-01-15
  2,Bob Smith,Marketing,95000,2021-03-22
  ...

• Tokens: 1,298 (60% savings)
• Characters: 3,360
• File Size: 3.3 KB (60% smaller)

The difference is substantial. For 100 employee records, TOON cuts the token count by almost two-thirds.

Benchmark 2: E-commerce Products

JSON: 5,892 tokens TOON: 2,356 tokens (60% savings)

Product catalogs are ideal for TOON because they're naturally tabular—each product has the same set of fields.

Benchmark 3: Time-Series Analytics

JSON: 12,450 tokens TOON: 4,980 tokens (60% savings)

Time-series data sees huge gains because it's extremely uniform: timestamp, value, maybe a few metadata fields, repeated thousands of times.

Benchmark 4: Nested Configuration

JSON: 2,134 tokens TOON: 1,920 tokens (10% savings)

For deeply nested, non-uniform data, TOON's advantage shrinks. It's still more compact, but the difference is marginal.

Token Efficiency Analysis

Why TOON Saves Tokens

Field Name Elimination

JSON repeats field names for every object. TOON declares fields once in the header. For arrays, this alone saves 40-50% of tokens.

Example: In a 100-item array, JSON repeats "id", "name", and "role" 100 times. TOON declares them once.

Reduced Punctuation

JSON requires quotes around keys and values, plus colons, commas, and braces for every object:

{"key":"value"}

TOON uses minimal syntax:

key: value

This saves another 10-15% on punctuation overhead.

Tabular Format

CSV-style rows for arrays eliminate structural overhead per item. Each row is just the values, separated by commas. This contributes 20-30% of the savings for large arrays.

Token Breakdown Example

For the array: [{"id":1,"name":"Alice"},{"id":2,"name":"Bob"}]

JSON Tokenization (using GPT tokenizer):

• [{"id":1,"name":"Alice"},{"id":2,"name":"Bob"}]
• Tokenized as: [ + { + "id" + : + 1 + , + "name" + : + "Alice" + ...
• Total: 31 tokens

TOON Tokenization:

users[2]{id,name}:
  1,Alice
  2,Bob

• Tokenized as: users[2]{id,name}: + 1,Alice + 2,Bob
• Total: 13 tokens (58% savings)

The savings compound as array size grows.

LLM Accuracy Comparison

Based on research with 209 data retrieval questions across Claude, GPT-4, Gemini, and Grok:

Metric	JSON	TOON	Improvement
Overall Accuracy	69.7%	73.9%	+4.2%
GPT-4	71.2%	75.1%	+3.9%
Claude 3	68.5%	72.8%	+4.3%
Gemini	69.4%	73.7%	+4.3%

Why TOON Improves Accuracy

Explicit Structure: The [N] length markers help models validate their understanding. If a model expects 100 items and only finds 50, it knows something's wrong.

Reduced Ambiguity: Tabular format with explicit headers is clearer than deeply nested objects. Models can reference field names explicitly.

Field Headers: The {field1,field2,...} syntax provides a clear schema upfront, making it easier for models to extract specific fields.

Less Noise: Fewer punctuation tokens mean less cognitive load for the model. There's less to parse and more signal in the data.

When to Use TOON vs JSON

Use TOON When:

Sending data to LLM prompts

ChatGPT API calls, Claude conversations, custom LLM applications—anytime you're including data in a prompt, TOON can cut costs.

Working with uniform data structures

Database query results, CSV exports, user/employee/product lists, transaction logs, analytics data. If your data is tabular or has repeated structures, TOON works well.

Token cost is a concern

High-volume API usage, large datasets in prompts, budget-constrained projects. When you're paying per token, 30-60% savings matter.

Context window is limited

Need to fit more data? Complex prompts with examples? Multi-turn conversations with context? TOON gives you more room.

Use JSON When:

Implementing web APIs

REST endpoints, HTTP responses, browser-server communication. JSON is the standard here, and there's no reason to change.

Storing configuration files

Deeply nested configs, non-uniform structures, package manifests (like package.json). JSON's flexibility is valuable here.

Working with existing tools

Database storage (MongoDB stores JSON), browser localStorage, Node.js config files. These tools expect JSON.

Non-uniform data

Objects with varying fields, highly nested structures, schema-less data. TOON's tabular format requires consistency.

Hybrid Approach: Best of Both Worlds

Many applications use both formats strategically:

// 1. Store data in JSON (database/API)
const jsonData = await fetch('/api/users').then(r => r.json());

// 2. Convert to TOON for LLM
import { encode } from '@toon-format/toon';
const toonData = encode(jsonData);

// 3. Send to LLM
const response = await openai.chat.completions.create({
  messages: [{
    role: "user",
    content: `Analyze this data:\n\`\`\`\n${toonData}\n\`\`\``
  }]
});

// 4. Store results back in JSON
await saveToDatabase(response);

This gives you:

• Standard JSON for your infrastructure
• Optimized TOON for AI/LLM tasks
• Flexibility to switch as needed
• No need to rewrite your existing code

Cost Analysis: Real-World Savings

Scenario: E-commerce Product Recommendation System

Setup:

• 500 products in database
• 1,000 recommendations per day
• GPT-4 API at $0.03 per 1K tokens

JSON Approach:

• JSON payload: ~6,000 tokens per request
• Daily tokens: 6,000 × 1,000 = 6M tokens
• Daily cost: $180
• Monthly cost: $5,400

TOON Approach:

• TOON payload: ~2,400 tokens per request (60% savings)
• Daily tokens: 2,400 × 1,000 = 2.4M tokens
• Daily cost: $72
• Monthly cost: $2,160

Savings: $3,240 per month

For a startup or small business, that's a significant cost reduction. Scale it up to enterprise volume, and you're looking at tens of thousands of dollars saved annually.

Developer Experience

JSON Advantages

• Native browser support via JSON.parse() and JSON.stringify()
• Familiar to all developers (learning curve: ~0)
• Excellent tooling: linters, formatters, validators, IDE support
• Decades of documentation and Stack Overflow answers

TOON Advantages

• Cleaner, more readable for humans (especially tabular data)
• Built-in validation via length markers
• Less verbose for arrays of objects
• Easy to explain to non-developers (looks like a table)

Learning Curve

JSON: 30 minutes to learn the basics TOON: 1 hour to master (similar to YAML)

Both are simple formats. TOON takes a bit longer because it's less familiar, but the syntax is straightforward.

Migration Strategy

From JSON to TOON

Step 1: Identify where you're sending data to LLMs

// Before
const prompt = `Here's the data: ${JSON.stringify(data)}`;

Step 2: Install the TOON library

npm install @toon-format/toon

Step 3: Convert to TOON

import { encode } from '@toon-format/toon';

// After
const toonData = encode(data);
const prompt = `Here's the data:\n\`\`\`\n${toonData}\n\`\`\``;

Step 4: Measure the savings

import { encode as tokenize } from 'gpt-tokenizer';

const jsonTokens = tokenize(JSON.stringify(data)).length;
const toonTokens = tokenize(encode(data)).length;
const savings = ((jsonTokens - toonTokens) / jsonTokens * 100).toFixed(1);

console.log(`Token savings: ${savings}%`);

Run this on your actual data to see the real-world impact. For tabular data, you'll typically see 50-60% reduction. For nested objects, 30-40% is common.

Limitations and Trade-offs

TOON Limitations

Not for all data types

Deeply nested configs and non-uniform data don't benefit as much. JSON might be clearer in these cases.

Requires a library

No native browser support means you need to install a package. It's a small dependency (~10KB), but it's still an extra step.

Smaller ecosystem

Fewer tools, libraries, and examples compared to JSON. You might need to build integrations yourself.

Encoding overhead

Converting JSON to TOON takes time (though it's usually sub-millisecond). For real-time apps with ultra-low latency requirements, this might matter.

JSON Limitations (for LLM use)

Token waste

30-60% more tokens than necessary for tabular data. This adds up fast with high-volume usage.

Verbose syntax

Lots of punctuation and repetitive structure. Makes it harder for models to parse.

No built-in validation

Can't express array lengths or field schemas in the format itself. You need external validation.

Future-Proofing Your Application

The Dual-Format Strategy

Use JSON for infrastructure, TOON for LLM prompts:

// Define data layer (JSON)
interface User {
  id: number;
  name: string;
  email: string;
}

// Storage: JSON
await db.users.insert(jsonData);

// API Response: JSON
app.get('/api/users', (req, res) => {
  res.json(users);
});

// LLM Prompts: TOON
const toonData = encode(users);
await sendToLLM(toonData);

This approach gives you:

• Standard JSON for infrastructure (databases, APIs, config files)
• Optimized TOON for AI/LLM tasks (prompts, data analysis)
• Flexibility to switch formats as needed
• No need to choose one over the other

The Bottom Line

TOON vs JSON isn't about replacement—it's about using the right tool for the right job:

• JSON excels at APIs, storage, and web communication
• TOON excels at LLM prompts and AI data optimization

For LLM applications, TOON offers measurable benefits:

• 30-60% token savings on average
• Lower API costs (often thousands of dollars per month)
• Better accuracy (+4.2% on data retrieval tasks)
• Cleaner data presentation for both humans and models

If you're working with LLMs and sending structured data in your prompts, TOON is worth trying. The migration is straightforward, and the savings can be significant.

Recommendation: Keep using JSON for your infrastructure. Convert to TOON when sending data to LLMs. It's a one-line change with substantial cost savings.

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Ready to try TOON?

• Convert your JSON to TOON
• Compare formats in the playground
• Read the complete TOON guide
• Learn how to use TOON with ChatGPT