language benchmark
AlignBench is a comprehensive multi-dimensional benchmark for evaluating Chinese alignment of Large Language Models. It contains 8 main categories: Fundamental Language Ability, Advanced Chinese Understanding, Open-ended Questions, Writing Ability, Logical Reasoning, Mathematics, Task-oriented Role Play, and Professional Knowledge. The benchmark includes 683 real-scenario rooted queries with human-verified references and uses a rule-calibrated multi-dimensional LLM-as-Judge approach with Chain-of-Thought for evaluation.
Updated Aug 11, 2026
Higher score ranks better on this benchmark.
| 01 | AC | 81.6% | 100.0% | 4 | C | |
| 02 | DE | 80.4% | 66.7% | 4 | C | |
| 03 | AC | 73.3% | 33.3% | 4 | C | |
| 04 | AC | 72.1% | 0.0% | 4 | C |
A closer view of the leading scores on this benchmark.
The leading models and scores on this benchmark.
What AlignBench measures and how its scores work.
AlignBench is a comprehensive multi-dimensional benchmark for evaluating Chinese alignment of Large Language Models. It contains 8 main categories: Fundamental Language Ability, Advanced Chinese Understanding, Open-ended Questions, Writing Ability, Logical Reasoning, Mathematics, Task-oriented Role Play, and Professional Knowledge. The benchmark includes 683 real-scenario rooted queries with human-verified references and uses a rule-calibrated multi-dimensional LLM-as-Judge approach with Chain-of-Thought for evaluation.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about AlignBench.
Qwen2.5 72B Instruct is currently ranked first with 81.6%.
AlignBench is a comprehensive multi-dimensional benchmark for evaluating Chinese alignment of Large Language Models. It contains 8 main categories: Fundamental Language Ability, Advanced Chinese Understanding, Open-ended Questions, Writing Ability, Logical Reasoning, Mathematics, Task-oriented Role Play, and Professional Knowledge. The benchmark includes 683 real-scenario rooted queries with human-verified references and uses a rule-calibrated multi-dimensional LLM-as-Judge approach with Chain-of-Thought for evaluation.
Yes. Higher values rank better for this benchmark.
4 model results are currently shown.
Yes. This benchmark can contribute to the current LLMBoard capability score.