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docs: add benchmarks for gemini-2.5-flash

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Johann Schopplich
2025-10-27 16:02:51 +01:00
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benchmarks/results/accuracy/report.md
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@@ -1,24 +1,31 @@
### Retrieval Accuracy
Tested across **2 LLMs** with data retrieval tasks:
Tested across **3 LLMs** with data retrieval tasks:
```
gpt-5-nano
toon ███████████████████░ 97.5% (155/159)
markdown-kv ███████████████████░ 95.6% (152/159)
yaml ███████████████████░ 94.3% (150/159)
json ███████████████████░ 93.7% (149/159)
csv ███████████████████░ 93.7% (149/159)
toon ████████████████████ 99.4% (158/159)
yaml ███████████████████░ 95.0% (151/159)
csv ██████████████████░░ 92.5% (147/159)
json ██████████████████░░ 92.5% (147/159)
xml ██████████████████░░ 91.2% (145/159)
claude-haiku-4-5
markdown-kv ███████████████░░░░░ 76.7% (122/159)
toon ███████████████░░░░░ 75.5% (120/159)
json ███████████████░░░░░ 75.5% (120/159)
xml ███████████████░░░░░ 75.5% (120/159)
csv ███████████████░░░░░ 75.5% (120/159)
yaml ███████████████░░░░░ 74.8% (119/159)
json ███████████████░░░░░ 75.5% (120/159)
yaml ███████████████░░░░░ 74.2% (118/159)
gemini-2.5-flash
xml ██████████████████░░ 91.8% (146/159)
csv █████████████████░░░ 86.2% (137/159)
toon █████████████████░░░ 84.9% (135/159)
json ████████████████░░░░ 81.8% (130/159)
yaml ████████████████░░░░ 78.6% (125/159)
```
**Advantage:** TOON achieves **86.5% accuracy** (vs JSON's 84.6%) while using **46.3% fewer tokens**.
**Advantage:** TOON achieves **86.6% accuracy** (vs JSON's 83.2%) while using **46.3% fewer tokens**.
<details>
<summary><strong>View detailed breakdown by dataset and model</strong></summary>
@@ -29,41 +36,41 @@ claude-haiku-4-5
| Format | Accuracy | Tokens | Correct/Total |
| ------ | -------- | ------ | ------------- |
| `toon` | 86.2% | 2.483 | 100/116 |
| `csv` | 80.2% | 2.337 | 93/116 |
| `yaml` | 82.8% | 4.969 | 96/116 |
| `markdown-kv` | 84.5% | 6.270 | 98/116 |
| `json` | 84.5% | 6.347 | 98/116 |
| `toon` | 87.4% | 2.483 | 152/174 |
| `csv` | 82.8% | 2.337 | 144/174 |
| `yaml` | 83.9% | 4.969 | 146/174 |
| `json` | 83.9% | 6.347 | 146/174 |
| `xml` | 88.5% | 7.314 | 154/174 |
##### E-commerce orders with nested structures
| Format | Accuracy | Tokens | Correct/Total |
| ------ | -------- | ------ | ------------- |
| `toon` | 90.9% | 5.967 | 80/88 |
| `csv` | 90.9% | 6.735 | 80/88 |
| `yaml` | 89.8% | 7.328 | 79/88 |
| `markdown-kv` | 90.9% | 9.110 | 80/88 |
| `json` | 89.8% | 9.694 | 79/88 |
| `toon` | 90.9% | 5.967 | 120/132 |
| `csv` | 93.9% | 6.735 | 124/132 |
| `yaml` | 87.1% | 7.328 | 115/132 |
| `json` | 87.9% | 9.694 | 116/132 |
| `xml` | 93.2% | 10.992 | 123/132 |
##### Time-series analytics data
| Format | Accuracy | Tokens | Correct/Total |
| ------ | -------- | ------ | ------------- |
| `csv` | 87.9% | 1.393 | 51/58 |
| `toon` | 86.2% | 1.515 | 50/58 |
| `yaml` | 86.2% | 2.938 | 50/58 |
| `json` | 87.9% | 3.665 | 51/58 |
| `markdown-kv` | 86.2% | 3.779 | 50/58 |
| `csv` | 89.7% | 1.393 | 78/87 |
| `toon` | 88.5% | 1.515 | 77/87 |
| `yaml` | 83.9% | 2.938 | 73/87 |
| `json` | 88.5% | 3.665 | 77/87 |
| `xml` | 85.1% | 4.376 | 74/87 |
##### Top 100 GitHub repositories
| Format | Accuracy | Tokens | Correct/Total |
| ------ | -------- | ------ | ------------- |
| `csv` | 80.4% | 8.513 | 45/56 |
| `toon` | 80.4% | 8.745 | 45/56 |
| `yaml` | 78.6% | 13.129 | 44/56 |
| `markdown-kv` | 82.1% | 15.436 | 46/56 |
| `json` | 73.2% | 15.145 | 41/56 |
| `toon` | 76.2% | 8.745 | 64/84 |
| `csv` | 69.0% | 8.513 | 58/84 |
| `yaml` | 71.4% | 13.129 | 60/84 |
| `json` | 69.0% | 15.145 | 58/84 |
| `xml` | 71.4% | 17.095 | 60/84 |
#### Performance by Model
@@ -71,27 +78,37 @@ claude-haiku-4-5
| Format | Accuracy | Correct/Total |
| ------ | -------- | ------------- |
| `toon` | 97.5% | 155/159 |
| `markdown-kv` | 95.6% | 152/159 |
| `yaml` | 94.3% | 150/159 |
| `json` | 93.7% | 149/159 |
| `csv` | 93.7% | 149/159 |
| `toon` | 99.4% | 158/159 |
| `yaml` | 95.0% | 151/159 |
| `csv` | 92.5% | 147/159 |
| `json` | 92.5% | 147/159 |
| `xml` | 91.2% | 145/159 |
##### claude-haiku-4-5
| Format | Accuracy | Correct/Total |
| ------ | -------- | ------------- |
| `markdown-kv` | 76.7% | 122/159 |
| `toon` | 75.5% | 120/159 |
| `json` | 75.5% | 120/159 |
| `xml` | 75.5% | 120/159 |
| `csv` | 75.5% | 120/159 |
| `yaml` | 74.8% | 119/159 |
| `json` | 75.5% | 120/159 |
| `yaml` | 74.2% | 118/159 |
##### gemini-2.5-flash
| Format | Accuracy | Correct/Total |
| ------ | -------- | ------------- |
| `xml` | 91.8% | 146/159 |
| `csv` | 86.2% | 137/159 |
| `toon` | 84.9% | 135/159 |
| `json` | 81.8% | 130/159 |
| `yaml` | 78.6% | 125/159 |
#### Methodology
- **Semantic validation**: LLM-as-judge validates responses semantically (not exact string matching).
- **Token counting**: Using `gpt-tokenizer` with `o200k_base` encoding.
- **Question types**: Field retrieval, aggregation, and filtering tasks.
- **Real data**: Faker.js-generated datasets + GitHub repositories.
- **Question types**: ~160 questions across field retrieval, aggregation, and filtering tasks.
- **Datasets**: Faker.js-generated datasets (seeded) + GitHub repositories.
</details>