No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by jeinlee1991 · Agent Tool · ★ 6.5k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is chinese-llm-benchmark safe to install? View the security audit →
非线智能 NoneLinear - ReLE评测：中文AI大模型能力评测（持续更新） ReLE （Really Reliable Live Evaluation for LLM），原名CLiB 目前已囊括384个大模型，覆盖chatgpt、gpt-5.5、谷歌gemini-3.1-pro、Claude-4.8、文心ERNIE-X1.1、ERNIE-5.1、qwen3.7-max、qwen3.7-plus、百川、讯飞星火、商汤senseChat等商用模型， 以及step3.7-flash、kimi-k2.6、ernie4.5、MiniMax-M3、deepseek-v4、Qwen3.6、llama4、智谱GLM-5.1、MiMo-V2、LongCat、gemma4、mistral等开源大模型。 支持多维度能力评测，包括教育、医疗与心理健康、金融、法律与行政公务、推理与数学计算、语言与指令遵从、agent与工具调用等7个领域，以及细分的300个维度（比如牙科、高中语文…）。详见我们的技术报告ReLE: A Scalable System and Structured Benchmark for Diagnosing Capability Anisotropy in Chinese LLMs 媒体报道(机器之心):全球304个中文大模型实测：没有“全能王者”，ReLE凭70%降本方案破解评估困局 不仅提供排行榜，也提供规模超200万的大模型缺陷库！方便广大社区研究分析、改进大模型。 为您的私有大模型提供免费评测服务，联系我们(非线智能 ReLE benchmark团队)：加微信 目录 🔄最近更新 ⚓GitHub热门大模型评测项目 📝大模型基本信息 📊排行榜 0、多模态排行榜 1、综合能力排行榜 1.1 推理类模型排行榜 1.2 商用大模型排行榜（含开源模型的付费API） 1.3 开源大模型排行榜 2、教育排行榜 2.1 小学学科 2.3 中考TODO 2.4 高中学科 2.6 高等教育TODO 2.7 考研TODO | 2.8 教师资格TODO 3、医疗与心理健康排行榜 3.1 医师
| Stars | 6,467 |
| Forks | 263 |
| Category | Agent Tool |
| Quality Score | 50.9624801952756/100 |
| Open Issues | 19 |
| Last Updated | 2026-10-10 |
| Created | 2023-06-04 |
| Platforms | claude-code, gemini |
| Est. Tokens | ~28k |
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chinese-llm-benchmark is 非线智能 NoneLinear - ReLE评测：中文AI大模型能力评测（持续更新）：目前已囊括374个大模型，覆盖chatgpt、gpt-5.4、谷歌gemini-3.1-pro、Claude-4.6、文心ERNIE-X1.1、ERNIE-5.0、qwen3.6-max、qwen3.6-plus、百川、讯飞星火、商汤senseChat等商用模型， 以及step3.5-flash、kimi-k2.. It is categorized as a Agent Tool with 6.5k GitHub stars.
You can find installation instructions and usage details in the chinese-llm-benchmark GitHub repository at github.com/jeinlee1991/chinese-llm-benchmark. The project has 6.5k stars and 263 forks, indicating an active community.
The top alternatives to chinese-llm-benchmark on Agent Skills Hub include PocketFlow, generative-ai, ouroboros. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: