XML vs TOON: Complete Format Comparison for LLM Optimization
Compare XML vs TOON for LLM prompts: token efficiency, verbosity analysis, and how TOON saves up to 69% on AI API costs.
XML (eXtensible Markup Language) is a survivor. It outlived SGML, it coexists with JSON, and it still powers the world's biggest banking and healthcare systems. But XML was designed for a different era—an era of "Documents," not "Tokens."
In the era of Generative AI, where we pay for every byte of context we feed to a model, XML's verbosity is a liability. TOON offers a modern alternative: it preserves the hierarchical structure that XML does so well, but strips away the "Markup" to leave only the "Meaning."
Documents vs Data: A Philosophical Split
XML's roots are in SGML (Standard Generalized Markup Language), the same parent as HTML. It was designed to markup text documents.
<note>
<to>Tove</to>
<from>Jani</from>
<body>Don't forget me this weekend!</body>
</note>Its superpower is "Mixed Content":
<p>This is <b>bold</b> and this is <i>italic</i>.</p>This is amazing for publishing. It is terrible for data serialization. When you are sending a list of users to an API, you never need mixed content. You need strict Keys and Values.
TOON is for Data. It assumes structure, not prose.
note:
to: Tove
from: Jani
body: "Don't forget me this weekend!"The "Closing Tag Tax"
The most obvious inefficiency in XML is the closing tag.
If you have a tag `<internationalization_enabled>`, you must also have `</internationalization_enabled>`.
Math:
- Tag Name: 26 characters
- Brackets + Slash: 5 characters (`<`, `>`, `</`, `>`)
- Total Overhead: 57 characters to wrap a boolean `true`.
TOON Math:
- Key Name: 26 characters
- Colon + Space: 2 characters
- Total Overhead: 28 characters.
Result: TOON is consistently 50% smaller just by removing redundancy. For an LLM with a 128k Context Window, this effectively doubles your memory.
Attributes vs Elements: The Confusion
XML developers have argued for 20 years about how to represent data.
Option A (Attributes):
<user id="123" name="Alice" />This is concise, but attributes cannot contain nested structures (like a list of addresses).
Option B (Elements):
<user>
<id>123</id>
<name>Alice</name>
</user>This is flexible, but verbose.
The TOON Solution: Unification
TOON abolishes this distinction. Everything is a key-value pair.
user:
id: 123
name: AliceThis matches the mental model of modern programming languages (JSON Objects, Python Dicts, Java Maps).
Parsing Complexity: DOM vs Stream
XML parsers are legendary for their complexity.
- DOM Parsing: Loads the entire tree into memory. Explodes RAM for large files.
- SAX Parsing: Event-based (`startElement`, `endElement`). Extremely fast but painfully hard to write code for ("Callback Hell").
TOON is designed for Linear Streaming.
- It reads line by line.
- Indentation tells it the depth.
- It emits objects as they complete.
Writing a TOON parser takes an afternoon. Writing a compliant XML parser takes a year.
Token Economics: The 69% Savings
Let's look at a realistic "RAG Chunk" payload.
| Format | Content (50 chunks) | Tokens (GPT-4) | Cost |
|---|---|---|---|
| XML | Verbose Markup | 6,500 | $0.20 |
| TOON | Tabular Data | 2,015 | $0.06 |
The savings are not just money. They are Speed. Generating 2,000 tokens is 3x faster than generating 6,500. For a chatbot, this is the difference between "Snappy" and "Sluggish."
Use Cases
When XML Wins
The Document Web.
If you are writing a book (`DocBook`), a technical manual (`DITA`), or a rich text document (XHTML), XML is superior. Mixed content `
text bold text
` is a feature TOON does not attempt to replicate.When TOON Wins
The Data Web.
If you are building an API, feeding a vector database, or configuring an AI Agent, use TOON. The model doesn't care about angle brackets. It cares about relationships, values, and types.
Conclusion
XML feels like a "heavy" format because it carries the weight of 30 years of history.
TOON feels "light" because it carries only what is necessary for the task at hand: transferring structured data to intelligence.
If you are still sending XML to OpenAI, you are paying a "Legacy Tax." It's time to upgrade.
Recommended Reading
Protobuf vs TOON: Binary Speed vs Token Efficiency
Compare Google's Protocol Buffers with TOON. Learn why binary formats struggle with LLMs and how TOON provides a token-optimized alternative.
YAML vs TOON: Human-Readable Format Battle for LLM Optimization
Compare YAML vs TOON for LLM prompts: token efficiency, readability, edge cases, and which format saves more on AI API costs.
TOML vs TOON: Configuration vs Token-Optimized Data Formats
Compare TOML vs TOON for LLM applications: token efficiency, nested structures, config use cases, and cost savings analysis.