wehub-resource-sync
587 lines
24 KiB
Markdown
587 lines
24 KiB
Markdown
---
|
||
description: Retrieval accuracy and token efficiency results for TOON across mixed-structure and flat-only tracks.
|
||
---
|
||
|
||
# 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
|
||
|
||
<!-- automd:file src="../../benchmarks/results/retrieval-accuracy.md" -->
|
||
|
||
Benchmarks test LLM comprehension across different input formats using 209 data retrieval questions on 4 models.
|
||
|
||
<details>
|
||
<summary><strong>Show Dataset Catalog</strong></summary>
|
||
|
||
#### 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)
|
||
|
||
</details>
|
||
|
||
#### 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.
|
||
|
||
<details>
|
||
<summary><strong>Performance by dataset, model, and question type</strong></summary>
|
||
|
||
#### 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 |
|
||
|
||
</details>
|
||
|
||
#### 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
|
||
|
||
<!-- /automd -->
|
||
|
||
## Token Efficiency
|
||
|
||
Token counts are measured using the GPT-5 `o200k_base` tokenizer via [`gpt-tokenizer`](https://github.com/niieani/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.
|
||
|
||
<!-- automd:file src="../../benchmarks/results/token-efficiency.md" -->
|
||
|
||
#### 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
|
||
```
|
||
|
||
<details>
|
||
<summary><strong>Show detailed examples</strong></summary>
|
||
|
||
#### 📈 Time-series analytics data
|
||
|
||
**Savings:** 13,130 tokens (59.0% reduction vs JSON)
|
||
|
||
**JSON** (22,245 tokens):
|
||
|
||
```json
|
||
{
|
||
"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):
|
||
|
||
```json
|
||
{
|
||
"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
|
||
```
|
||
|
||
</details>
|
||
|
||
<!-- /automd -->
|
||
|
||
## Related Resources
|
||
|
||
- [Formal Byte-Level Model](/reference/efficiency-formalization) – Mathematical analysis of byte efficiency compared to JSON
|
||
- [Specification](/reference/spec) – Formal TOON specification
|