reasoning benchmark
LongFact Concepts evaluates long-form factuality in large language models using 2,280 fact-seeking prompts across 38 topics.
Updated Sep 5, 2026
Higher score ranks better on this benchmark.
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelOP | Score0.70% | Percentile100.00% | Participants1 | EvidenceC | Evaluated |
The leading models and scores on this benchmark.
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
Ranking basisThis longfact concepts AI model leaderboard uses descending score in the benchmark original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
Selection summary
GPT-5 currently leads LongFact Concepts with 0.70%. It is the top model on this specific benchmark, while the best LLM for the broader task should also be checked against other benchmarks, price, and runtime.
Use this leaderboard with the supporting benchmark results and coverage details above. A leaderboard position summarizes the selected ranking signal; it does not replace workload-specific testing.
What LongFact Concepts measures and how its scores work.
LongFact Concepts is a benchmark for evaluating long-form factuality in large language models.
It measures the factual accuracy of long-form responses using SAFE (Search-Augmented Factuality Evaluator).
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
LLM Stats. Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about LongFact Concepts.
GPT-5 is currently ranked first with 0.70%.
The current leaders are GPT-5 (0.70%).
GPT-5 has the lowest matched official input price at $1.3 input / $10 output per 1M tokens.
The fastest matched records are GPT-5 (100.00 tok/s via OpenAI).
No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.
It measures the factual accuracy of long-form responses using SAFE (Search-Augmented Factuality Evaluator).
Yes. Higher values rank better for this benchmark.
1 model results are currently shown.
No. This benchmark is shown for reference but does not contribute to the overall score.