long context benchmark
RULER v1 is a synthetic long-context benchmark with 13 official tasks spanning retrieval, multi-hop tracing, aggregation, and QA.
Updated Sep 5, 2026
Higher score ranks better on this benchmark.
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelNV | Score94.70% | Percentile100.00% | Participants4 | EvidenceC | Evaluated |
| Rank02 | ModelNV | Score91.75% | Percentile66.67% | Participants4 | EvidenceC | Evaluated |
| Rank03 | ModelMI | Score87.10% | Percentile33.33% | Participants4 | EvidenceC | Evaluated |
| Rank04 | ModelMI | Score84.10% | Percentile0.00% | Participants4 | EvidenceC | Evaluated |
The leading models and scores on this benchmark.
A closer view of the leading 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.
What RULER measures and how its scores work.
RULER v1 is a synthetic long-context benchmark based on the public standalone NVIDIA RULER implementation.
It measures how model quality degrades as input length increases using the Score metric, reported as a ratio, across retrieval, multi-hop tracing, aggregation, and QA tasks.
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 RULER.
Nemotron 3 Ultra (550B A55B) is currently ranked first with 94.70%.
The current leaders are Nemotron 3 Ultra (550B A55B) (94.70%), Nemotron 3 Super (120B A12B) (91.75%), and Phi-3.5-MoE-instruct (87.10%).
Nemotron 3 Super (120B A12B) has the lowest matched official input price at $0.20 input / $0.80 output per 1M tokens.
The fastest matched records are Phi-3.5-mini-instruct (23.00 tok/s via Azure).
No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.
It measures how model quality degrades as input length increases using the Score metric, reported as a ratio, across retrieval, multi-hop tracing, aggregation, and QA tasks.
Yes. Higher values rank better for this benchmark.
4 model results are currently shown.
No. This benchmark is shown for reference but does not contribute to the overall score.
Ranking basisThis ruler 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
Nemotron 3 Ultra (550B A55B) currently leads RULER with 94.70%. 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.