reasoning benchmark
Factuality, Retrieval, And reasoning MEasurement Set - a unified evaluation dataset of 824 challenging multi-hop questions for testing retrieval-augmented generation systems across factuality, retrieval accuracy, and reasoning capabilities, requiring integration of 2-15 Wikipedia articles per question
Updated Aug 11, 2026
Higher score ranks better on this benchmark.
| 01 | MA | 87.0% | 100.0% | 2 | C | |
| 02 | DE | 73.3% | 0.0% | 2 | C |
A closer view of the leading scores on this benchmark.
The leading models and scores on this benchmark.
What FRAMES measures and how its scores work.
Factuality, Retrieval, And reasoning MEasurement Set - a unified evaluation dataset of 824 challenging multi-hop questions for testing retrieval-augmented generation systems across factuality, retrieval accuracy, and reasoning capabilities, requiring integration of 2-15 Wikipedia articles per question
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about FRAMES.
Kimi K2-Thinking-0905 is currently ranked first with 87.0%.
Factuality, Retrieval, And reasoning MEasurement Set - a unified evaluation dataset of 824 challenging multi-hop questions for testing retrieval-augmented generation systems across factuality, retrieval accuracy, and reasoning capabilities, requiring integration of 2-15 Wikipedia articles per question
Yes. Higher values rank better for this benchmark.
2 model results are currently shown.
No. This benchmark is shown for reference but does not contribute to the overall score.