ZeroSCROLLS/QuALITY
Llama 3.1-405B 95.2% leads since Jul 2024 Dropped Models answer QuALITY multiple-choice questions about long stories zero-shot, as one task of the ZeroSCROLLS suite. Scoring is accuracy.
The ZeroSCROLLS leaderboard at zero.scrolls-benchmark.com did not answer on 2026-11-01.
In the authors’ words
We introduce ZeroSCROLLS, a zero-shot benchmark for natural language understanding over long texts, which contains only test and small validation sets, without training data. We adapt six tasks from the SCROLLS benchmark, and add four new datasets, including two novel information fusing tasks, such as aggregating the percentage of positive reviews. Using ZeroSCROLLS, we conduct a comprehensive evaluation of both open-source and closed large language models, finding that Claude outperforms ChatGPT, and that GPT-4 achieves the highest average score. However, there is still room for improvement on multiple open challenges in ZeroSCROLLS, such as aggregation tasks, where models struggle to pass the naive baseline. As the state of the art is a moving target, we invite researchers to evaluate their ideas on the live ZeroSCROLLS leaderboard.
- Level
- Everyday Most adults, without training
- Scored
- Checked automatically A program checks the result against an answer key, unit tests or the expected final state.
- Answers
- Multiple choice
Llama 3.1-405B leads with 95.2% among models released by Jul 2024
Best score per model as labs report them in their own cards. Labs pick their own settings, so these compare less cleanly than independent runs.
The newest model with a score here came out in Jul 2024, and nobody has reported a newer one. Among the 20 highest-ranked models on the capability index, these have no score yet:
- GPT-6 Astra
- Claude Opus 5.5
- GPT-6.1 Terra
- GPT-6.1 Sol
- and 16 more
- 1 Llama 3.1-405B 95.2%
- 2 Llama 3.1-70B 90.5%
- 3 Llama 3.1-8B 81%
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | Llama 3.1-405B | 95.2% | Jul 2024 | Model card | |
| 2 | Llama 3.1-70B | 90.5% | Nov 2024 | Model card | |
| 3 | Llama 3.1-8B | 81% | Nov 2024 | Model card |
Number one over time
Lab claims count from the day the card came out, independent runs from the model’s release.
The best score passed 85% in Jul 2024
All the scores we have, one dot each, so a model tested at several settings appears more than once.
Labs dropped it from launch posts once the best score passed 85%
Launch posts from every lab that has listed it, one tick per post. Tall colored ticks include it, and short gray ones leave it out.
| Date | Launch post | Score | As the post writes it |
|---|---|---|---|
| 23 Jul 2024 | Llama 3.1-405B | 95.2 | ZeroSCROLLS/QuALITY |
3 system cards report it, from 1 lab
Besides launch posts, we read the system cards, model cards and technical reports labs publish with a release. They report far more, including safety and frontier-risk tests.
| Date | Document | Score | As the document writes it | Section |
|---|---|---|---|---|
| 23 Nov 2024 | Llama 3.1-8B technical report | 81 | ZeroSCROLLS/QuALITY, exact match, validation set, post-trained (Table 2, Table 21) | Capabilities |
| 23 Nov 2024 | Llama 3.1-70B technical report | 90.5 | ZeroSCROLLS/QuALITY, exact match, validation set, post-trained (Table 2, Table 21) | Capabilities |
| 23 Nov 2024 | Llama 3.1-405B technical report | 95.2 | ZeroSCROLLS/QuALITY, exact match, validation set, post-trained (Table 2, Table 21) | Capabilities |
More in Long context
- MRCR v2 In use Multi-round coreference: retrieve the n-th of several similar requests hidden in a long synthetic conversation, up to 1M tokens. Muse Spark 1.3 98.5%
- LongBench v2 In use 503 multiple-choice questions over 8K-2M-word contexts (QA, dialogue, code repos, structured data). Accuracy. Beam 1.0 66.9%
- GraphWalks In use A graph is given as an edge list in long context. The model answers BFS or parent queries, scored by F1. Claude Fable 5 100%
- OpenAI MRCR v2 In use Models read a long synthetic chat that hides 2, 4 or 8 identical writing requests and must reproduce the response to the i-th one. Scored by mean string match ratio per context bin.
K-EXAONE 2.0 94.4% - CL-bench In use Model must learn new knowledge from a supplied long context (rules, procedures, data) and solve tasks. Strict LLM-verified rubrics. GPT-5.4 27.9%
- AA-LCR 1.1 In use Models answer 100 questions that need reasoning across sets of real documents of about 100K tokens. An LLM equality checker with a fixed system prompt compares each answer with the key.
Step 5 Preview 88.3%