GDP.pdf w/ python

GDP.pdf w/ python is a benchmark for reading and parsing real documents. The official description says: "Each original PDF sent to a sandbox for IPython processing; rubric graded with Gemini-3.5-Flash (medium effort); task-macro mean rubric pass rate; averaged over 5 runs". Below we describe in simple words what benchmarks of this type generally examine.

GDP.pdf w/ python 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: Each original PDF sent to a sandbox for IPython processing; rubric graded with Gemini-3.5-Flash (medium effort); task-macro mean rubric pass rate; averaged over 5 runs.

What is the GDP.pdf w/ python benchmark?

GDP.pdf w/ python is a benchmark for reading and parsing real documents. The official description says: 'Each original PDF sent to a sandbox for IPython processing; rubric graded with Gemini-3.5-Flash (medium effort); task-macro mean rubric pass rate; averaged over 5 runs'. Below we describe in simple words what benchmarks of this type generally examine.

What does GDP.pdf w/ python test?

GDP.pdf w/ python 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 GDP.pdf w/ python 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 GDP.pdf w/ python data and scores?

The official source is https://huggingface.co/moonshotai/Kimi-K3.1-Pro. 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/gdp-pdf-with-python/scores) or pull it directly from the Open LLM Radar API with any HTTP client.