reasoning benchmark
LongFact Objects evaluates the factuality of long-form responses from large language models.
Updated Sep 5, 2026
Higher score ranks better on this benchmark.
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelOP | Score0.80% | 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 objects 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 Objects with 0.80%. 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 Objects measures and how its scores work.
LongFact Objects is a benchmark comprising 2,280 fact-seeking prompts spanning 38 topics.
It measures factual accuracy in long-form responses using SAFE (Search-Augmented Factuality Evaluator), reported as a Score ratio.
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 Objects.
GPT-5 is currently ranked first with 0.80%.
The current leaders are GPT-5 (0.80%).
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 factual accuracy in long-form responses using SAFE (Search-Augmented Factuality Evaluator), reported as a Score ratio.
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.