Benchmarks

The benchmarks on this page measure TOON's performance across two key dimensions:

• Retrieval Accuracy: How well LLMs understand and extract information from different input formats.
• Token Efficiency: How many tokens each format requires to represent the same data.

Benchmarks are organized into two tracks to ensure fair comparisons:

• Mixed-Structure Track: Datasets with nested or semi-uniform structures (TOON vs JSON, YAML, XML). CSV excluded as it cannot properly represent these structures.
• Flat-Only Track: Datasets with flat tabular structures where CSV is applicable (CSV vs TOON vs JSON, YAML, XML).

Retrieval Accuracy

Benchmarks test LLM comprehension across different input formats using 209 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	11	deep	✗	0%
Valid complete dataset (control)	20	uniform	✓	100%
Array truncated: 3 rows removed from end	17	uniform	✓	100%
Extra rows added beyond declared length	23	uniform	✓	100%
Inconsistent field count (missing salary in row 10)	20	uniform	✓	100%
Missing required fields (no email in multiple rows)	20	uniform	✓	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 that qualify for TOON's tabular format (uniform objects with primitive values)

Efficiency Ranking (Accuracy per 1K Tokens)

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

TOON           ████████████████████   27.7 acc%/1K tok  │  76.4% acc  │  2,759 tokens
JSON compact   █████████████████░░░   23.7 acc%/1K tok  │  73.7% acc  │  3,104 tokens
YAML           ██████████████░░░░░░   19.9 acc%/1K tok  │  74.5% acc  │  3,749 tokens
JSON           ████████████░░░░░░░░   16.4 acc%/1K tok  │  75.0% acc  │  4,587 tokens
XML            ██████████░░░░░░░░░░   13.8 acc%/1K tok  │  72.1% acc  │  5,221 tokens

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

TIP

TOON achieves 76.4% accuracy (vs JSON's 75.0%) while using 39.9% fewer tokens.

Note on CSV: Excluded from ranking as it only supports 109 of 209 questions (flat tabular data only). While CSV is highly token-efficient for simple tabular data, it cannot represent nested structures that other formats handle.

Per-Model Accuracy

Accuracy across 4 LLMs on 209 data retrieval questions:

claude-haiku-4-5-20251001
→ TOON           ████████████░░░░░░░░    59.8% (125/209)
  JSON           ███████████░░░░░░░░░    57.4% (120/209)
  YAML           ███████████░░░░░░░░░    56.0% (117/209)
  XML            ███████████░░░░░░░░░    55.5% (116/209)
  JSON compact   ███████████░░░░░░░░░    55.0% (115/209)
  CSV            ██████████░░░░░░░░░░    50.5% (55/109)

gemini-3-flash-preview
  XML            ████████████████████    98.1% (205/209)
  JSON           ███████████████████░    97.1% (203/209)
  YAML           ███████████████████░    97.1% (203/209)
→ TOON           ███████████████████░    96.7% (202/209)
  JSON compact   ███████████████████░    96.7% (202/209)
  CSV            ███████████████████░    96.3% (105/109)

gpt-5-nano
→ TOON           ██████████████████░░    90.9% (190/209)
  JSON compact   ██████████████████░░    90.9% (190/209)
  JSON           ██████████████████░░    89.0% (186/209)
  CSV            ██████████████████░░    89.0% (97/109)
  YAML           █████████████████░░░    87.1% (182/209)
  XML            ████████████████░░░░    80.9% (169/209)

grok-4-1-fast-non-reasoning
→ TOON           ████████████░░░░░░░░    58.4% (122/209)
  YAML           ████████████░░░░░░░░    57.9% (121/209)
  JSON           ███████████░░░░░░░░░    56.5% (118/209)
  XML            ███████████░░░░░░░░░    54.1% (113/209)
  JSON compact   ██████████░░░░░░░░░░    52.2% (109/209)
  CSV            ██████████░░░░░░░░░░    51.4% (56/109)

TIP

TOON achieves 76.4% accuracy (vs JSON's 75.0%) while using 39.9% fewer tokens on these datasets.

Performance by dataset, model, and question type

Performance by Question Type

Question Type	TOON	JSON	YAML	JSON compact	XML	CSV
Field Retrieval	99.6%	99.3%	98.5%	98.5%	98.9%	100.0%
Aggregation	61.9%	61.9%	59.9%	58.3%	54.4%	50.9%
Filtering	56.8%	53.1%	56.3%	55.2%	51.6%	50.9%
Structure Awareness	89.0%	87.0%	84.0%	84.0%	81.0%	85.9%
Structural Validation	70.0%	60.0%	60.0%	55.0%	85.0%	80.0%

Performance by Dataset

Uniform employee records

Format	Accuracy	Tokens	Correct/Total
csv	73.2%	2,334	120/164
toon	73.2%	2,498	120/164
json-compact	73.8%	3,924	121/164
yaml	73.8%	4,959	121/164
json-pretty	73.8%	6,331	121/164
xml	74.4%	7,296	122/164

E-commerce orders with nested structures

Format	Accuracy	Tokens	Correct/Total
toon	82.3%	7,458	135/164
json-compact	78.7%	7,110	129/164
yaml	79.9%	8,755	131/164
json-pretty	79.3%	11,234	130/164
xml	77.4%	12,649	127/164

Time-series analytics data

Format	Accuracy	Tokens	Correct/Total
csv	75.0%	1,411	90/120
toon	78.3%	1,553	94/120
json-compact	74.2%	2,354	89/120
yaml	75.8%	2,954	91/120
json-pretty	75.0%	3,681	90/120
xml	72.5%	4,389	87/120

Top 100 GitHub repositories

Format	Accuracy	Tokens	Correct/Total
csv	65.9%	8,527	87/132
toon	66.7%	8,779	88/132
yaml	65.2%	13,141	86/132
json-compact	59.8%	11,464	79/132
json-pretty	63.6%	15,157	84/132
xml	56.1%	17,105	74/132

Semi-uniform event logs

Format	Accuracy	Tokens	Correct/Total
json-compact	68.3%	4,839	82/120
toon	65.0%	5,819	78/120
json-pretty	69.2%	6,817	83/120
yaml	61.7%	5,847	74/120
xml	58.3%	7,729	70/120

Deeply nested configuration

Format	Accuracy	Tokens	Correct/Total
json-compact	90.5%	568	105/116
toon	94.8%	655	110/116
yaml	93.1%	675	108/116
json-pretty	92.2%	924	107/116
xml	91.4%	1,013	106/116

Valid complete dataset (control)

Format	Accuracy	Tokens	Correct/Total
toon	100.0%	535	4/4
json-compact	100.0%	787	4/4
yaml	100.0%	992	4/4
json-pretty	100.0%	1,274	4/4
xml	25.0%	1,462	1/4
csv	0.0%	483	0/4

Array truncated: 3 rows removed from end

Format	Accuracy	Tokens	Correct/Total
csv	100.0%	413	4/4
xml	100.0%	1,243	4/4
toon	0.0%	462	0/4
json-pretty	0.0%	1,085	0/4
yaml	0.0%	843	0/4
json-compact	0.0%	670	0/4

Extra rows added beyond declared length

Format	Accuracy	Tokens	Correct/Total
csv	100.0%	550	4/4
toon	75.0%	605	3/4
json-compact	75.0%	901	3/4
xml	100.0%	1,678	4/4
yaml	75.0%	1,138	3/4
json-pretty	50.0%	1,460	2/4

Inconsistent field count (missing salary in row 10)

Format	Accuracy	Tokens	Correct/Total
csv	100.0%	480	4/4
json-compact	100.0%	782	4/4
yaml	100.0%	985	4/4
toon	100.0%	1,008	4/4
json-pretty	100.0%	1,266	4/4
xml	100.0%	1,453	4/4

Missing required fields (no email in multiple rows)

Format	Accuracy	Tokens	Correct/Total
csv	100.0%	340	4/4
xml	100.0%	1,409	4/4
toon	75.0%	974	3/4
json-pretty	50.0%	1,225	2/4
yaml	25.0%	951	1/4
json-compact	0.0%	750	0/4

Performance by Model

claude-haiku-4-5-20251001

Format	Accuracy	Correct/Total
toon	59.8%	125/209
json-pretty	57.4%	120/209
yaml	56.0%	117/209
xml	55.5%	116/209
json-compact	55.0%	115/209
csv	50.5%	55/109

gemini-3-flash-preview

Format	Accuracy	Correct/Total
xml	98.1%	205/209
json-pretty	97.1%	203/209
yaml	97.1%	203/209
toon	96.7%	202/209
json-compact	96.7%	202/209
csv	96.3%	105/109

gpt-5-nano

Format	Accuracy	Correct/Total
toon	90.9%	190/209
json-compact	90.9%	190/209
json-pretty	89.0%	186/209
csv	89.0%	97/109
yaml	87.1%	182/209
xml	80.9%	169/209

grok-4-1-fast-non-reasoning

Format	Accuracy	Correct/Total
toon	58.4%	122/209
yaml	57.9%	121/209
json-pretty	56.5%	118/209
xml	54.1%	113/209
json-compact	52.2%	109/209
csv	51.4%	56/109

What's Being Measured

This benchmark tests LLM comprehension and data retrieval accuracy across different input formats. Each LLM receives formatted data and must answer questions about it. This does not test the model's ability to generate TOON output – only to read and understand it.

Datasets Tested

Eleven datasets designed to test different structural patterns and validation capabilities:

Primary datasets:

1. Tabular (100 employee records): Uniform objects with identical fields – optimal for TOON's tabular format.
2. Nested (50 e-commerce orders): Complex structures with nested customer objects and item arrays.
3. Analytics (60 days of metrics): Time-series data with dates and numeric values.
4. GitHub (100 repositories): Real-world data from top GitHub repos by stars.
5. Event Logs (75 logs): Semi-uniform data with ~50% flat logs and ~50% with nested error objects.
6. Nested Config (1 configuration): Deeply nested configuration with minimal tabular eligibility.

Structural validation datasets:

7. Control: Valid complete dataset (baseline for validation)
8. Truncated: Array with 3 rows removed from end (tests [N] length detection)
9. Extra rows: Array with 3 additional rows beyond declared length
10. Width mismatch: Inconsistent field count (missing salary in row 10)
11. Missing fields: Systematic field omissions (no email in multiple rows)

Question Types

209 questions are generated dynamically across five categories:

• Field retrieval (33%): Direct value lookups or values that can be read straight off a record (including booleans and simple counts such as array lengths)

  • Example: "What is Alice's salary?" → 75000
  • Example: "How many items are in order ORD-0042?" → 3
  • Example: "What is the customer name for order ORD-0042?" → John Doe

• Aggregation (30%): Dataset-level totals and averages plus single-condition filters (counts, sums, min/max comparisons)

  • Example: "How many employees work in Engineering?" → 17
  • Example: "What is the total revenue across all orders?" → 45123.50
  • Example: "How many employees have salary > 80000?" → 23

• Filtering (23%): Multi-condition queries requiring compound logic (AND constraints across fields)

  • Example: "How many employees in Sales have salary > 80000?" → 5
  • Example: "How many active employees have more than 10 years of experience?" → 8

• Structure awareness (12%): Tests format-native structural affordances (TOON's [N] count and {fields}, CSV's header row)

  • Example: "How many employees are in the dataset?" → 100
  • Example: "List the field names for employees" → id, name, email, department, salary, yearsExperience, active
  • Example: "What is the department of the last employee?" → Sales

• Structural validation (2%): Tests ability to detect incomplete, truncated, or corrupted data using structural metadata

  • Example: "Is this data complete and valid?" → YES (control dataset) or NO (corrupted datasets)
  • Tests TOON's [N] length validation and {fields} consistency checking
  • Demonstrates CSV's lack of structural validation capabilities

Evaluation Process

1. Format conversion: Each dataset is converted to all 6 formats (TOON, JSON, YAML, JSON compact, XML, CSV).
2. Query LLM: Each model receives formatted data + question in a prompt and extracts the answer.
3. Validate deterministically: Answers are validated using type-aware comparison (e.g., 50000 = $50,000, Engineering = engineering, 2025-01-01 = January 1, 2025) without requiring an LLM judge.

Models & Configuration

• Models tested: claude-haiku-4-5-20251001, gemini-3-flash-preview, gpt-5-nano, grok-4-1-fast-non-reasoning
• Token counting: Using gpt-tokenizer with o200k_base encoding (GPT-5 tokenizer)
• Temperature: Not set (models use their defaults)
• Total evaluations: 209 questions × 6 formats × 4 models = 5,016 LLM calls

Token Efficiency

Token counts are measured using the GPT-5 o200k_base tokenizer via gpt-tokenizer. Savings are calculated against formatted JSON (2-space indentation) as the primary baseline, with additional comparisons to compact JSON (minified), YAML, and XML. Actual savings vary by model and tokenizer.

The benchmarks test datasets across different structural patterns (uniform, semi-uniform, nested, deeply nested) to show where TOON excels and where other formats may be better.

Mixed-Structure Track

Datasets with nested or semi-uniform structures. CSV excluded as it cannot properly represent these structures.

🛒 E-commerce orders with nested structures  ┊  Tabular: 33%
   │
   TOON                █████████████░░░░░░░    73,126 tokens
   ├─ vs JSON          (−33.3%)               109,599 tokens
   ├─ vs JSON compact  (+5.3%)                 69,459 tokens
   ├─ vs YAML          (−14.4%)                85,415 tokens
   └─ vs XML           (−40.7%)               123,344 tokens

🧾 Semi-uniform event logs  ┊  Tabular: 50%
   │
   TOON                █████████████████░░░   154,084 tokens
   ├─ vs JSON          (−15.0%)               181,201 tokens
   ├─ vs JSON compact  (+19.9%)               128,529 tokens
   ├─ vs YAML          (−0.8%)                155,397 tokens
   └─ vs XML           (−25.2%)               205,859 tokens

🧩 Deeply nested configuration  ┊  Tabular: 0%
   │
   TOON                ██████████████░░░░░░       620 tokens
   ├─ vs JSON          (−31.9%)                   911 tokens
   ├─ vs JSON compact  (+11.1%)                   558 tokens
   ├─ vs YAML          (−6.3%)                    662 tokens
   └─ vs XML           (−38.2%)                 1,003 tokens

──────────────────────────────────── Total ────────────────────────────────────
   TOON                ████████████████░░░░   227,830 tokens
   ├─ vs JSON          (−21.9%)               291,711 tokens
   ├─ vs JSON compact  (+14.7%)               198,546 tokens
   ├─ vs YAML          (−5.7%)                241,474 tokens
   └─ vs XML           (−31.0%)               330,206 tokens

Flat-Only Track

Datasets with flat tabular structures where CSV is applicable.

👥 Uniform employee records  ┊  Tabular: 100%
   │
   CSV                 ███████████████████░    47,102 tokens
   TOON                ████████████████████    49,919 tokens   (+6.0% vs CSV)
   ├─ vs JSON          (−60.7%)               127,063 tokens
   ├─ vs JSON compact  (−36.9%)                79,059 tokens
   ├─ vs YAML          (−50.1%)               100,011 tokens
   └─ vs XML           (−65.9%)               146,579 tokens

📈 Time-series analytics data  ┊  Tabular: 100%
   │
   CSV                 ██████████████████░░     8,383 tokens
   TOON                ████████████████████     9,115 tokens   (+8.7% vs CSV)
   ├─ vs JSON          (−59.0%)                22,245 tokens
   ├─ vs JSON compact  (−35.9%)                14,211 tokens
   ├─ vs YAML          (−49.0%)                17,858 tokens
   └─ vs XML           (−65.8%)                26,616 tokens

⭐ Top 100 GitHub repositories  ┊  Tabular: 100%
   │
   CSV                 ███████████████████░     8,512 tokens
   TOON                ████████████████████     8,744 tokens   (+2.7% vs CSV)
   ├─ vs JSON          (−42.3%)                15,144 tokens
   ├─ vs JSON compact  (−23.7%)                11,454 tokens
   ├─ vs YAML          (−33.4%)                13,128 tokens
   └─ vs XML           (−48.9%)                17,095 tokens

──────────────────────────────────── Total ────────────────────────────────────
   CSV                 ███████████████████░    63,997 tokens
   TOON                ████████████████████    67,778 tokens   (+5.9% vs CSV)
   ├─ vs JSON          (−58.8%)               164,452 tokens
   ├─ vs JSON compact  (−35.3%)               104,724 tokens
   ├─ vs YAML          (−48.3%)               130,997 tokens
   └─ vs XML           (−64.4%)               190,290 tokens

Show detailed examples

📈 Time-series analytics data

Savings: 13,130 tokens (59.0% reduction vs JSON)

JSON (22,245 tokens):

{
  "metrics": [
    {
      "date": "2025-01-01",
      "views": 6138,
      "clicks": 174,
      "conversions": 12,
      "revenue": 2712.49,
      "bounceRate": 0.35
    },
    {
      "date": "2025-01-02",
      "views": 4616,
      "clicks": 274,
      "conversions": 34,
      "revenue": 9156.29,
      "bounceRate": 0.56
    },
    {
      "date": "2025-01-03",
      "views": 4460,
      "clicks": 143,
      "conversions": 8,
      "revenue": 1317.98,
      "bounceRate": 0.59
    },
    {
      "date": "2025-01-04",
      "views": 4740,
      "clicks": 125,
      "conversions": 13,
      "revenue": 2934.77,
      "bounceRate": 0.37
    },
    {
      "date": "2025-01-05",
      "views": 6428,
      "clicks": 369,
      "conversions": 19,
      "revenue": 1317.24,
      "bounceRate": 0.3
    }
  ]
}

TOON (9,115 tokens):

metrics[5]{date,views,clicks,conversions,revenue,bounceRate}:
  2025-01-01,6138,174,12,2712.49,0.35
  2025-01-02,4616,274,34,9156.29,0.56
  2025-01-03,4460,143,8,1317.98,0.59
  2025-01-04,4740,125,13,2934.77,0.37
  2025-01-05,6428,369,19,1317.24,0.3

---

⭐ Top 100 GitHub repositories

Savings: 6,400 tokens (42.3% reduction vs JSON)

JSON (15,144 tokens):

{
  "repositories": [
    {
      "id": 28457823,
      "name": "freeCodeCamp",
      "repo": "freeCodeCamp/freeCodeCamp",
      "description": "freeCodeCamp.org's open-source codebase and curriculum. Learn math, programming,…",
      "createdAt": "2014-12-24T17:49:19Z",
      "updatedAt": "2025-10-28T11:58:08Z",
      "pushedAt": "2025-10-28T10:17:16Z",
      "stars": 430886,
      "watchers": 8583,
      "forks": 42146,
      "defaultBranch": "main"
    },
    {
      "id": 132750724,
      "name": "build-your-own-x",
      "repo": "codecrafters-io/build-your-own-x",
      "description": "Master programming by recreating your favorite technologies from scratch.",
      "createdAt": "2018-05-09T12:03:18Z",
      "updatedAt": "2025-10-28T12:37:11Z",
      "pushedAt": "2025-10-10T18:45:01Z",
      "stars": 430877,
      "watchers": 6332,
      "forks": 40453,
      "defaultBranch": "master"
    },
    {
      "id": 21737465,
      "name": "awesome",
      "repo": "sindresorhus/awesome",
      "description": "😎 Awesome lists about all kinds of interesting topics",
      "createdAt": "2014-07-11T13:42:37Z",
      "updatedAt": "2025-10-28T12:40:21Z",
      "pushedAt": "2025-10-27T17:57:31Z",
      "stars": 410052,
      "watchers": 8017,
      "forks": 32029,
      "defaultBranch": "main"
    }
  ]
}

TOON (8,744 tokens):

repositories[3]{id,name,repo,description,createdAt,updatedAt,pushedAt,stars,watchers,forks,defaultBranch}:
  28457823,freeCodeCamp,freeCodeCamp/freeCodeCamp,"freeCodeCamp.org's open-source codebase and curriculum. Learn math, programming,…","2014-12-24T17:49:19Z","2025-10-28T11:58:08Z","2025-10-28T10:17:16Z",430886,8583,42146,main
  132750724,build-your-own-x,codecrafters-io/build-your-own-x,Master programming by recreating your favorite technologies from scratch.,"2018-05-09T12:03:18Z","2025-10-28T12:37:11Z","2025-10-10T18:45:01Z",430877,6332,40453,master
  21737465,awesome,sindresorhus/awesome,😎 Awesome lists about all kinds of interesting topics,"2014-07-11T13:42:37Z","2025-10-28T12:40:21Z","2025-10-27T17:57:31Z",410052,8017,32029,main

Related Resources

• Formal Byte-Level Model – Mathematical analysis of byte efficiency compared to JSON
• Specification – Formal TOON specification