reasoning benchmark
LongFact is a benchmark for evaluating long-form factuality in large language models. It comprises 2,280 fact-seeking prompts spanning 38 topics, designed to test a model's ability to generate accurate, long-form responses. The benchmark uses SAFE (Search-Augmented Factuality Evaluator) to evaluate factual accuracy.
Updated Aug 7, 2026
Sorted by the source-provided rank. Higher score is better according to the registry.
| 1 | OP | 0.8% | 100.0% | 1 | C |
The top published results on this benchmark's own scale.
Definition and scoring fields from the benchmark registry.
LongFact is a benchmark for evaluating long-form factuality in large language models. It comprises 2,280 fact-seeking prompts spanning 38 topics, designed to test a model's ability to generate accurate, long-form responses. The benchmark uses SAFE (Search-Augmented Factuality Evaluator) to evaluate factual accuracy.
Scores are shown in ratio. The current registry marks this benchmark as not independently verified with evidence level B.
Source-native results are preserved. Eligibility for the overall LLMBoard score is a separate policy decision.
Common questions about LongFact Objects.
GPT-5 is currently ranked first with 0.8%.
LongFact is a benchmark for evaluating long-form factuality in large language models. It comprises 2,280 fact-seeking prompts spanning 38 topics, designed to test a model's ability to generate accurate, long-form responses. The benchmark uses SAFE (Search-Augmented Factuality Evaluator) to evaluate factual accuracy.
Yes. Higher values rank better for this benchmark.
1 unique published model results are currently shown.
This benchmark is preserved as source-native evidence but is not eligible for the current overall score.