RACE-M
Models read passages from English exams for Chinese middle school students and answer multiple-choice questions. Scored by accuracy.
This is the middle school subset of RACE, alongside the harder high school subset RACE-H.
In the authors’ words
We present RACE, a new dataset for benchmark evaluation of methods in the reading comprehension task. Collected from the English exams for middle and high school Chinese students in the age range between 12 to 18, RACE consists of near 28,000 passages and near 100,000 questions generated by human experts (English instructors), and covers a variety of topics which are carefully designed for evaluating the students' ability in understanding and reasoning. In particular, the proportion of questions that requires reasoning is much larger in RACE than that in other benchmark datasets for reading comprehension, and there is a significant gap between the performance of the state-of-the-art models (43%) and the ceiling human performance (95%). We hope this new dataset can serve as a valuable resource for research and evaluation in machine comprehension. The dataset is freely available at http://www.cs.cmu.edu/~glai1/data/race/ and the code is available at https://github.com/qizhex/RACE_AR_baselines.
- Level
- School Students, from grade school to high school
- Scored
- Checked automatically A program checks the result against an answer key, unit tests or the expected final state.
- Answers
- Multiple choice
PaLM 2-L leads with 77% among models released by Dec 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 Dec 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 PaLM 2-L 77%
- 2
DeepSeek LLM 67B 70.9% - 3 DeepSeek-V3 67.1%
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | PaLM 2-L | 77% | May 2023 | Model card | |
| 2 | DeepSeek LLM 67B | 70.9% | Jan 2024 | Model card | |
| 3 | DeepSeek-V3 | 67.1% | Dec 2024 | Model card |
Number one over time
Lab claims count from the day the card came out, independent runs from the model’s release.
Scores over time
All the scores we have, one dot each, so a model tested at several settings appears more than once.
No launch post has listed it since Dec 2024
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 |
|---|---|---|---|
| 26 Dec 2024 | DeepSeek-V3 | 67.1 | RACE-Middle (Acc.) |
| 10 May 2023 |  PaLM 2-L | 77 | RACE-M, 1-shot |
One system card reports it
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 |
|---|---|---|---|---|
| 5 Jan 2024 | DeepSeek LLM 67B technical report | 70.9 | RACE-Middle, DeepSeek 67B Chat (Table 6) | Capabilities |
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