Data & Teknologi

TOON vs JSON: Which Data Format is More Effective in the AI Era?

Hans·March 24, 2026·8 min read

If you work with APIs or AI-based systems, you're almost certainly familiar with JSON. This format has been the backbone of digital data exchange for over two decades. But a new format is beginning to attract serious attention: TOON, or Token-Oriented Object Notation.

TOON isn't just an alternative format. It was designed from the ground up to answer a specific need in the AI era: token efficiency.

What Is TOON?

TOON is a text-based serialization format designed specifically to be sent to Large Language Models (LLMs) as input. Launched in November 2025, TOON combines YAML-style indentation-based structure for nested objects with a CSV-style tabular layout for uniform arrays.

The result is a format significantly more compact than JSON, without sacrificing machine readability. As a concrete example: the same data in JSON requires 15,145 tokens, while in TOON it only needs 8,745 tokens. A 42.3% saving from a format change alone.

Why Does This Matter?

It might sound like a minor optimization, but let's think at scale.

Every day, billions of requests are sent to AI systems worldwide. Every wasted token isn't just an API cost issue; it also affects processing speed, context capacity, and energy consumption. If a large portion of data sent to AI could be reduced by changing formats, the impact on global computing infrastructure is substantial. This isn't a small optimization. It's systemic efficiency.

Real Numbers

In tests involving 209 data retrieval questions across multiple LLMs, TOON achieved 73.9% accuracy compared to JSON's 69.7%, while using 39.6% fewer tokens. Meaning TOON isn't just cheaper, it's also more accurate.

This is interesting because a more compact format actually helps models understand data structure more easily.

Real-World Use Cases

TOON is most effective for tabular data and uniform object arrays. Some concrete examples:

E-commerce product catalogs: Sending thousands of product SKUs to AI for analysis or recommendations. With JSON, each product repeats field names repeatedly. With TOON, field names are written once in a header, similar to CSV but with explicit data types.
User lists for segmentation: AI agents analyzing the behavior of thousands of users can receive TOON data far more efficiently, fitting more data into a single context window.
Financial transaction logs: Repetitive log data with uniform structure is a perfect candidate. Thousands of entries with identical fields can be dramatically compressed.
Automated reporting pipelines: Systems that regularly send data to AI for summarization or analysis can significantly reduce API costs by switching to TOON.

Challenges Worth Noting

Adoption won't be as smooth as it might seem. There are some real challenges to navigate.

First, more careful initial setup. Unlike JSON, which can be written manually and directly, TOON requires understanding data structure before encoding. The schema-awareness that makes TOON powerful also means a learning curve upfront.

Second, loose administrative standards. Because the format is still relatively new, there are no fully mature standards yet. This can create inconsistencies across implementations, and in certain contexts could open security gaps if parsing isn't done rigorously. This challenge will likely resolve itself as the ecosystem matures.

Third, and perhaps the biggest: AI systems need to be configured to understand TOON. The models being used need to know what format is being sent, how to parse it, and how fast that process needs to be. Without proper configuration, TOON's efficiency advantages won't materialize, and could even degrade results.

Fourth, a still-developing ecosystem. Libraries for Python and JavaScript are available, but for other languages like Go, Rust, and .NET, development is still ongoing. Integration into certain technology stacks still requires extra work.

When TOON Works, When It Doesn't

TOON works best for: AI agent-to-agent communication with uniform tabular data, high-volume data pipelines to LLMs, systems where token costs are a significant concern, and automated AI-based analytics and reporting.

TOON isn't ideal yet for: deeply nested or non-uniform data structures, public web APIs requiring universal compatibility, teams not yet ready to invest time in the initial setup, or cases where human-readability is the top priority.

Closing Thoughts

JSON isn't going anywhere anytime soon. It's too deeply embedded in global digital infrastructure, from REST APIs to application configuration. And for many use cases, JSON remains the right choice.

But on the AI side, the story is different. As AI becomes increasingly widespread, every architectural data decision will have a cumulative impact on efficiency and cost. TOON offers a sensible approach: use JSON for general inter-system communication, but encode with TOON when that data is headed to an AI model.

This isn't a revolution. It's a natural evolution. And for anyone working at the intersection of data and AI, TOON is a format worth paying attention to now, before it becomes the new standard everyone scrambles to adopt.

Sources

Hans

Hans Anipar | SEO & Marketing Analytics Consultant

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