language benchmark
CLUEWSC is the Chinese version of the Winograd Schema Challenge and part of the CLUE benchmark, focusing on pronoun disambiguation and coreference resolution.
Updated Sep 5, 2026
Higher score ranks better on this benchmark.
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelMA | Score91.40% | Percentile100.00% | Participants3 | EvidenceC | Evaluated |
| Rank02 | ModelDE | Score90.90% | Percentile50.00% | Participants3 | EvidenceC | Evaluated |
| Rank03 | ModelBA | Score48.60% | Percentile0.00% | Participants3 | EvidenceC | Evaluated |
The leading models and scores on this benchmark.
A closer view of the leading scores on this benchmark.
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
What CLUEWSC measures and how its scores work.
CLUEWSC is a language benchmark with 1,244 training samples and 304 development samples extracted from contemporary Chinese literature.
It measures models' ability to determine which noun a pronoun refers to in a sentence, using the Score metric reported as a ratio.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
LLM Stats. Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about CLUEWSC.
Kimi-k1.5 is currently ranked first with 91.40%.
The current leaders are Kimi-k1.5 (91.40%), DeepSeek-V3 (90.90%), and ERNIE 4.5 (48.60%).
No matched official input price is currently available.
The fastest matched records are DeepSeek-V3 (100.00 tok/s via DeepSeek).
No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.
It measures models' ability to determine which noun a pronoun refers to in a sentence, using the Score metric reported as a ratio.
Yes. Higher values rank better for this benchmark.
3 model results are currently shown.
No. This benchmark is shown for reference but does not contribute to the overall score.
Ranking basisThis cluewsc AI model leaderboard uses descending score in the benchmark original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
Selection summary
Kimi-k1.5 currently leads CLUEWSC with 91.40%. 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.