reasoning benchmark
FRAMES Leaderboard
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 17, 2026
Models2
Model coverage2
MetricScore
EvidenceB
FRAMES Ranking
Higher score ranks better on this benchmark.
Sort by
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelMA | Score87.0% | Percentile100.0% | Participants2 | EvidenceC | Evaluated |
| Rank02 | ModelDE | Score73.3% | Percentile0.0% | Participants2 | EvidenceC | Evaluated |
FRAMES Highlights
The leading models and scores on this benchmark.
FRAMES Score Distribution
A closer view of the leading scores on this benchmark.
The Top AI Models for FRAMES
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
Ranking basisThis frames AI model leaderboard uses descending score in the benchmark's original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
- 01MAKimi K2-Thinking-0905Moonshot AI
- Score
- 87.0%
- Price
- $0.60 input / $2.5 output per 1M tokens
Strengths
- Ranks #1 of 2 compared models
- 100th percentile on this benchmark
- C evidence result
Considerations
- This result measures FRAMES, not total model capability
- 02DEDeepSeek-V3DeepSeek
- Score
- 73.3%
- Speed
- Up to 100 tok/s via DeepSeek
Strengths
- Ranks #2 of 2 compared models
- 0th percentile on this benchmark
- C evidence result
Considerations
- This result measures FRAMES, not total model capability
Selection summary
Best AI Models for FRAMES
Kimi K2-Thinking-0905 currently leads FRAMES with 87.0%. 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 is FRAMES?
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.
- Family
- FRAMES
- Modality
- text
- Primary category
- reasoning
- Score direction
- higher
- LLMBoard eligible
- No
- Evaluation key
- frames|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about FRAMES.
Which model scores highest on FRAMES?
Kimi K2-Thinking-0905 is currently ranked first with 87.0%.
What does FRAMES measure?
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
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
2 model results are currently shown.
Does this benchmark affect the overall score?
No. This benchmark is shown for reference but does not contribute to the overall score.