ParseBench Mean

ParseBench Mean is a benchmark for reading and parsing real documents. The official description says: "ParseBench Mean - document parsing benchmark mean score across text content and formatting". Below we describe in simple words what benchmarks of this type generally examine.

ParseBench Mean measures reading and parsing real documents. In detail, it checks:

• extracting structured text from scans, PDFs and photos
• keeping tables, headings and reading order intact
• answering comprehension questions about the document content
• handling noisy layouts, handwriting and multi-page documents

The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better.

Official methodology note: ParseBench Mean - document parsing benchmark mean score across text content and formatting. Pipeline: qwen3_6_35b_a3b_fp8_vllm_parse_layout.

What is the ParseBench Mean benchmark?

ParseBench Mean is a benchmark for reading and parsing real documents. The official description says: 'ParseBench Mean - document parsing benchmark mean score across text content and formatting'. Below we describe in simple words what benchmarks of this type generally examine.

What does ParseBench Mean test?

ParseBench Mean mainly measures reading and parsing real documents. Concretely, it checks: extracting structured text from scans, PDFs and photos; keeping tables, headings and reading order intact; answering comprehension questions about the document content.

How is the ParseBench Mean score calculated?

The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better. The declared maximum score of this benchmark is 100.0.

Where do I get ParseBench Mean data and scores?

The official source is https://huggingface.co/datasets/llamaindex/ParseBench. The official benchmark page/reference is linked on this page. Every score on Open LLM Radar is also available as open data: download the scores JSON (http://open-llm-radar-api:8000/api/v1/benchmarks/parsebench-mean/scores) or pull it directly from the Open LLM Radar API with any HTTP client.