TOON vs JSON: A Developer’s Guide to Token-Efficient Structured Data for LLMs

Introduction

JSON is one of the most widely used formats in modern software development. APIs, web applications, configuration files, and integrations all rely on it because it is predictable, well supported, and easy for machines to parse.

But working with JSON inside AI and LLM workflows introduces a different problem: how efficiently can structured data be represented inside a prompt?

Large JSON payloads can contain a lot of punctuation, repeated object keys, and structural characters. When the same information needs to be passed to an LLM repeatedly, that representation can become unnecessarily verbose.

While exploring better ways to work with structured data in AI-assisted development, I came across TOON (Token-Oriented Object Notation). TOON is designed as a compact, human-readable encoding of JSON-shaped data, with a particular focus on LLM-oriented workflows.

In this article, I’ll explain how TOON differs from JSON, where its format is most useful, what I found interesting from a developer perspective, and why it should be treated as a complementary representation rather than a replacement for JSON.

What Is TOON?

TOON is a data encoding format designed to represent the JSON data model in a more compact and human-readable way.

Instead of using JSON's braces, repeated keys, and punctuation-heavy structure everywhere, TOON uses a combination of indentation and tabular notation. It also makes array sizes and object fields explicit, which gives the encoded data clear structural boundaries.

The format is especially interesting for LLM prompts and structured context, where token usage and readability can both matter.

TOON should not be thought of as “JSON but prettier.” Its main value is providing a compact representation of JSON-shaped data for workflows where humans and language models need to inspect or reason about structured information.

The Problem with Large JSON Payloads

Consider a simple list of users represented as JSON:

{
  "users": [
    {
      "id": 101,
      "name": "Ada",
      "role": "admin"
    },
    {
      "id": 102,
      "name": "Bob",
      "role": "editor"
    },
    {
      "id": 103,
      "name": "Cleo",
      "role": "viewer"
    }
  ]
}

JSON is clear and universally understood, but the same object structure is repeated for every item.

For a small payload this is not a problem. For hundreds or thousands of similar records, however, the representation becomes much more verbose.

This matters when the data is being inserted into an LLM prompt because the representation itself consumes tokens.

The Same Data in TOON

The same structure can be represented using TOON's tabular notation:

users[3]{id,name,role}:
  101,Ada,admin
  102,Bob,editor
  103,Cleo,viewer

The difference is important.

Instead of repeating id, name, and role for every object, TOON declares the fields once:

users[3]{id,name,role}:

The [3] indicates the number of rows, while {id,name,role} declares the fields for each row.

The values can then be represented as compact rows:

101,Ada,admin
102,Bob,editor
103,Cleo,viewer

This is where TOON becomes particularly interesting for structured data containing uniform arrays of objects.

JSON vs TOON: What Actually Changes?

The underlying information does not need to change. The representation changes.

The important point is that TOON does not need to replace JSON inside your application.

A practical architecture can keep JSON as the application's canonical data format and encode that data as TOON only when preparing context for an LLM.

Why TOON Is Interesting for LLM Workflows

LLM applications frequently pass structured information into prompts:

• User profiles

• Product catalogs

• Search results

• Database records

• Analytics data

• Tool outputs

• Configuration data

• Retrieved documents with metadata

When these structures contain many repeated objects, a compact representation can make the prompt easier to inspect and potentially reduce the number of tokens required to represent the same information.

TOON also provides explicit structural information such as array lengths and field declarations. That makes the intended shape of the data visible instead of relying entirely on repeated JSON syntax.

A Practical LLM Workflow

One useful way to think about TOON is as a translation layer between your application and an LLM.

Application Data
      ↓
     JSON
      ↓
 TOON Encoder
      ↓
   LLM Prompt
      ↓
 TOON Decoder
      ↓
     JSON
      ↓
Application Logic

Your application can continue using the data structures and APIs it already understands.

TOON can simply become an LLM-facing representation when compact structured context is useful.

This is a much more practical approach than trying to replace JSON across an entire application.

Where TOON Works Best

TOON is particularly interesting when the data contains many objects with the same fields.

For example:

products[4]{id,name,price,stock}:
  101,Laptop,999,12
  102,Monitor,299,34
  103,Keyboard,79,120
  104,Mouse,39,250

The schema is declared once and each record becomes a compact row.

This representation can be especially useful when sending large structured datasets to an LLM for tasks such as:

• Summarization

• Filtering

• Classification

• Comparison

• Data analysis

• Retrieval-augmented generation

• Structured question answering

• Tool context

Where JSON Is Still the Better Choice

TOON is not a universal replacement for JSON.

JSON remains the better choice when you need:

• Standard API responses

• Broad interoperability

• Existing SDK and library support

• Database or application storage

• Service-to-service communication

• A universally recognized data interchange format

• Maximum compatibility with existing systems

If two backend services need to communicate, there is usually little reason to introduce TOON simply because it is more compact.

JSON is already excellent at that job.

Where TOON May Not Be the Best Fit

TOON's compact tabular representation is strongest when data has a predictable, repeated structure.

It may be less attractive for highly irregular data, deeply nested structures with little repetition, or situations where the surrounding ecosystem already expects JSON or another established format.

The right question is therefore not:

“Should I use TOON instead of JSON?”

A better question is:

“Where would a more compact representation improve this particular workflow?”

My Developer Experience

What initially caught my attention was not the syntax itself, but how quickly a structured dataset could become understandable at a glance.

When working with AI-assisted development, I often move between application data, API responses, database results, and prompts. JSON is excellent for the application side, but large nested payloads can become visually noisy when pasted into a prompt or discussed during development.

TOON gave me another option: keep the application data in JSON, but use a more compact representation when the data is primarily being consumed as context by an LLM.

For uniform collections, the difference is particularly noticeable because the field names only need to be declared once.

That does not make TOON universally better. It simply makes it useful for a specific class of problems.

Token Efficiency: An Important but Context-Dependent Benefit

One of the reasons TOON has attracted attention is its focus on reducing the representation size of structured data in LLM workflows.

However, token savings are not guaranteed for every dataset.

The amount of savings depends on factors such as:

• How repetitive the data is

• How deeply nested it is

• How many fields each object contains

• The tokenizer used by the model

• The size and shape of the dataset

For this reason, token efficiency should be measured against your own real prompts rather than assumed from a single example.

A good experiment is to take the same dataset, encode it as JSON and TOON, and compare the resulting token counts and task accuracy for the LLM and model you actually use.

A Simple Evaluation Approach

If you want to evaluate TOON in a real project, start with one representative workflow.

Step 1: Choose a real dataset

Pick something your application already sends to an LLM, such as product data, search results, or database records.

Step 2: Encode it as JSON

Use your existing production representation as the baseline.

Step 3: Encode the same data as TOON

Do not change the underlying information. Only change the representation.

Step 4: Compare token usage

Measure the token count for both representations using the tokenizer relevant to your target model.

Step 5: Compare task quality

Ask the model to perform the same task with both representations and compare accuracy, consistency, and response quality.

Step 6: Measure the real trade-off

Consider not only tokens, but also latency, implementation complexity, maintainability, and compatibility.

This gives you a much more useful answer than simply assuming one format is better.

TOON Is a Complement to JSON, Not Its Replacement

The most useful way I currently see TOON is as a specialized representation layer.

Keep JSON where JSON already works well.

Use TOON when structured data needs to be presented to an LLM and a compact, readable representation provides a practical advantage.

This separation also keeps your architecture simple:

┌───────────────┐
│ Application   │
│ Data / APIs   │
└───────┬───────┘
        │
       JSON
        │
┌───────▼───────┐
│ TOON Encoder  │
└───────┬───────┘
        │
       TOON
        │
┌───────▼───────┐
│      LLM      │
└───────────────┘

This keeps the internal application architecture familiar while allowing you to experiment with a representation optimized for an AI-facing workflow.

Final Thoughts

JSON is not going away. It remains one of the most practical and widely supported ways to exchange structured data between software systems.

TOON solves a different problem.

It offers a compact, human-readable representation of JSON-shaped data that can be particularly useful when structured information is being passed into LLM workflows. Its strongest use case appears when the data contains repeated, uniform objects where field declarations can be shared across rows.

From a developer perspective, that makes TOON worth experimenting with — not as a replacement for JSON, but as another tool in the toolbox.

If your application already sends large structured payloads to an LLM, try encoding one real dataset as both JSON and TOON. Measure the token usage, compare the model's results, and decide whether the trade-off makes sense for your workflow.

Call to Action

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