TOON is a new token-efficient data format designed for AI workflows. It reduces syntactic overhead, cuts LLM token costs by up to 60%, and improves reasoning accuracy compared to JSON. This article explains how TOON works, where it excels, and why it’s emerging as an AI-native data standard.
In the evolving landscape of Artificial Intelligence, data formats play an important role in enabling communication between systems and large language models (LLMs). For nearly two decades, JSON(java script object notation) has dominated as the standard for structured format for data exchange, powering APIs, mobile applications, cloud services, and web systems. The simplicity of JSON i.e its cross-language compatibility and readability have made it indispensable. However the advent of LLMs has introduced new set of challenges, where token consumption directly impacts costs, performance and stability.
This problem is being addressed with the development of TOON(Token-Oriented Object Notation). TOON is a compact, human-readable serialisation format specially engineered for LLM interactions.
This language addresses the inefficiencies of traditional formats by minimizing syntactic overhead, thereby reducing token usage while preserving data integrity. This format is not positioned as a wholesale replacement for JSON but as a complementary tool optimized for AI-driven workflows. By aligning with how LLMs process information, TOON enhances reasoning accuracy and lowers operational expenses for developers building AI-heavy applications.
This article provides a comprehensive overview of the language, drawing on its core features, syntax, benefits, and practical implementations. It examines why JSON’s design, while effective for human-centric systems, encounters limitations in the AI era, and how TOON offers a targeted solution.
The Limitations of JSON in AI Workflows
JSON was architected with human developers in mind, emphasising data readability and ease of debugging. Its structure although conveying clarity from its usage of curly braces, square brackets, colons, commas and quotation marks – introduces redundancy when processed by LLMs.
In LLM contexts, every character contributes to token count, and tokens equate to computational resources and financial costs.
Consider a basic JSON example representing user data:
{
“users”: [
{ “id”: 1, “name”: “Alice”, “role”: “admin” },
{ “id”: 2, “name”: “Bob”, “role”: “user” }
]
}
This structure repeats keys for each object, encloses strings in quotes, and uses punctuation to delineate elements. When scaled to large datasets—such as thousands of records or nested hierarchies—the token overhead escalates.
Developers working with AI encounter this in scenarios like:
- Supplying structured context to LLMs for prompt engineering
- Handling extensive datasets in agentic workflows
- Embedding schemas within prompts
Benchmarks indicate that JSON consumes significantly more tokens than necessary. This inefficiency not only increases API costs but also slows down system performance, particularly in token-limited environments.
Moreover, LLMs perform better with consistent, pattern-based inputs. JSON’s verbose syntax can hinder this, leading to reduced accuracy in data extraction, transformation, and validation tasks. As AI applications grow—encompassing search engines, copilots, and autonomous agents—managing token usage has become a critical bottleneck.
TOON: A Token Efficient Alternative
TOON adopts a declarative, table-like syntax that eliminates unnecessary punctuation, declares keys once per block and uses indentation to denote hierarchy. The equivalent TOON representation of the earlier JSON example is :
users[2]{id,name,role}:
1,Alice,admin
2,Bob,user
This format declares the array length (e.g., [2]) and field headers ({id,name,role}) upfront, followed by comma-separated data rows. The result is a 30-60% reduction in token count for uniform arrays of objects, with savings peaking for large, flat datasets.
TOON’s design draws inspiration from YAML’s indentation-based structure and CSV’s tabular efficiency but adds explicit instructions like array lengths and field declarations to aid LLM parsing. This makes it particularly suitable for uniform data, where all objects share the same primitive-valued fields.
Deep-Dive into TOON Syntax
TOON’s syntax is minimalistic yet expressive, supporting objects, arrays, nesting and tabular representations.
-
Simple Objects: Key-value pairs separated by colons, with indentation for nesting.
name: Pradeep
age: 39
city: Indore -
Arrays of Values: Declared with length, followed by comma-separated items.
colors[3]: red,green,blue
-
Arrays of Objects: Headers in curly braces, data in rows
users[2]{id,name}:
1,Pradeep
2,Bob
- Nested Structures: Indentation handles hierarchy without additional symbols.
user:
id: 1
name: Pradeep
profile:
age: 36
city: Indore
- Nested Arrays: Combine lists and objects seamlessly.
teams[1]:
– name: Team Alpha
members[2]{id,name}:
1,Pradeep
2,Bob
TOON also supports optional key folding for single-key wrappers which reduces indentation levels and hence token consumption levels. TOON keeps things simple: regular words do not need quotes, and values like numbers of booleans work naturally without any extra syntax. Such a syntactical flexibility ensures that TOON remains human-readable while being schema-aware that allows more reliable conversion to and from JSON.
Benefits of Adoption
The advantages of this language extend beyond token savings, impacting various aspects of AI development:
- Cost Reduction: By cutting token usage by up to 60%, TOON lowers expenses in LLM API calls, especially for high-volume applications like data analytics or agent frameworks.
- Improved LLM Performance: Tabular formats align with LLMs’ pattern recognition, boosting accuracy in retrieval (up to 99.6% in benchmarks), aggregation (54.4%), and filtering (56.3%). Structural guards enable better validation, with TOON achieving 70% accuracy in validation tasks versus JSON’s 50%.
- Efficiency in Workflows: In serverless AI environments, TOON accelerates processing by minimizing payload size. It excels in prompt engineering, where structured data must be embedded without bloating contexts.
- Benchmarked Superiority: Across datasets like uniform employee records (100 rows) and e-commerce orders (50 rows), TOON consistently outperforms JSON in token efficiency (e.g., 2,518 vs. 6,360 tokens) while maintaining or exceeding accuracy (73.8% vs. 68.3%).
- Versatility: TOON supports multiple LLMs, with accuracy rates like 90.9% on GPT-5-nano and 87.6% on Gemini-2.5-flash, demonstrating broad compatibility.
Practical Implementation of TOON
TOON integrates seamlessly into existing pipelines via libraries in several languages.
- JavaScript/TypeScript: Install via npm (npm install @toon-format/toon). Use encode to convert JSON to TOON and decode for the reverse.
import { encode, decode } from “@toon-format/toon”;
const data = { users: [{ id: 1, name: “Pradeep”, role: “admin” }, { id: 2, name: “Bob”, role: “user” }] };
const toonStr = encode(data);
// Output: users[2]{id,name,role}: 1,Pradeep,admin 2,Bob,user
const jsonObj = decode(toonStr);
- Python: Install with pip (pip install python-toon). Similar encode/decode functions are available.
from toon import encode, decode
data = {“name”: “Pradeep”, “age”: 36}
print(encode(data)) # Output: name: Pradeep\nage: 36
When JSON Remains Preferable and Hybrid Approaches
TOON shines for uniform, tabular data but is less ideal for deeply nested, non-uniform structures (where JSON-compact may use fewer tokens) or pure flat tables (where CSV is more compact). JSON excels in scenarios requiring strict schema validation, variable object shapes, or standard interoperability.
A hybrid model is recommended: Use JSON for application-to-API exchanges and TOON for application-to-LLM interactions. This leverages JSON’s ubiquity while harnessing TOON’s efficiency.
The Future Outlook for TOON
TOON is gaining adoption in AI-focused domains, including agent frameworks, LLM pipelines, fine-tuning, dataset compression, and semantic indexing. As structured prompts become standard, it’s role in exchanging tables, logs, and states with models is expected to expand.
Early implementations and benchmarks underscore its potential to define AI-native data interchange, much like JSON defined web data.
This new language represents a pragmatic evolution in data formats, tailored to the demands of the AI era. By reducing token overhead, enhancing LLM reasoning, and integrating easily with existing tools, it provides developers with a cost-effective means to optimize AI workflows. While JSON retains its place in broader ecosystems, TOON’s specialized design positions it as an essential component in the AI stack. For organizations prioritizing efficiency in LLM applications, adopting TOON offers tangible advantages in performance and scalability.