RACE
InternVL3.5-241B-A28B 94.2% leads since Aug 2025 Only in system cards Models answer reading comprehension questions from Chinese middle and high school English exams. Reported here without a subset split.
Use race-m or race-h when a report gives the middle or high school subset.
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
InternVL3.5-241B-A28B leads with 94.2% among models released by Aug 2025
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 Aug 2025, 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 InternVL3.5-241B-A28B 94.2%
- 2
Llama 3.1-70B 59% - 3 Llama 3.1-405B 58.1%
- 4 Llama 3.1-8B 54.3%
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | InternVL3.5-241B-A28B | 94.2% | Aug 2025 | Model card | |
| 2 | Llama 3.1-70B | 59% | Nov 2024 | Model card | |
| 3 | Llama 3.1-405B | 58.1% | Nov 2024 | Model card | |
| 4 | Llama 3.1-8B | 54.3% | Nov 2024 | Model card |
2 models have held number one since 2023
Lab claims count from the day the card came out, independent runs from the model’s release.
The best score passed 85% in Aug 2025
All the scores we have, one dot each, so a model tested at several settings appears more than once.
Labs report it only in system cards
We read the headline results of 479 launch posts from 51 labs, and none of them list this benchmark. Some system cards do, below.
4 system cards report it, from 2 labs
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 |
|---|---|---|---|---|
| 25 Aug 2025 | InternVL3.5-241B-A28B technical report | 94.2 | RACE-High, OpenCompass (Table 14) | Capabilities |
| 23 Nov 2024 | Llama 3.1-8B technical report | 54.3 | RACE, pretrained (Table 9) | Capabilities |
| 23 Nov 2024 | Llama 3.1-70B technical report | 59 | RACE, pretrained (Table 9) | Capabilities |
| 23 Nov 2024 | Llama 3.1-405B technical report | 58.1 | RACE, pretrained (Table 9) | Capabilities |
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