#1
retab-large
Retab
Accuracy
97.2%
Leaderboard
Our frontier models extraction benchmark
Structured extraction scores on proprietary, human-annotated documents across invoices, contracts, financial reports, and technical documentation.
- Top score
- 97.2%
- Systems
- 7
- Dataset
- Private
Extract API
Leaderboard
Models are evaluated with identical extraction tasks and compared against human-verified annotations.
#2
Extend
Extend
Accuracy
91.4%
#3
Landing
Landing
Accuracy
89.2%
#4
LlamaParse
LlamaIndex
Accuracy
87.8%
#5
retab-small
Retab
Accuracy
79.3%
#6
Reducto
Reducto
Accuracy
63.5%
#7
retab-micro
Retab
Accuracy
58.2%
| Rank | Model | Platform | Accuracy |
|---|---|---|---|
| #1 | retab-large | Retab | 97.2% |
| #2 | Extend | Extend | 91.4% |
| #3 | Landing | Landing | 89.2% |
| #4 | LlamaParse | LlamaIndex | 87.8% |
| #5 | retab-small | Retab | 79.3% |
| #6 | Reducto | Reducto | 63.5% |
| #7 | retab-micro | Retab | 58.2% |
Methodology
Real documents, human ground truth.
Our benchmark is built on a curated dataset of proprietary documents provided by partner customers. These documents span a wide range of industries and use cases, including invoices, contracts, financial reports, and technical documentation.
Each document has been manually annotated by domain experts to establish ground truth for structured data extraction. We evaluate models by running them with the same prompts through identical extraction tasks and comparing their outputs against these human-verified annotations.
Private benchmark
The dataset is not publicly available to prevent model overfitting and ensure fair evaluation of real-world performance.
Real-world data
Documents reflect actual production scenarios, providing meaningful accuracy metrics for enterprise extraction tasks.
Run evaluations on frontier AI capabilities
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