reasoning benchmark
CLUEWSC Leaderboard
CLUEWSC2020 is the Chinese version of the Winograd Schema Challenge, part of the CLUE benchmark. It focuses on pronoun disambiguation and coreference resolution, requiring models to determine which noun a pronoun refers to in a sentence. The dataset contains 1,244 training samples and 304 development samples extracted from contemporary Chinese literature.
Updated Aug 17, 2026
Models3
Model coverage3
MetricScore
EvidenceB
CLUEWSC Ranking
Higher score ranks better on this benchmark.
Sort by
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelMA | Score91.4% | Percentile100.0% | Participants3 | EvidenceC | Evaluated |
| Rank02 | ModelDE | Score90.9% | Percentile50.0% | Participants3 | EvidenceC | Evaluated |
| Rank03 | ModelBA | Score48.6% | Percentile0.0% | Participants3 | EvidenceC | Evaluated |
CLUEWSC Highlights
The leading models and scores on this benchmark.
CLUEWSC Score Distribution
A closer view of the leading scores on this benchmark.
The Top AI Models for CLUEWSC
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
Ranking basisThis cluewsc AI model leaderboard uses descending score in the benchmark's original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
- 01MAKimi-k1.5Moonshot AI
- Score
- 91.4%
Strengths
- Ranks #1 of 3 compared models
- 100th percentile on this benchmark
- C evidence result
Considerations
- This result measures CLUEWSC, not total model capability
- 02DEDeepSeek-V3DeepSeek
- Score
- 90.9%
- Speed
- Up to 100 tok/s via DeepSeek
Strengths
- Ranks #2 of 3 compared models
- 50th percentile on this benchmark
- C evidence result
Considerations
- This result measures CLUEWSC, not total model capability
- 03BAERNIE 4.5Baidu
- Score
- 48.6%
Strengths
- Ranks #3 of 3 compared models
- 0th percentile on this benchmark
- C evidence result
Considerations
- This result measures CLUEWSC, not total model capability
Selection summary
Best AI Models for CLUEWSC
Kimi-k1.5 currently leads CLUEWSC with 91.4%. It is the top model on this specific benchmark, while the best LLM for the broader task should also be checked against other benchmarks, price and runtime.
Use this leaderboard with the supporting benchmark results and coverage details above. A leaderboard position summarizes the selected ranking signal; it does not replace workload-specific testing.
What is CLUEWSC?
What CLUEWSC measures and how its scores work.
CLUEWSC2020 is the Chinese version of the Winograd Schema Challenge, part of the CLUE benchmark. It focuses on pronoun disambiguation and coreference resolution, requiring models to determine which noun a pronoun refers to in a sentence. The dataset contains 1,244 training samples and 304 development samples extracted from contemporary Chinese literature.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- CLUEWSC
- Modality
- text
- Primary category
- reasoning
- Score direction
- higher
- LLMBoard eligible
- Yes
- Evaluation key
- cluewsc|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about CLUEWSC.
Which model scores highest on CLUEWSC?
Kimi-k1.5 is currently ranked first with 91.4%.
What does CLUEWSC measure?
CLUEWSC2020 is the Chinese version of the Winograd Schema Challenge, part of the CLUE benchmark. It focuses on pronoun disambiguation and coreference resolution, requiring models to determine which noun a pronoun refers to in a sentence. The dataset contains 1,244 training samples and 304 development samples extracted from contemporary Chinese literature.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
3 model results are currently shown.
Does this benchmark affect the overall score?
Yes. This benchmark can contribute to the current LLMBoard capability score.