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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
parent df68417d8b
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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>