JSQuAD
The Frontier
Best score over time · one chart, every benchmark
Distribution
Where models cluster
Correlated benchmarks
Pearson r · original research
Benchmarks that track with JSQuAD
Pearson correlation across models scored on both benchmarks. Closer to 1 = strongly predictive.
Full rankings
11 models tested · sorted by score
| # | Model | Score |
|---|---|---|
| 1 |  Qwen2 VL 7B Instruct | 89.9 |
| 2 |  DeepSeek R1 Distill Qwen 14B | 89.8 |
| 3 | Qwen2 7B Instruct | 89.6 |
| 4 |  Meta Llama 3 8B Instruct | 89.5 |
| 5 | Meta Llama 3 8B | 88.9 |
| 6 |  Gemma 2 2b It | 83.8 |
| 7 | Llama 2 7b Chat Hf | 83.0 |
| 8 | DeepSeek R1 Distill Llama 8B | 80.2 |
| 9 | Llama 2 7b Hf | 79.9 |
| 10 | DeepSeek R1 Distill Qwen 7B | 74.2 |
| 11 | HF SmolLM2 135M Instruct | 13.9 |
Frequently asked
Pulled from the JSQuAD dataset · updated daily
What does JSQuAD measure?
JSQuAD is a knowledge benchmark in the BenchGecko catalog. 11 AI models have been tested on it. Scores range from 13.9 to 89.9 out of 100.
Which model leads on JSQuAD?
Qwen2 VL 7B Instruct from Alibaba leads JSQuAD with a score of 89.9. The median score across 11 tested models is 83.8.
Is JSQuAD saturated?
No · the top score is 89.9 out of 100 (90%). There is still meaningful room for improvement on JSQuAD.
Does JSQuAD predict performance on other benchmarks?
Yes · JSQuAD scores correlate 0.90 with LLM-JP · Overall across 11 shared models. Models that do well on JSQuAD tend to do well on LLM-JP · Overall.
How often is JSQuAD data refreshed?
BenchGecko pulls updates daily. New model scores on JSQuAD appear as soon as they are published by Epoch AI or the model provider.
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