RULER 512K

RULER 512K evaluates the official 13-task RULER v1 suite at a 524288-token context budget.

MiniCPM-SALA from OpenBMB currently leads the RULER 512K leaderboard with a score of 0.871 across 1 evaluated AI models.

PaperImplementation
About this benchmark

What RULER 512K measures

RULER 512K is a text benchmark that evaluates large language models on long context and reasoning tasks. LLM Stats tracks 1 model on this benchmark, with a maximum possible score of 1. Current average across reported models is 0.9, with the leader reaching 0.9.

Compare leaders on the best AI for long context and best AI for reasoning leaderboards.

Publication

Paper
RULER: What's the Real Context Size of Your Long-Context Language Models?
Authors
Cheng-Ping Hsieh, Simeng Sun, Samuel Kriman, Shantanu Acharya, and 4 others
Published

Abstract

The needle-in-a-haystack (NIAH) test, which examines the ability to retrieve a piece of information (the "needle") from long distractor texts (the "haystack"), has been widely adopted to evaluate long-context language models (LMs). However, this simple retrieval-based test is indicative of only a superficial form of long-context understanding. To provide a more comprehensive evaluation of long-context LMs, we create a new synthetic benchmark RULER with flexible configurations for customized sequence length and task complexity. RULER expands upon the vanilla NIAH test to encompass variations with diverse types and quantities of needles. Moreover, RULER introduces new task categories multi-hop tracing and aggregation to test behaviors beyond searching from context. We evaluate 17 long-context LMs with 13 representative tasks in RULER. Despite achieving nearly perfect accuracy in the vanilla NIAH test, almost all models exhibit large performance drops as the context length increases. While these models all claim context sizes of 32K tokens or greater, only half of them can maintain satisfactory performance at the length of 32K. Our analysis of Yi-34B, which supports context length of 200K, reveals large room for improvement as we increase input length and task complexity. We open source RULER to spur comprehensive evaluation of long-context LMs.

OpenBMBMiniCPM-SALA leads with 87.1%.

Progress Over Time

Interactive timeline showing model performance evolution on RULER 512K

State-of-the-art frontier
Open
Proprietary

RULER 512K Leaderboard

1 models
ContextCostLicense
19B
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FAQ

Common questions about RULER 512K.

What is the RULER 512K benchmark?

RULER 512K evaluates the official 13-task RULER v1 suite at a 524288-token context budget.

What is the RULER 512K leaderboard?

The RULER 512K leaderboard ranks 1 AI models based on their performance on this benchmark. Currently, MiniCPM-SALA by OpenBMB leads with a score of 0.871. The average score across all models is 0.871.

What is the highest RULER 512K score?

The highest RULER 512K score is 0.871, achieved by MiniCPM-SALA from OpenBMB.

How many models are evaluated on RULER 512K?

1 models have been evaluated on the RULER 512K benchmark, with 0 verified results and 1 self-reported results.

Where can I find the RULER 512K paper?

The RULER 512K paper is available at https://arxiv.org/abs/2404.06654. The paper details the methodology, dataset construction, and evaluation criteria.

Where can I find the RULER 512K dataset?

The RULER 512K dataset is available at https://github.com/NVIDIA/RULER.

What categories does RULER 512K cover?

RULER 512K is categorized under long context and reasoning. The benchmark evaluates text models.

What's the difference between RULER 512K and RULER?

RULER 512K is a variant of RULER. See the RULER leaderboard for the broader benchmark and per-model comparison.

What is the best open-source model on RULER 512K?

MiniCPM-SALA by OpenBMB is the top-ranked open-source model on RULER 512K, with a score of 0.871 (rank #1).

How recent are the RULER 512K leaderboard results?

The RULER 512K leaderboard was last updated in June 2026 and currently includes 1 evaluated models.

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