GDP.pdf
GDP.pdf: official Artificial Analysis (artificialanalysis.ai) text-only version as cited on the KRAFTON Raon-1 mini preview card; PDFs are OCR-ed in advance, no image input. Aggregator-sourced score (aggregator_source=true), judge gpt-5.6-luna.
Category
Long Context
Score type
percent
Scale
100%
higher is better
Models tested
5
What is GDP.pdf?
GDP.pdf is a benchmark for reading and reasoning over very long inputs. The official description says: "GDP.pdf: official Artificial Analysis (artificialanalysis.ai) text-only version as cited on the KRAFTON Raon-1 mini preview card; PDFs are OCR-ed in advance, no image input". Below we describe in simple words what benchmarks of this type generally examine.
What does it test?
GDP.pdf measures reading and reasoning over very long inputs. In detail, it checks:
- finding relevant facts buried in documents hundreds of thousands of tokens long
- retrieving multiple needles hidden across the full context
- reasoning over whole books, codebases or conversation histories
- staying accurate near the context window limit (no 'lost in the middle' falloff)
How to read the score
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: GDP.pdf: official Artificial Analysis (artificialanalysis.ai) text-only version as cited on the KRAFTON Raon-1 mini preview card; PDFs are OCR-ed in advance, no image input. Aggregator-sourced score (aggregator_source=true), judge gpt-5.6-luna.
Frequently asked questions
What is the GDP.pdf benchmark?
GDP.pdf is a benchmark for reading and reasoning over very long inputs. The official description says: 'GDP.pdf: official Artificial Analysis (artificialanalysis.ai) text-only version as cited on the KRAFTON Raon-1 mini preview card; PDFs are OCR-ed in advance, no image input'. Below we describe in simple words what benchmarks of this type generally examine.
What does GDP.pdf test?
GDP.pdf mainly measures reading and reasoning over very long inputs. Concretely, it checks: finding relevant facts buried in documents hundreds of thousands of tokens long; retrieving multiple needles hidden across the full context; reasoning over whole books, codebases or conversation histories.
How is the GDP.pdf 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 data and scores?
The official source is https://huggingface.co/KRAFTON/Raon-1-mini-preview. 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/scores) or pull it directly from the Open LLM Radar API with any HTTP client.
Benchmark data & downloads
Where to get, inspect and reuse the data behind this benchmark.
Official benchmark page / paper
The benchmark maintainer's website, paper or leaderboard
Benchmark metadata (JSON)
This page's benchmark record from the Open LLM Radar API
All model scores (JSON download)
Full score table as JSON; paginate with limit/offset params
Open LLM Radar API docs
Swagger UI to explore and reuse all radar API endpoints
Model scores 5 models tested
| # | Model | Score | Date | Source | Verified |
|---|---|---|---|---|---|
| 1 |
Qwen3.8-27B
Qwen Team (Alibaba Cloud)
|
11.2%
|
21.10.2026 | Report | Unverified |
| 2 |
Muse-Glimmer-30B
Meta Superintelligence Lab
|
8.4%
|
21.10.2026 | Report | Unverified |
| 3 |
Qwen3.6-35B-A3B
Qwen Team (Alibaba Cloud)
|
6.6%
|
21.10.2026 | Report | Unverified |
| 4 |
Raon-1 mini
KRAFTON AI
|
5.8%
|
21.10.2026 | Report | Verified |
| 5 |
Gemma4-26B-A4B
Google
|
3.6%
|
21.10.2026 | Report | Unverified |