RULER 128K Leaderboard

What this benchmark measures

• effective long-context handling
• multi-hop tracing over long inputs
• aggregation over long inputs

Relevant to:

• long-context model selection
• context window validation

Failure modes it exercises:

• degradation well before the advertised context limit

Method and its limits

Thirteen synthetic tasks in four categories — needle-in-a-haystack retrieval, multi-hop variable tracing, aggregation and embedded question answering — generated at 4K, 8K, 16K, 32K, 64K and 128K tokens with tunable complexity.

• Tasks are synthetic by construction; the authors say directly that RULER is not a substitute for realistic tasks.
• The official page notes that many leaderboard scores are self-reported by model authors and that some entries are unresolved.

Dataset

Dataset

RULER

Type

synthetic configurable long-context tasks

Freshness

aging

Benchmark release

2024-04-09

Thirteen tasks across four categories, reported at 4K through 128K tokens, as registered on the official repository. The page states no release date; the freshness date recorded here is the primary paper's publication date.

How to read this score

The benchmark's purpose is the gap between claimed and effective context. The official page reports that although every model evaluated claims 32K tokens or more, only half handle 32K effectively against a threshold set by Llama-2-7b's 85.6% at 4K, and that almost all models fall below that threshold before reaching their claimed length.

Similarity to real tasks: Low — Established from the official source: RULER generates synthetic examples with configurable sequence length and complexity across retrieval, multi-hop variable tracing, aggregation and embedded question answering. The authors state the benchmark is by no means comprehensive and not a substitute for realistic tasks, so it is explicitly a diagnostic rather than a workload proxy.

Data contamination risk: Low — Examples are generated rather than fixed, so a specific test set cannot be memorised. No run-specific audit is registered.

Benchmark gaming risk: Medium — Generation is configurable, so results depend on the complexity settings chosen, and the official page records that many published scores are self-reported.

What you still need to test yourself

• Test your own long-context task; the authors state this benchmark does not substitute for realistic work.

This benchmark supports decisions about:

• Check effective rather than advertised context length.
• Identify the length at which a candidate model starts degrading.

Limitations

• Synthetic diagnostic, explicitly not a substitute for realistic long-context tasks.
• Published scores mix self-reported and measured figures.

Official repository reviewed 2026-10-12. The source states no release date; the freshness date recorded here is the publication date of the primary paper attached 2026-10-25.

What this evidence does not cover

• The official leaderboard mixes results the maintainers measured themselves with results reported by model authors, marking the latter with an asterisk.
• Both registered scores are the 128K column only, and one of them falls below the benchmark's own effective-length threshold at that size.

Sources

• RULER — official repository NVIDIA · accessed 2026-10-12Supports the thirteen tasks across four categories, the 4K to 128K evaluation lengths, the Llama-2-7b 85.6% threshold, the finding that only half of models handle 32K effectively, and the authors' statement that the benchmark is not comprehensive; it notes many scores are self-reported, does not support any model score.
• RULER: What's the Real Context Size of Your Long-Context Language Models? Hsieh et al. · accessed 2026-10-25The primary methodology paper, establishing the synthetic task families behind the benchmark and its release date. It does not establish any model's score.

How this benchmark is scored

Primary source: https://github.com/hsiehjackson/RULER

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