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test: add benchmarks for compact vs. pretty JSON

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Johann Schopplich
2025-10-30 15:02:51 +01:00
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@@ -57,26 +57,30 @@ The benchmarks test datasets that favor TOON's strengths (uniform tabular data).
### Token Efficiency
```
⭐ GitHub Repositories ██████████████░░░░░░░░░░░ 8,745 tokens
vs JSON: 15,145 (-42.3%)
vs YAML: 13,129 (-33.4%)
vs XML: 17,095 (-48.8%)
⭐ GitHub Repositories ██████████████░░░░░░░░░░░ 8,745 tokens
vs JSON (-42.3%) 15,145
vs JSON compact (-23.7%) 11,455
vs YAML (-33.4%) 13,129
vs XML (-48.8%) 17,095
📈 Daily Analytics ██████████░░░░░░░░░░░░░░░ 4,507 tokens
vs JSON: 10,977 (-58.9%)
vs YAML: 8,810 (-48.8%)
vs XML: 13,128 (-65.7%)
📈 Daily Analytics ██████████░░░░░░░░░░░░░░░ 4,507 tokens
vs JSON (-58.9%) 10,977
vs JSON compact (-35.7%) 7,013
vs YAML (-48.8%) 8,810
vs XML (-65.7%) 13,128
🛒 E-Commerce Order ████████████████░░░░░░░░░ 166 tokens
vs JSON: 257 (-35.4%)
vs YAML: 197 (-15.7%)
vs XML: 271 (-38.7%)
🛒 E-Commerce Order ████████████████░░░░░░░░░ 166 tokens
vs JSON (-35.4%) 257
vs JSON compact (-2.9%) 171
vs YAML (-15.7%) 197
vs XML (-38.7%) 271
─────────────────────────────────────────────────────────────────────
Total █████████████░░░░░░░░░░░░ 13,418 tokens
vs JSON: 26,379 (-49.1%)
vs YAML: 22,136 (-39.4%)
vs XML: 30,494 (-56.0%)
Total ██████████████░░░░░░░░░░░ 13,418 tokens
vs JSON (-49.1%) 26,379
vs JSON compact (-28.0%) 18,639
vs YAML (-39.4%) 22,136
vs XML (-56.0%) 30,494
```
<details>
@@ -218,7 +222,7 @@ metrics[5]{date,views,clicks,conversions,revenue,bounceRate}:
<!-- /automd -->
> [!NOTE]
> Measured with [`gpt-tokenizer`](https://github.com/niieani/gpt-tokenizer) using `o200k_base` encoding (used by GPT-5 and other modern models). Savings will vary across models and tokenizers.
> Token savings are measured against formatted JSON (2-space indentation) as the primary baseline. Additional comparisons include compact JSON (minified), YAML, and XML to provide a comprehensive view across common data formats. Measured with [`gpt-tokenizer`](https://github.com/niieani/gpt-tokenizer) using `o200k_base` encoding (GPT-5 tokenizer). Actual savings vary by model and tokenizer.
<!-- automd:file src="./benchmarks/results/retrieval-accuracy.md" -->
@@ -228,35 +232,39 @@ Accuracy across **4 LLMs** on 154 data retrieval questions:
```
gpt-5-nano
→ toon ███████████████████░ 96.1% (148/154)
csv ██████████████████░░ 90.3% (139/154)
yaml ██████████████████░░ 89.0% (137/154)
json ██████████████████░░ 87.7% (135/154)
xml █████████████████░░░ 83.8% (129/154)
→ TOON ███████████████████░ 96.1% (148/154)
CSV ██████████████████░░ 91.6% (141/154)
YAML ██████████████████░░ 91.6% (141/154)
JSON compact ██████████████████░░ 91.6% (141/154)
XML █████████████████░░░ 87.0% (134/154)
JSON █████████████████░░░ 86.4% (133/154)
claude-haiku-4-5-20251001
yaml ██████████░░░░░░░░░░ 49.4% (76/154)
→ toon ██████████░░░░░░░░░░ 48.1% (74/154)
csv ██████████░░░░░░░░░░ 48.1% (74/154)
json █████████░░░░░░░░░░░ 47.4% (73/154)
xml █████████░░░░░░░░░░░ 46.8% (72/154)
JSON ██████████░░░░░░░░░░ 50.0% (77/154)
YAML ██████████░░░░░░░░░░ 49.4% (76/154)
→ TOON ██████████░░░░░░░░░░ 48.7% (75/154)
XML ██████████░░░░░░░░░░ 48.1% (74/154)
CSV █████████░░░░░░░░░░░ 47.4% (73/154)
JSON compact █████████░░░░░░░░░░░ 44.2% (68/154)
gemini-2.5-flash
csv ██████████████████░░ 87.7% (135/154)
xml █████████████████░░░ 85.1% (131/154)
→ toon █████████████████░░░ 83.8% (129/154)
json ████████████████░░░░ 78.6% (121/154)
yaml ███████████████░░░░░ 76.6% (118/154)
CSV ██████████████████░░ 87.7% (135/154)
XML ██████████████████░░ 87.7% (135/154)
→ TOON █████████████████░░░ 86.4% (133/154)
YAML ████████████████░░░░ 79.9% (123/154)
JSON compact ████████████████░░░░ 79.9% (123/154)
JSON ███████████████░░░░░ 76.6% (118/154)
grok-4-fast-non-reasoning
→ toon ██████████░░░░░░░░░░ 48.7% (75/154)
json ██████████░░░░░░░░░░ 48.1% (74/154)
xml █████████░░░░░░░░░░░ 47.4% (73/154)
yaml █████████░░░░░░░░░░░ 46.8% (72/154)
csv █████████░░░░░░░░░░░ 45.5% (70/154)
→ TOON ██████████░░░░░░░░░░ 49.4% (76/154)
JSON ██████████░░░░░░░░░░ 48.7% (75/154)
XML █████████░░░░░░░░░░░ 46.1% (71/154)
YAML █████████░░░░░░░░░░░ 46.1% (71/154)
JSON compact █████████░░░░░░░░░░░ 45.5% (70/154)
CSV █████████░░░░░░░░░░░ 44.2% (68/154)
```
**Key tradeoff:** TOON achieves **69.2% accuracy** (vs JSON's 65.4%) while using **46.3% fewer tokens** on these datasets.
**Key tradeoff:** TOON achieves **70.1% accuracy** (vs JSON's 65.4%) while using **46.3% fewer tokens** on these datasets.
<details>
<summary><strong>Performance by dataset and model</strong></summary>
@@ -267,41 +275,45 @@ grok-4-fast-non-reasoning
| Format | Accuracy | Tokens | Correct/Total |
| ------ | -------- | ------ | ------------- |
| `csv` | 67.0% | 2,337 | 134/200 |
| `toon` | 66.5% | 2,483 | 133/200 |
| `yaml` | 65.5% | 4,969 | 131/200 |
| `json` | 63.5% | 6,347 | 127/200 |
| `xml` | 66.5% | 7,314 | 133/200 |
| `csv` | 65.5% | 2,337 | 131/200 |
| `toon` | 67.5% | 2,483 | 135/200 |
| `json-compact` | 65.5% | 3,943 | 131/200 |
| `yaml` | 68.5% | 4,969 | 137/200 |
| `xml` | 69.5% | 7,314 | 139/200 |
| `json-pretty` | 64.5% | 6,347 | 129/200 |
##### E-commerce orders with nested structures
| Format | Accuracy | Tokens | Correct/Total |
| ------ | -------- | ------ | ------------- |
| `toon` | 78.8% | 5,967 | 126/160 |
| `csv` | 71.9% | 6,735 | 115/160 |
| `yaml` | 71.9% | 7,328 | 115/160 |
| `json` | 73.1% | 9,694 | 117/160 |
| `xml` | 73.8% | 10,992 | 118/160 |
| `csv` | 76.3% | 6,735 | 122/160 |
| `json-compact` | 70.6% | 5,962 | 113/160 |
| `yaml` | 72.5% | 7,328 | 116/160 |
| `json-pretty` | 76.9% | 9,694 | 123/160 |
| `xml` | 73.1% | 10,992 | 117/160 |
##### Time-series analytics data
| Format | Accuracy | Tokens | Correct/Total |
| ------ | -------- | ------ | ------------- |
| `csv` | 67.6% | 1,393 | 92/136 |
| `toon` | 67.6% | 1,515 | 92/136 |
| `yaml` | 64.7% | 2,938 | 88/136 |
| `json` | 68.4% | 3,665 | 93/136 |
| `xml` | 66.2% | 4,376 | 90/136 |
| `toon` | 68.4% | 1,515 | 93/136 |
| `csv` | 65.4% | 1,393 | 89/136 |
| `json-compact` | 64.7% | 2,341 | 88/136 |
| `yaml` | 66.2% | 2,938 | 90/136 |
| `json-pretty` | 64.7% | 3,665 | 88/136 |
| `xml` | 66.9% | 4,376 | 91/136 |
##### Top 100 GitHub repositories
| Format | Accuracy | Tokens | Correct/Total |
| ------ | -------- | ------ | ------------- |
| `csv` | 64.2% | 8,513 | 77/120 |
| `toon` | 62.5% | 8,745 | 75/120 |
| `yaml` | 57.5% | 13,129 | 69/120 |
| `json` | 55.0% | 15,145 | 66/120 |
| `xml` | 53.3% | 17,095 | 64/120 |
| `toon` | 65.0% | 8,745 | 78/120 |
| `csv` | 62.5% | 8,513 | 75/120 |
| `json-compact` | 58.3% | 11,455 | 70/120 |
| `yaml` | 56.7% | 13,129 | 68/120 |
| `xml` | 55.8% | 17,095 | 67/120 |
| `json-pretty` | 52.5% | 15,145 | 63/120 |
#### Performance by Model
@@ -310,40 +322,44 @@ grok-4-fast-non-reasoning
| Format | Accuracy | Correct/Total |
| ------ | -------- | ------------- |
| `toon` | 96.1% | 148/154 |
| `csv` | 90.3% | 139/154 |
| `yaml` | 89.0% | 137/154 |
| `json` | 87.7% | 135/154 |
| `xml` | 83.8% | 129/154 |
| `csv` | 91.6% | 141/154 |
| `yaml` | 91.6% | 141/154 |
| `json-compact` | 91.6% | 141/154 |
| `xml` | 87.0% | 134/154 |
| `json-pretty` | 86.4% | 133/154 |
##### claude-haiku-4-5-20251001
| Format | Accuracy | Correct/Total |
| ------ | -------- | ------------- |
| `json-pretty` | 50.0% | 77/154 |
| `yaml` | 49.4% | 76/154 |
| `toon` | 48.1% | 74/154 |
| `csv` | 48.1% | 74/154 |
| `json` | 47.4% | 73/154 |
| `xml` | 46.8% | 72/154 |
| `toon` | 48.7% | 75/154 |
| `xml` | 48.1% | 74/154 |
| `csv` | 47.4% | 73/154 |
| `json-compact` | 44.2% | 68/154 |
##### gemini-2.5-flash
| Format | Accuracy | Correct/Total |
| ------ | -------- | ------------- |
| `csv` | 87.7% | 135/154 |
| `xml` | 85.1% | 131/154 |
| `toon` | 83.8% | 129/154 |
| `json` | 78.6% | 121/154 |
| `yaml` | 76.6% | 118/154 |
| `xml` | 87.7% | 135/154 |
| `toon` | 86.4% | 133/154 |
| `yaml` | 79.9% | 123/154 |
| `json-compact` | 79.9% | 123/154 |
| `json-pretty` | 76.6% | 118/154 |
##### grok-4-fast-non-reasoning
| Format | Accuracy | Correct/Total |
| ------ | -------- | ------------- |
| `toon` | 48.7% | 75/154 |
| `json` | 48.1% | 74/154 |
| `xml` | 47.4% | 73/154 |
| `yaml` | 46.8% | 72/154 |
| `csv` | 45.5% | 70/154 |
| `toon` | 49.4% | 76/154 |
| `json-pretty` | 48.7% | 75/154 |
| `xml` | 46.1% | 71/154 |
| `yaml` | 46.1% | 71/154 |
| `json-compact` | 45.5% | 70/154 |
| `csv` | 44.2% | 68/154 |
</details>
@@ -383,7 +399,7 @@ Four datasets designed to test different structural patterns (all contain arrays
#### Evaluation Process
1. **Format conversion**: Each dataset is converted to all 5 formats (TOON, CSV, XML, JSON, YAML).
1. **Format conversion**: Each dataset is converted to all 6 formats (TOON, CSV, XML, YAML, JSON, JSON compact).
2. **Query LLM**: Each model receives formatted data + question in a prompt and extracts the answer.
3. **Validate with LLM-as-judge**: `gpt-5-nano` validates if the answer is semantically correct (e.g., `50000` = `$50,000`, `Engineering` = `engineering`, `2025-01-01` = `January 1, 2025`).
@@ -392,7 +408,7 @@ Four datasets designed to test different structural patterns (all contain arrays
- **Models tested**: `gpt-5-nano`, `claude-haiku-4-5-20251001`, `gemini-2.5-flash`, `grok-4-fast-non-reasoning`
- **Token counting**: Using `gpt-tokenizer` with `o200k_base` encoding (GPT-5 tokenizer)
- **Temperature**: Not set (models use their defaults)
- **Total evaluations**: 154 questions × 5 formats × 4 models = 3,080 LLM calls
- **Total evaluations**: 154 questions × 6 formats × 4 models = 3,696 LLM calls
</details>

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@@ -4,35 +4,39 @@ Accuracy across **4 LLMs** on 154 data retrieval questions:
```
gpt-5-nano
→ toon ███████████████████░ 96.1% (148/154)
csv ██████████████████░░ 90.3% (139/154)
yaml ██████████████████░░ 89.0% (137/154)
json ██████████████████░░ 87.7% (135/154)
xml █████████████████░░░ 83.8% (129/154)
→ TOON ███████████████████░ 96.1% (148/154)
CSV ██████████████████░░ 91.6% (141/154)
YAML ██████████████████░░ 91.6% (141/154)
JSON compact ██████████████████░░ 91.6% (141/154)
XML █████████████████░░░ 87.0% (134/154)
JSON █████████████████░░░ 86.4% (133/154)
claude-haiku-4-5-20251001
yaml ██████████░░░░░░░░░░ 49.4% (76/154)
→ toon ██████████░░░░░░░░░░ 48.1% (74/154)
csv ██████████░░░░░░░░░░ 48.1% (74/154)
json █████████░░░░░░░░░░░ 47.4% (73/154)
xml █████████░░░░░░░░░░░ 46.8% (72/154)
JSON ██████████░░░░░░░░░░ 50.0% (77/154)
YAML ██████████░░░░░░░░░░ 49.4% (76/154)
→ TOON ██████████░░░░░░░░░░ 48.7% (75/154)
XML ██████████░░░░░░░░░░ 48.1% (74/154)
CSV █████████░░░░░░░░░░░ 47.4% (73/154)
JSON compact █████████░░░░░░░░░░░ 44.2% (68/154)
gemini-2.5-flash
csv ██████████████████░░ 87.7% (135/154)
xml █████████████████░░░ 85.1% (131/154)
→ toon █████████████████░░░ 83.8% (129/154)
json ████████████████░░░░ 78.6% (121/154)
yaml ███████████████░░░░░ 76.6% (118/154)
CSV ██████████████████░░ 87.7% (135/154)
XML ██████████████████░░ 87.7% (135/154)
→ TOON █████████████████░░░ 86.4% (133/154)
YAML ████████████████░░░░ 79.9% (123/154)
JSON compact ████████████████░░░░ 79.9% (123/154)
JSON ███████████████░░░░░ 76.6% (118/154)
grok-4-fast-non-reasoning
→ toon ██████████░░░░░░░░░░ 48.7% (75/154)
json ██████████░░░░░░░░░░ 48.1% (74/154)
xml █████████░░░░░░░░░░░ 47.4% (73/154)
yaml █████████░░░░░░░░░░░ 46.8% (72/154)
csv █████████░░░░░░░░░░░ 45.5% (70/154)
→ TOON ██████████░░░░░░░░░░ 49.4% (76/154)
JSON ██████████░░░░░░░░░░ 48.7% (75/154)
XML █████████░░░░░░░░░░░ 46.1% (71/154)
YAML █████████░░░░░░░░░░░ 46.1% (71/154)
JSON compact █████████░░░░░░░░░░░ 45.5% (70/154)
CSV █████████░░░░░░░░░░░ 44.2% (68/154)
```
**Key tradeoff:** TOON achieves **69.2% accuracy** (vs JSON's 65.4%) while using **46.3% fewer tokens** on these datasets.
**Key tradeoff:** TOON achieves **70.1% accuracy** (vs JSON's 65.4%) while using **46.3% fewer tokens** on these datasets.
<details>
<summary><strong>Performance by dataset and model</strong></summary>
@@ -43,41 +47,45 @@ grok-4-fast-non-reasoning
| Format | Accuracy | Tokens | Correct/Total |
| ------ | -------- | ------ | ------------- |
| `csv` | 67.0% | 2,337 | 134/200 |
| `toon` | 66.5% | 2,483 | 133/200 |
| `yaml` | 65.5% | 4,969 | 131/200 |
| `json` | 63.5% | 6,347 | 127/200 |
| `xml` | 66.5% | 7,314 | 133/200 |
| `csv` | 65.5% | 2,337 | 131/200 |
| `toon` | 67.5% | 2,483 | 135/200 |
| `json-compact` | 65.5% | 3,943 | 131/200 |
| `yaml` | 68.5% | 4,969 | 137/200 |
| `xml` | 69.5% | 7,314 | 139/200 |
| `json-pretty` | 64.5% | 6,347 | 129/200 |
##### E-commerce orders with nested structures
| Format | Accuracy | Tokens | Correct/Total |
| ------ | -------- | ------ | ------------- |
| `toon` | 78.8% | 5,967 | 126/160 |
| `csv` | 71.9% | 6,735 | 115/160 |
| `yaml` | 71.9% | 7,328 | 115/160 |
| `json` | 73.1% | 9,694 | 117/160 |
| `xml` | 73.8% | 10,992 | 118/160 |
| `csv` | 76.3% | 6,735 | 122/160 |
| `json-compact` | 70.6% | 5,962 | 113/160 |
| `yaml` | 72.5% | 7,328 | 116/160 |
| `json-pretty` | 76.9% | 9,694 | 123/160 |
| `xml` | 73.1% | 10,992 | 117/160 |
##### Time-series analytics data
| Format | Accuracy | Tokens | Correct/Total |
| ------ | -------- | ------ | ------------- |
| `csv` | 67.6% | 1,393 | 92/136 |
| `toon` | 67.6% | 1,515 | 92/136 |
| `yaml` | 64.7% | 2,938 | 88/136 |
| `json` | 68.4% | 3,665 | 93/136 |
| `xml` | 66.2% | 4,376 | 90/136 |
| `toon` | 68.4% | 1,515 | 93/136 |
| `csv` | 65.4% | 1,393 | 89/136 |
| `json-compact` | 64.7% | 2,341 | 88/136 |
| `yaml` | 66.2% | 2,938 | 90/136 |
| `json-pretty` | 64.7% | 3,665 | 88/136 |
| `xml` | 66.9% | 4,376 | 91/136 |
##### Top 100 GitHub repositories
| Format | Accuracy | Tokens | Correct/Total |
| ------ | -------- | ------ | ------------- |
| `csv` | 64.2% | 8,513 | 77/120 |
| `toon` | 62.5% | 8,745 | 75/120 |
| `yaml` | 57.5% | 13,129 | 69/120 |
| `json` | 55.0% | 15,145 | 66/120 |
| `xml` | 53.3% | 17,095 | 64/120 |
| `toon` | 65.0% | 8,745 | 78/120 |
| `csv` | 62.5% | 8,513 | 75/120 |
| `json-compact` | 58.3% | 11,455 | 70/120 |
| `yaml` | 56.7% | 13,129 | 68/120 |
| `xml` | 55.8% | 17,095 | 67/120 |
| `json-pretty` | 52.5% | 15,145 | 63/120 |
#### Performance by Model
@@ -86,40 +94,44 @@ grok-4-fast-non-reasoning
| Format | Accuracy | Correct/Total |
| ------ | -------- | ------------- |
| `toon` | 96.1% | 148/154 |
| `csv` | 90.3% | 139/154 |
| `yaml` | 89.0% | 137/154 |
| `json` | 87.7% | 135/154 |
| `xml` | 83.8% | 129/154 |
| `csv` | 91.6% | 141/154 |
| `yaml` | 91.6% | 141/154 |
| `json-compact` | 91.6% | 141/154 |
| `xml` | 87.0% | 134/154 |
| `json-pretty` | 86.4% | 133/154 |
##### claude-haiku-4-5-20251001
| Format | Accuracy | Correct/Total |
| ------ | -------- | ------------- |
| `json-pretty` | 50.0% | 77/154 |
| `yaml` | 49.4% | 76/154 |
| `toon` | 48.1% | 74/154 |
| `csv` | 48.1% | 74/154 |
| `json` | 47.4% | 73/154 |
| `xml` | 46.8% | 72/154 |
| `toon` | 48.7% | 75/154 |
| `xml` | 48.1% | 74/154 |
| `csv` | 47.4% | 73/154 |
| `json-compact` | 44.2% | 68/154 |
##### gemini-2.5-flash
| Format | Accuracy | Correct/Total |
| ------ | -------- | ------------- |
| `csv` | 87.7% | 135/154 |
| `xml` | 85.1% | 131/154 |
| `toon` | 83.8% | 129/154 |
| `json` | 78.6% | 121/154 |
| `yaml` | 76.6% | 118/154 |
| `xml` | 87.7% | 135/154 |
| `toon` | 86.4% | 133/154 |
| `yaml` | 79.9% | 123/154 |
| `json-compact` | 79.9% | 123/154 |
| `json-pretty` | 76.6% | 118/154 |
##### grok-4-fast-non-reasoning
| Format | Accuracy | Correct/Total |
| ------ | -------- | ------------- |
| `toon` | 48.7% | 75/154 |
| `json` | 48.1% | 74/154 |
| `xml` | 47.4% | 73/154 |
| `yaml` | 46.8% | 72/154 |
| `csv` | 45.5% | 70/154 |
| `toon` | 49.4% | 76/154 |
| `json-pretty` | 48.7% | 75/154 |
| `xml` | 46.1% | 71/154 |
| `yaml` | 46.1% | 71/154 |
| `json-compact` | 45.5% | 70/154 |
| `csv` | 44.2% | 68/154 |
</details>
@@ -159,7 +171,7 @@ Four datasets designed to test different structural patterns (all contain arrays
#### Evaluation Process
1. **Format conversion**: Each dataset is converted to all 5 formats (TOON, CSV, XML, JSON, YAML).
1. **Format conversion**: Each dataset is converted to all 6 formats (TOON, CSV, XML, YAML, JSON, JSON compact).
2. **Query LLM**: Each model receives formatted data + question in a prompt and extracts the answer.
3. **Validate with LLM-as-judge**: `gpt-5-nano` validates if the answer is semantically correct (e.g., `50000` = `$50,000`, `Engineering` = `engineering`, `2025-01-01` = `January 1, 2025`).
@@ -168,6 +180,6 @@ Four datasets designed to test different structural patterns (all contain arrays
- **Models tested**: `gpt-5-nano`, `claude-haiku-4-5-20251001`, `gemini-2.5-flash`, `grok-4-fast-non-reasoning`
- **Token counting**: Using `gpt-tokenizer` with `o200k_base` encoding (GPT-5 tokenizer)
- **Temperature**: Not set (models use their defaults)
- **Total evaluations**: 154 questions × 5 formats × 4 models = 3,080 LLM calls
- **Total evaluations**: 154 questions × 6 formats × 4 models = 3,696 LLM calls
</details>

36
benchmarks/results/token-efficiency.md
View File

@@ -1,26 +1,30 @@
### Token Efficiency
```
⭐ GitHub Repositories ██████████████░░░░░░░░░░░ 8,745 tokens
vs JSON: 15,145 (-42.3%)
vs YAML: 13,129 (-33.4%)
vs XML: 17,095 (-48.8%)
⭐ GitHub Repositories ██████████████░░░░░░░░░░░ 8,745 tokens
vs JSON (-42.3%) 15,145
vs JSON compact (-23.7%) 11,455
vs YAML (-33.4%) 13,129
vs XML (-48.8%) 17,095
📈 Daily Analytics ██████████░░░░░░░░░░░░░░░ 4,507 tokens
vs JSON: 10,977 (-58.9%)
vs YAML: 8,810 (-48.8%)
vs XML: 13,128 (-65.7%)
📈 Daily Analytics ██████████░░░░░░░░░░░░░░░ 4,507 tokens
vs JSON (-58.9%) 10,977
vs JSON compact (-35.7%) 7,013
vs YAML (-48.8%) 8,810
vs XML (-65.7%) 13,128
🛒 E-Commerce Order ████████████████░░░░░░░░░ 166 tokens
vs JSON: 257 (-35.4%)
vs YAML: 197 (-15.7%)
vs XML: 271 (-38.7%)
🛒 E-Commerce Order ████████████████░░░░░░░░░ 166 tokens
vs JSON (-35.4%) 257
vs JSON compact (-2.9%) 171
vs YAML (-15.7%) 197
vs XML (-38.7%) 271
─────────────────────────────────────────────────────────────────────
Total █████████████░░░░░░░░░░░░ 13,418 tokens
vs JSON: 26,379 (-49.1%)
vs YAML: 22,136 (-39.4%)
vs XML: 30,494 (-56.0%)
Total ██████████████░░░░░░░░░░░ 13,418 tokens
vs JSON (-49.1%) 26,379
vs JSON compact (-28.0%) 18,639
vs YAML (-39.4%) 22,136
vs XML (-56.0%) 30,494
```
<details>

6
benchmarks/scripts/fetch-github-repos.ts
View File

@@ -1,10 +1,11 @@
import * as fsp from 'node:fs/promises'
import * as path from 'node:path'
import process from 'node:process'
import * as prompts from '@clack/prompts'
import { ofetch } from 'ofetch'
import pMap from 'p-map'
import { BENCHMARKS_DIR } from '../src/constants'
import { ensureDir, saveJsonFile } from '../src/utils'
import { ensureDir } from '../src/utils'
prompts.intro('GitHub Repositories Fetcher')
@@ -79,7 +80,8 @@ async function saveRepos(repos: Record<string, any>[]): Promise<void> {
const outputFile = path.join(outputDir, 'github-repos.json')
await ensureDir(outputDir)
await saveJsonFile(outputFile, repos)
const jsonOutput = JSON.stringify(repos, undefined, 2)
await fsp.writeFile(outputFile, `${jsonOutput}\n`, 'utf-8')
const relativePath = path.relative(BENCHMARKS_DIR, outputFile)
prompts.log.info(`Result saved to \`${relativePath}\``)

140
benchmarks/scripts/token-efficiency-benchmark.ts
View File

@@ -3,7 +3,7 @@ import * as path from 'node:path'
import * as prompts from '@clack/prompts'
import { encode } from '../../src/index'
import githubRepos from '../data/github-repos.json' with { type: 'json' }
import { BENCHMARKS_DIR, ROOT_DIR } from '../src/constants'
import { BENCHMARKS_DIR, FORMATTER_DISPLAY_NAMES, ROOT_DIR } from '../src/constants'
import { generateAnalyticsData, generateOrderData } from '../src/datasets'
import { formatters } from '../src/formatters'
import { createProgressBar, ensureDir, tokenize } from '../src/utils'
@@ -50,118 +50,102 @@ const BENCHMARK_EXAMPLES = [
prompts.intro('Token Efficiency Benchmark')
// Calculate total savings
let totalJsonTokens = 0
let totalToonTokens = 0
let totalXmlTokens = 0
let totalYamlTokens = 0
const results: BenchmarkResult[] = []
const totalTokensByFormat: Record<string, number> = {}
for (const example of BENCHMARK_EXAMPLES) {
const data = example.getData()
const jsonString = JSON.stringify(data, undefined, 2)
const toonString = encode(data)
const xmlString = formatters.xml!(data)
const yamlString = formatters.yaml!(data)
// Calculate tokens for each format
const formatMetrics: FormatMetrics[] = []
const tokensByFormat: Record<string, number> = {}
const jsonTokens = tokenize(jsonString)
const toonTokens = tokenize(toonString)
const xmlTokens = tokenize(xmlString)
const yamlTokens = tokenize(yamlString)
for (const [formatName, formatter] of Object.entries(formatters)) {
const formattedString = formatter(data)
const tokens = tokenize(formattedString)
tokensByFormat[formatName] = tokens
totalTokensByFormat[formatName] = (totalTokensByFormat[formatName] || 0) + tokens
}
const jsonSavings = jsonTokens - toonTokens
const xmlSavings = xmlTokens - toonTokens
const yamlSavings = yamlTokens - toonTokens
totalJsonTokens += jsonTokens
totalToonTokens += toonTokens
totalXmlTokens += xmlTokens
totalYamlTokens += yamlTokens
// Calculate savings vs TOON
const toonTokens = tokensByFormat.toon!
for (const [formatName, tokens] of Object.entries(tokensByFormat)) {
const savings = tokens - toonTokens
formatMetrics.push({
name: formatName,
tokens,
savings,
savingsPercent: formatName === 'toon' ? '0.0' : ((savings / tokens) * 100).toFixed(1),
})
}
results.push({
name: example.name,
emoji: example.emoji,
description: example.description,
data,
formats: [
{
name: 'toon',
tokens: toonTokens,
savings: 0,
savingsPercent: '0.0',
},
{
name: 'json',
tokens: jsonTokens,
savings: jsonSavings,
savingsPercent: ((jsonSavings / jsonTokens) * 100).toFixed(1),
},
{
name: 'xml',
tokens: xmlTokens,
savings: xmlSavings,
savingsPercent: ((xmlSavings / xmlTokens) * 100).toFixed(1),
},
{
name: 'yaml',
tokens: yamlTokens,
savings: yamlSavings,
savingsPercent: ((yamlSavings / yamlTokens) * 100).toFixed(1),
},
],
formats: formatMetrics,
showDetailed: example.showDetailed,
})
}
const totalJsonSavings = totalJsonTokens - totalToonTokens
const totalJsonSavingsPercent = ((totalJsonSavings / totalJsonTokens) * 100).toFixed(1)
const totalXmlSavings = totalXmlTokens - totalToonTokens
const totalXmlSavingsPercent = ((totalXmlSavings / totalXmlTokens) * 100).toFixed(1)
const totalYamlSavings = totalYamlTokens - totalToonTokens
const totalYamlSavingsPercent = ((totalYamlSavings / totalYamlTokens) * 100).toFixed(1)
// Calculate total savings percentages
const totalToonTokens = totalTokensByFormat.toon!
const totalSavingsPercent: Record<string, string> = {}
for (const [formatName, totalTokens] of Object.entries(totalTokensByFormat)) {
if (formatName === 'toon') {
totalSavingsPercent[formatName] = '0.0'
}
else {
const savings = totalTokens - totalToonTokens
totalSavingsPercent[formatName] = ((savings / totalTokens) * 100).toFixed(1)
}
}
// Generate ASCII bar chart visualization (stacked compact format)
const formatOrder = ['json-pretty', 'json-compact', 'yaml', 'xml']
const datasetRows = results
.map((result) => {
const toon = result.formats.find(f => f.name === 'toon')!
const json = result.formats.find(f => f.name === 'json')!
const xml = result.formats.find(f => f.name === 'xml')!
const yaml = result.formats.find(f => f.name === 'yaml')!
const percentage = Number.parseFloat(json.savingsPercent)
const percentage = Number.parseFloat(result.formats.find(f => f.name === 'json-pretty')!.savingsPercent)
const bar = createProgressBar(100 - percentage, 100) // Invert to show TOON tokens
const toonStr = toon.tokens.toLocaleString('en-US')
const jsonStr = json.tokens.toLocaleString('en-US')
const xmlStr = xml.tokens.toLocaleString('en-US')
const yamlStr = yaml.tokens.toLocaleString('en-US')
const line1 = `${result.emoji} ${result.name.padEnd(25)} ${bar} ${toonStr.padStart(6)} tokens`
const line2 = ` vs JSON: ${jsonStr.padStart(6)} (-${json.savingsPercent}%)`
const line3 = ` vs YAML: ${yamlStr.padStart(6)} (-${yaml.savingsPercent}%)`
const line4 = ` vs XML: ${xmlStr.padStart(6)} (-${xml.savingsPercent}%)`
const line1 = `${result.emoji} ${result.name.padEnd(25)} ${bar} ${toonStr.padStart(6)} tokens`
return `${line1}\n${line2}\n${line3}\n${line4}`
const comparisonLines = formatOrder.map((formatName) => {
const format = result.formats.find(f => f.name === formatName)!
const label = FORMATTER_DISPLAY_NAMES[formatName] || formatName.toUpperCase()
const labelWithSavings = `vs ${label} (-${format.savingsPercent}%)`.padEnd(28)
const tokenStr = format.tokens.toLocaleString('en-US').padStart(6)
return ` ${labelWithSavings}${tokenStr}`
})
return [line1, ...comparisonLines].join('\n')
})
.join('\n\n')
// Add separator and totals row
const separator = '─────────────────────────────────────────────────────────────────────'
// Calculate bar for totals (TOON vs average of JSON+YAML+XML)
const averageComparisonTokens = (totalJsonTokens + totalYamlTokens + totalXmlTokens) / 3
// Calculate bar for totals (TOON vs average of comparison formats)
const comparisonTokens = formatOrder.map(name => totalTokensByFormat[name]!)
const averageComparisonTokens = comparisonTokens.reduce((a, b) => a + b, 0) / comparisonTokens.length
const totalPercentage = (totalToonTokens / averageComparisonTokens) * 100
const totalBar = createProgressBar(totalPercentage, 100)
const totalLine1 = `Total ${totalBar} ${totalToonTokens.toLocaleString('en-US').padStart(6)} tokens`
const totalLine2 = ` vs JSON: ${totalJsonTokens.toLocaleString('en-US').padStart(6)} (-${totalJsonSavingsPercent}%)`
const totalLine3 = ` vs YAML: ${totalYamlTokens.toLocaleString('en-US').padStart(6)} (-${totalYamlSavingsPercent}%)`
const totalLine4 = ` vs XML: ${totalXmlTokens.toLocaleString('en-US').padStart(6)} (-${totalXmlSavingsPercent}%)`
const totalLine1 = `Total ${totalBar} ${totalToonTokens.toLocaleString('en-US').padStart(6)} tokens`
const barChartSection = `${datasetRows}\n\n${separator}\n${totalLine1}\n${totalLine2}\n${totalLine3}\n${totalLine4}`
const totalComparisonLines = formatOrder.map((formatName) => {
const label = FORMATTER_DISPLAY_NAMES[formatName] || formatName.toUpperCase()
const tokens = totalTokensByFormat[formatName]!
const percent = totalSavingsPercent[formatName]!
const labelWithSavings = `vs ${label} (-${percent}%)`.padEnd(28)
const tokenStr = tokens.toLocaleString('en-US').padStart(6)
return ` ${labelWithSavings}${tokenStr}`
})
const barChartSection = `${datasetRows}\n\n${separator}\n${totalLine1}\n${totalComparisonLines.join('\n')}`
// Generate detailed examples (only for selected examples)
// Note: Large datasets are truncated for display readability in the report.
@@ -185,7 +169,7 @@ const detailedExamples = results
const separator = i < filtered.length - 1 ? '\n\n---' : ''
const json = result.formats.find(f => f.name === 'json')!
const json = result.formats.find(f => f.name === 'json-pretty')!
const toon = result.formats.find(f => f.name === 'toon')!
return `#### ${result.emoji} ${result.name}

14
benchmarks/src/constants.ts
View File

@@ -14,7 +14,7 @@ export const BENCHMARKS_DIR: string = url.fileURLToPath(new URL('../', import.me
export const MODEL_RPM_LIMITS: Record<string, number | undefined> = {
'claude-haiku-4-5-20251001': 50,
'gemini-2.5-flash': 25,
'gpt-5-nano': undefined,
'gpt-5-nano': 50,
'grok-4-fast-non-reasoning': 50,
}
@@ -23,6 +23,18 @@ export const MODEL_RPM_LIMITS: Record<string, number | undefined> = {
*/
export const DEFAULT_CONCURRENCY = 10
/**
* Display names for data format types
*/
export const FORMATTER_DISPLAY_NAMES: Record<string, string> = {
'json-pretty': 'JSON',
'json-compact': 'JSON compact',
'toon': 'TOON',
'csv': 'CSV',
'xml': 'XML',
'yaml': 'YAML',
} as const
/**
* Progress bar configuration
*/

11
benchmarks/src/formatters.ts
View File

@@ -12,11 +12,12 @@ import { encode as encodeToon } from '../../src/index'
* CSV has inherent limitations with nested structures (see `toCSV` docs).
*/
export const formatters: Record<string, (data: unknown) => string> = {
json: data => JSON.stringify(data, undefined, 2),
toon: data => encodeToon(data),
csv: data => toCSV(data),
xml: data => toXML(data),
yaml: data => stringifyYAML(data),
'json-pretty': data => JSON.stringify(data, undefined, 2),
'json-compact': data => JSON.stringify(data),
'toon': data => encodeToon(data),
'csv': data => toCSV(data),
'xml': data => toXML(data),
'yaml': data => stringifyYAML(data),
}
/**

13
benchmarks/src/report.ts
View File

@@ -1,7 +1,7 @@
import type { EvaluationResult, FormatResult, Question } from './types'
import * as fsp from 'node:fs/promises'
import * as path from 'node:path'
import { BENCHMARKS_DIR } from './constants'
import { BENCHMARKS_DIR, FORMATTER_DISPLAY_NAMES } from './constants'
import { datasets } from './datasets'
import { models } from './evaluate'
import { createProgressBar, ensureDir, tokenize } from './utils'
@@ -49,7 +49,7 @@ export function generateMarkdownReport(
tokenCounts: Record<string, number>,
): string {
const toon = formatResults.find(r => r.format === 'toon')
const json = formatResults.find(r => r.format === 'json')
const json = formatResults.find(r => r.format === 'json-pretty')
const modelIds = models.map(m => m.modelId)
const modelNames = modelIds.filter(id => results.some(r => r.model === id))
@@ -71,10 +71,11 @@ export function generateMarkdownReport(
const formatLines = modelResults.map((result) => {
const bar = createProgressBar(result.accuracy, 1, 20)
const accuracyStr = `${(result.accuracy * 100).toFixed(1)}%`.padStart(6)
const countStr = `(${result.correctCount}/${result.totalCount})`
const accuracyString = `${(result.accuracy * 100).toFixed(1)}%`.padStart(6)
const countString = `(${result.correctCount}/${result.totalCount})`
const prefix = result.format === 'toon' ? '→ ' : ' '
return `${prefix}${result.format.padEnd(12)} ${bar} ${accuracyStr} ${countStr}`
const displayName = FORMATTER_DISPLAY_NAMES[result.format] || result.format
return `${prefix}${displayName.padEnd(12)} ${bar} ${accuracyString} ${countString}`
}).join('\n')
// Add blank line before model name, except for first model
@@ -248,7 +249,7 @@ ${totalQuestions} questions are generated dynamically across three categories:
#### Evaluation Process
1. **Format conversion**: Each dataset is converted to all ${formatCount} formats (${formatResults.map(f => f.format.toUpperCase()).join(', ')}).
1. **Format conversion**: Each dataset is converted to all ${formatCount} formats (${formatResults.map(f => FORMATTER_DISPLAY_NAMES[f.format] || f.format).join(', ')}).
2. **Query LLM**: Each model receives formatted data + question in a prompt and extracts the answer.
3. **Validate with LLM-as-judge**: \`gpt-5-nano\` validates if the answer is semantically correct (e.g., \`50000\` = \`$50,000\`, \`Engineering\` = \`engineering\`, \`2025-01-01\` = \`January 1, 2025\`).

16
benchmarks/src/utils.ts
View File

@@ -40,19 +40,3 @@ export function tokenize(text: string): number {
export async function ensureDir(dirPath: string): Promise<void> {
await fsp.mkdir(dirPath, { recursive: true })
}
/**
* Save data as formatted JSON file
*
* @param filePath - Path to save the file
* @param data - Data to serialize as JSON
* @param indent - Indentation spaces (default: 2)
*/
export async function saveJsonFile(
filePath: string,
data: unknown,
indent = 2,
): Promise<void> {
const json = JSON.stringify(data, undefined, indent)
await fsp.writeFile(filePath, `${json}\n`, 'utf-8')
}

8
src/encode/encoders.ts
View File

@@ -150,8 +150,8 @@ export function encodeArrayOfObjectsAsTabular(
depth: Depth,
options: ResolvedEncodeOptions,
): void {
const headerStr = formatHeader(rows.length, { key: prefix, fields: header, delimiter: options.delimiter, lengthMarker: options.lengthMarker })
writer.push(depth, `${headerStr}`)
const formattedHeader = formatHeader(rows.length, { key: prefix, fields: header, delimiter: options.delimiter, lengthMarker: options.lengthMarker })
writer.push(depth, `${formattedHeader}`)
writeTabularRows(rows, header, writer, depth + 1, options)
}
@@ -255,8 +255,8 @@ export function encodeObjectAsListItem(obj: JsonObject, writer: LineWriter, dept
const header = extractTabularHeader(firstValue)
if (header) {
// Tabular format for uniform arrays of objects
const headerStr = formatHeader(firstValue.length, { key: firstKey, fields: header, delimiter: options.delimiter, lengthMarker: options.lengthMarker })
writer.pushListItem(depth, headerStr)
const formattedHeader = formatHeader(firstValue.length, { key: firstKey, fields: header, delimiter: options.delimiter, lengthMarker: options.lengthMarker })
writer.pushListItem(depth, formattedHeader)
writeTabularRows(firstValue, header, writer, depth + 1, options)
}
else {