DROP
DROP - discrete reasoning over paragraphs. 1-shot.
Category
Reasoning
Score type
percent
Scale
100%
higher is better
Models tested
4
What is DROP?
DROP is a benchmark for general reasoning and comprehension. The official description says: "DROP - discrete reasoning over paragraphs". Below we describe in simple words what benchmarks of this type generally examine.
What does it test?
DROP measures general reasoning and comprehension. In detail, it checks:
- drawing the right conclusion from a passage or scenario
- commonsense judgement about everyday situations and words
- counting, comparing and reasoning over several facts at once
- avoiding shortcuts that memorise answers instead of reasoning
How to read the score
The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better.
Official methodology note: DROP - discrete reasoning over paragraphs. 1-shot.
Frequently asked questions
What is the DROP benchmark?
DROP is a benchmark for general reasoning and comprehension. The official description says: 'DROP - discrete reasoning over paragraphs'. Below we describe in simple words what benchmarks of this type generally examine.
What does DROP test?
DROP mainly measures general reasoning and comprehension. Concretely, it checks: drawing the right conclusion from a passage or scenario; commonsense judgement about everyday situations and words; counting, comparing and reasoning over several facts at once.
How is the DROP score calculated?
The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better. The declared maximum score of this benchmark is 100.0.
Where do I get DROP data and scores?
The official source is https://arxiv.org/abs/1903.00161. The official benchmark page/reference is linked on this page. Every score on Open LLM Radar is also available as open data: download the scores JSON (http://open-llm-radar-api:8000/api/v1/benchmarks/drop/scores) or pull it directly from the Open LLM Radar API with any HTTP client.
Benchmark data & downloads
Where to get, inspect and reuse the data behind this benchmark.
Official benchmark page / paper
The benchmark maintainer's website, paper or leaderboard
Benchmark metadata (JSON)
This page's benchmark record from the Open LLM Radar API
All model scores (JSON download)
Full score table as JSON; paginate with limit/offset params
Open LLM Radar API docs
Swagger UI to explore and reuse all radar API endpoints
Model scores 4 models tested
| # | Model | Score | Date | Source | Verified |
|---|---|---|---|---|---|
| 1 |
Gemma 3 27B PT
Google DeepMind
|
77.2%
|
27.02.2025 | Report | Verified |
| 2 |
Gemma 3 12B PT
Google DeepMind
|
72.2%
|
27.02.2025 | Report | Verified |
| 3 |
Gemma 3 4B PT
Google DeepMind
|
60.1%
|
27.02.2025 | Report | Verified |
| 4 |
Gemma 3 1B PT
Google DeepMind
|
42.4%
|
27.02.2025 | Report | Verified |