We benchmarked 18 LLMs on OCR (7k+ calls) — cheaper/old models oftentimes win. Full dataset + framework open-sourced. [R]
BenchmarkFreeWe benchmarked 18 LLMs on OCR (7k+ calls) — cheaper/old models oftentimes win. Full dataset + framework open-sourced. [R]
- Best for
- benchmarking llms for ocr performance
- Type
- Benchmark · Free
- Score
- 36/100
- Best alternative
- Hugging Face MCP Server
Capabilities1 decomposed
benchmarking llms for ocr performance
Medium confidenceThis capability benchmarks 18 different LLMs on Optical Character Recognition (OCR) tasks using a comprehensive dataset of over 7,000 calls. It employs a systematic evaluation framework that allows for the comparison of model performance across various metrics, such as accuracy and processing speed. The open-sourced framework enables users to replicate the benchmarks and adapt the methodology for their specific needs, promoting transparency and reproducibility in research.
Utilizes a large-scale dataset and a systematic evaluation framework that is fully open-sourced, allowing for community-driven improvements and transparency in results.
More comprehensive than existing benchmarks due to the inclusion of 18 models and a large dataset, enabling a more robust comparison.
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Best For
- ✓researchers evaluating LLM performance for OCR
- ✓developers selecting OCR models for applications
- ✓data scientists conducting comparative analysis
Known Limitations
- ⚠Limited to the 18 LLMs included in the benchmark; results may not generalize to other models.
- ⚠Performance may vary based on specific OCR tasks not covered in the dataset.
Requirements
Input / Output
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We benchmarked 18 LLMs on OCR (7k+ calls) — cheaper/old models oftentimes win. Full dataset + framework open-sourced. [R]
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