Token-Oriented Object Notation (TOON)

Token-Oriented Object Notation is a compact, human-readable encoding of the JSON data model that minimizes tokens and makes structure easy for models to follow.

TOON combines YAML's indentation-based structure for nested objects with CSV-style tabular forms for uniform data. Its sweet spot is uniform objects – same fields across items, whether in an array or keyed by ID – reaching CSV-like compactness while adding explicit structure that helps LLMs parse and validate data reliably. For deeply nested or non-uniform data, JSON may be more efficient.

Think of it as a translation layer: use JSON programmatically, and encode it as TOON for LLM input – a drop-in, lossless representation of the JSON you already have.

Tip

The TOON format is stable, but also an idea in progress. Nothing's set in stone – help shape where it goes by contributing to the spec or sharing feedback.

Why TOON?
Key Features
When Not to Use TOON
Benchmarks
Installation & Quick Start
CLI
Using TOON with LLMs
Ecosystem
Documentation
Media Type & File Extension
📋 Full Specification

Why TOON?

LLM tokens cost money – and JSON spends a lot of them on structure. A weather forecast in TOON:

location:
  city: Berlin
  country: DE
  units: metric
alerts[2]: frost,wind
forecast[3]{day,temp{min,max},condition,rainChance}:
  Mon,-2,4,snow,80
  Tue,1,7,cloudy,20
  Wed,3,11,sunny,5

The same data as JSON – ~117 tokens against TOON's ~66:

{
  "location": {
    "city": "Berlin",
    "country": "DE",
    "units": "metric"
  },
  "alerts": [
    "frost",
    "wind"
  ],
  "forecast": [
    {
      "day": "Mon",
      "temp": {
        "min": -2,
        "max": 4
      },
      "condition": "snow",
      "rainChance": 80
    },
    {
      "day": "Tue",
      "temp": {
        "min": 1,
        "max": 7
      },
      "condition": "cloudy",
      "rainChance": 20
    },
    {
      "day": "Wed",
      "temp": {
        "min": 3,
        "max": 11
      },
      "condition": "sunny",
      "rainChance": 5
    }
  ]
}

Three things are happening at once in the TOON above. Two are forms – one rendering of a value, picked automatically from the data's shape – and the third is a header feature:

alerts[2]: frost,wind is inline form: a primitive array on its header line.
forecast[3]{day,…}: is tabular form: the field list is declared once in the header, then one row per element.
temp{min,max} inside that header is a nested field group: the uniform nested temp objects fold into the header while rows stay flat.

The third form is keyed tabular, for objects whose values are uniform objects – config maps, feature flags, records by ID. The colon after the length ([2:]) marks it, and each row carries its own key:

JSON TOON

JSON	TOON
{ "environments": { "production": { "region": "eu-central-1", "replicas": 6, "debug": false }, "staging": { "region": "eu-central-1", "replicas": 2, "debug": true } } }	`environments[2:]{region,replicas,debug}: production: eu-central-1,6,false staging: eu-central-1,2,true`

{
  "environments": {
    "production": { "region": "eu-central-1", "replicas": 6, "debug": false },
    "staging": { "region": "eu-central-1", "replicas": 2, "debug": true }
  }
}

environments[2:]{region,replicas,debug}:
  production: eu-central-1,6,false
  staging: eu-central-1,2,true

Anything that fits none of these – mixed types, non-uniform objects – falls back to the fourth form, list form: one - item per element, or a bare - for an empty object. Those four cover the shapes; the Format Overview covers the rest.

Tip

Try it on your own data – no install required:

cat data.json | npx @toon-format/cli --stats

It prints the TOON alongside what the conversion saved – on the weather forecast above, that's:

ℹ Token estimates: ~117 (JSON) → ~66 (TOON)
✔ Saved ~51 tokens (-43.6%)

Key Features

📊 Token-Efficient & Accurate: Matches JSON's retrieval accuracy with fewer tokens – see Benchmarks.
🔁 JSON Data Model: Encodes the same objects, arrays, and primitives as JSON with deterministic, lossless round-trips.
🛤️ LLM-Friendly Guardrails: [N] declares how many rows, {fields} how wide – so truncated or malformed output can't slip through.
📐 Minimal Syntax: Uses indentation instead of braces and minimizes quoting, giving YAML-like readability with CSV-style compactness.
🧺 Tabular Forms: Uniform objects – in an array or under keys – declare the field list once, then stream one row each.
🌐 Multi-Language Ecosystem: Official implementations, plus dozens of community ports, all targeting one spec with a shared conformance test suite.

When Not to Use TOON

TOON excels with arrays of uniform objects. Reach for something else when:

Structures are deeply nested or non-uniform (tabular eligibility ≈ 0%) – compact JSON often wins outright.
Arrays are semi-uniform (~40–60% eligibility) – savings shrink; stay on JSON if your pipeline already speaks it.
Data is purely tabular – CSV is smaller. TOON's ~5–10% overhead buys declared lengths, field lists, and delimiter scoping, which is a reliability trade, not a size one.
Latency dominates – some deployments (notably local or quantized models) process compact JSON faster despite the higher token count. Measure TTFT and total time on your own setup.

Benchmarks below quantify the token and accuracy trade-offs; latency is the one you have to measure yourself.

Benchmarks

Two tracks, so every comparison is like-for-like:

Mixed-Structure Track: Nested and semi-uniform datasets (TOON vs JSON, YAML, XML). CSV is excluded – it cannot represent these structures without lossy flattening.
Flat-Only Track: Flat, fully tabular-eligible datasets, where CSV is a fair competitor.

Retrieval Accuracy

Benchmarks test LLM comprehension across different input formats using 244 data retrieval questions on 4 models.

Show Dataset Catalog

Dataset Catalog

Dataset	Rows	Structure	CSV Support	Eligibility
Uniform employee records	100	uniform	✓	100%
E-commerce orders with nested structures	50	nested	✗	33%
Time-series analytics data	60	uniform	✓	100%
Top 100 GitHub repositories	100	uniform	✓	100%
Semi-uniform event logs	75	semi-uniform	✗	50%
Deeply nested configuration	1	deep	✗	0%
Valid complete dataset (control)	20	uniform	✓	100%
Array truncated: 3 rows removed from end	20	uniform	✓	100%
Extra rows added beyond declared length	20	uniform	✓	100%
Inconsistent field count (missing salary in row 10)	20	uniform	✓	100%
Missing required fields (no email in multiple rows)	20	uniform	✓	100%
Feature flags keyed by name	40	uniform	✗	100%
Contacts with nested address and plan groups	50	nested	✗	100%

Structure classes:

uniform: All objects have identical fields with primitive values
semi-uniform: Mix of uniform and non-uniform structures
nested: Objects with nested structures (nested objects or arrays)
deep: Highly nested with minimal tabular eligibility

CSV Support: ✓ (supported), ✗ (not supported – would require lossy flattening)

Eligibility: Percentage of arrays and keyed maps that qualify for TOON's tabular forms (uniform records whose fields are primitives or uniform nested objects folded into nested field groups)

Efficiency Ranking (Accuracy per 1K Tokens)

Each format ranked by efficiency (accuracy percentage per 1,000 tokens):

TOON           ████████████████████   29.2 acc%/1K tok  │  72.2%  ±2.8 acc  │  2,474 tokens
JSON compact   ████████████████░░░░   23.8 acc%/1K tok  │  69.0%  ±2.9 acc  │  2,892 tokens
YAML           ██████████████░░░░░░   20.1 acc%/1K tok  │  70.1%  ±2.9 acc  │  3,487 tokens
JSON           ███████████░░░░░░░░░   16.6 acc%/1K tok  │  71.4%  ±2.8 acc  │  4,308 tokens
XML            ██████████░░░░░░░░░░   14.4 acc%/1K tok  │  70.7%  ±2.9 acc  │  4,909 tokens

Efficiency score = (Accuracy % ÷ Tokens) × 1,000. Higher is better.

Tip

TOON achieves 72.2% accuracy (vs JSON's 71.4%) while using 42.6% fewer tokens.

Note

CSV is excluded from the ranking as it only supports 109 of 244 questions (flat tabular data only). While CSV is highly token-efficient for simple tabular data, it cannot represent nested structures that other formats handle.

Accuracy on Flat Datasets

Every format answers the same 109 flat-dataset questions per model, so CSV can be compared on equal footing here.

Format	Accuracy	Correct/Total	Avg Tokens
`toon`	63.1% ±4.5	275/436	1,994
`csv`	62.2% ±4.5	271/436	1,851
`json-pretty`	60.3% ±4.6	263/436	3,950
`xml`	60.1% ±4.6	262/436	4,516
`yaml`	59.9% ±4.6	261/436	3,270
`json-compact`	58.0% ±4.6	253/436	2,718

Per-Model Accuracy

Accuracy across 4 LLMs on 244 data retrieval questions:

claude-haiku-4-5-20251001
→ TOON           █████████████░░░░░░░    65.6% ±5.9 (160/244)
  JSON           █████████████░░░░░░░    63.5% ±6.0 (155/244)
  XML            ████████████░░░░░░░░    62.3% ±6.0 (152/244)
  YAML           ████████████░░░░░░░░    62.3% ±6.0 (152/244)
  JSON compact   ████████████░░░░░░░░    61.9% ±6.0 (151/244)
  CSV            ██████████░░░░░░░░░░    49.5% ±9.2 (54/109)

gemini-3.6-flash
→ TOON           ██████████████░░░░░░    69.3% ±5.8 (169/244)
  JSON           ██████████████░░░░░░    68.4% ±5.8 (167/244)
  YAML           ██████████████░░░░░░    67.6% ±5.8 (165/244)
  XML            █████████████░░░░░░░    65.2% ±5.9 (159/244)
  JSON compact   █████████████░░░░░░░    63.5% ±6.0 (155/244)
  CSV            ████████████░░░░░░░░    57.8% ±9.1 (63/109)

gpt-5.4-nano
  XML            ████████████░░░░░░░░    59.4% ±6.1 (145/244)
  JSON           ███████████░░░░░░░░░    57.4% ±6.2 (140/244)
→ TOON           ███████████░░░░░░░░░    57.0% ±6.2 (139/244)
  JSON compact   ███████████░░░░░░░░░    54.9% ±6.2 (134/244)
  YAML           ███████████░░░░░░░░░    54.5% ±6.2 (133/244)
  CSV            █████████░░░░░░░░░░░    46.8% ±9.2 (51/109)

grok-4.5
→ TOON           ███████████████████░    97.1% ±2.2 (237/244)
  JSON           ███████████████████░    96.3% ±2.5 (235/244)
  XML            ███████████████████░    95.9% ±2.6 (234/244)
  YAML           ███████████████████░    95.9% ±2.6 (234/244)
  JSON compact   ███████████████████░    95.5% ±2.7 (233/244)
  CSV            ███████████████████░    94.5% ±4.5 (103/109)

Note

Accuracy figures include Wilson 95% confidence intervals (±); when two formats' intervals overlap, the difference between them is not statistically meaningful. CSV answers only the 109 flat-dataset questions, so its per-model cells cover a smaller, easier population than the other formats.

Performance by dataset and question type

Performance by Question Type

Question Type	TOON	JSON	XML	YAML	JSON compact	CSV
Field Retrieval	97.8%	99.2%	99.2%	99.7%	98.9%	100.0%
Aggregation	48.4%	48.4%	46.0%	46.0%	45.2%	32.8%
Filtering	38.0%	41.1%	37.5%	40.1%	38.0%	33.3%
Structure Awareness	90.3%	84.0%	84.0%	79.2%	78.5%	82.8%
Structural Validation	100.0%	50.0%	80.0%	50.0%	45.0%	80.0%

Performance by Dataset

Uniform employee records

Format	Accuracy	Tokens	Correct/Total
`csv`	64.6%	2,336	106/164
`toon`	62.8%	2,537	103/164
`json-compact`	62.2%	3,919	102/164
`yaml`	64.0%	4,982	105/164
`json-pretty`	62.2%	6,326	102/164
`xml`	61.0%	7,286	100/164

E-commerce orders with nested structures

Format	Accuracy	Tokens	Correct/Total
`json-compact`	70.7%	6,875	116/164
`toon`	71.3%	7,344	117/164
`yaml`	72.0%	8,456	118/164
`json-pretty`	71.3%	10,842	117/164
`xml`	74.4%	12,180	122/164

Time-series analytics data

Format	Accuracy	Tokens	Correct/Total
`csv`	64.2%	1,408	77/120
`toon`	63.3%	1,595	76/120
`json-compact`	59.2%	2,351	71/120
`yaml`	62.5%	2,951	75/120
`json-pretty`	65.0%	3,678	78/120
`xml`	62.5%	4,386	75/120

Top 100 GitHub repositories

Format	Accuracy	Tokens	Correct/Total
`toon`	57.6%	9,017	76/132
`csv`	54.5%	8,726	72/132
`json-compact`	53.8%	11,650	71/132
`yaml`	53.8%	13,350	71/132
`json-pretty`	55.3%	15,350	73/132
`xml`	53.8%	17,304	71/132

Semi-uniform event logs

Format	Accuracy	Tokens	Correct/Total
`json-compact`	56.7%	4,793	68/120
`toon`	60.8%	5,814	73/120
`json-pretty`	60.0%	6,759	72/120
`yaml`	55.0%	5,798	66/120
`xml`	50.8%	7,668	61/120

Deeply nested configuration

Format	Accuracy	Tokens	Correct/Total
`json-compact`	91.4%	562	106/116
`yaml`	93.1%	675	108/116
`toon`	91.4%	669	106/116
`json-pretty`	94.8%	918	110/116
`xml`	94.0%	1,007	109/116

Valid complete dataset (control)

Format	Accuracy	Tokens	Correct/Total
`toon`	100.0%	566	4/4
`json-compact`	100.0%	772	4/4
`yaml`	100.0%	984	4/4
`json-pretty`	100.0%	1,259	4/4
`xml`	0.0%	1,441	0/4
`csv`	0.0%	473	0/4

Array truncated: 3 rows removed from end

Format	Accuracy	Tokens	Correct/Total
`csv`	100.0%	408	4/4
`toon`	100.0%	498	4/4
`xml`	100.0%	1,229	4/4
`json-pretty`	0.0%	1,075	0/4
`yaml`	0.0%	841	0/4
`json-compact`	0.0%	660	0/4

Extra rows added beyond declared length

Format	Accuracy	Tokens	Correct/Total
`csv`	100.0%	547	4/4
`toon`	100.0%	644	4/4
`xml`	100.0%	1,663	4/4
`json-pretty`	0.0%	1,452	0/4
`yaml`	0.0%	1,135	0/4
`json-compact`	0.0%	893	0/4

Inconsistent field count (missing salary in row 10)

Format	Accuracy	Tokens	Correct/Total
`csv`	100.0%	470	4/4
`toon`	100.0%	563	4/4
`json-compact`	75.0%	767	3/4
`xml`	100.0%	1,432	4/4
`yaml`	75.0%	977	3/4
`json-pretty`	75.0%	1,251	3/4

Missing required fields (no email in multiple rows)

Format	Accuracy	Tokens	Correct/Total
`csv`	100.0%	442	4/4
`toon`	100.0%	535	4/4
`xml`	100.0%	1,386	4/4
`yaml`	75.0%	941	3/4
`json-pretty`	75.0%	1,207	3/4
`json-compact`	50.0%	732	2/4

Feature flags keyed by name

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Format	Accuracy	Tokens	Correct/Total

toon

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