LongBenchPro

LongBenchPro is a benchmark for reading and reasoning over very long inputs. The official description says: "LongBenchPro - long context understanding benchmark with professional-level tasks". Below we describe in simple words what benchmarks of this type generally examine.

LongBenchPro 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)

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: LongBenchPro - long context understanding benchmark with professional-level tasks.

What is the LongBenchPro benchmark?

LongBenchPro is a benchmark for reading and reasoning over very long inputs. The official description says: 'LongBenchPro - long context understanding benchmark with professional-level tasks'. Below we describe in simple words what benchmarks of this type generally examine.

What does LongBenchPro test?

LongBenchPro 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 LongBenchPro 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 LongBenchPro data and scores?

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/longbenchpro/scores) or pull it directly from the Open LLM Radar API with any HTTP client.