Beam128K

Beam128K is a benchmark for reading and reasoning over very long inputs. The official description says: "Beam128K - long context retrieval and understanding benchmark at 128K tokens". Below we describe in simple words what benchmarks of this type generally examine.

Beam128K 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: Beam128K - long context retrieval and understanding benchmark at 128K tokens.

What is the Beam128K benchmark?

Beam128K is a benchmark for reading and reasoning over very long inputs. The official description says: 'Beam128K - long context retrieval and understanding benchmark at 128K tokens'. Below we describe in simple words what benchmarks of this type generally examine.

What does Beam128K test?

Beam128K 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 Beam128K 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 Beam128K 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/beam128k/scores) or pull it directly from the Open LLM Radar API with any HTTP client.