Chinese Llm Benchmark — AI Agent Framework: Live Stats & TrendScore

Live GitHub stats, community sentiment, and trend data for Chinese Llm Benchmark. TrendingBots tracks star velocity, fork activity, and what developers are saying — updated from real data sources.

GitHub data synced: May 1, 2026 • Sentiment updated: Apr 13, 2026

GitHub Statistics

Community Sentiment

Community Buzz: DGX Spark + Qwen3.5-35B-A3B: MXFP4 produces Chinese character artifacts — anyone else seeing this? as mentioned on Reddit, and 'Kimi questioned it' from the r/aigossips subreddit

Pros & Cons

What People Love

Trillion parameters model, Open-source AI models, Advancements in AI research

Common Complaints

Chinese character artifacts, Model optimization issues

Biggest Positive: Trillion parameters model

Biggest Negative: Chinese character artifacts

Why Chinese Llm Benchmark Stands Out

ReLE is different from alternatives because it provides a comprehensive and scalable benchmarking system for evaluating language models, with a focus on Chinese LLMs. Its technical approach involves a structured benchmark with multiple tasks and models, allowing for a detailed analysis of model capabilities. ReLE solves the problem of limited benchmarking capabilities for Chinese language models, providing a valuable resource for researchers and developers. By offering a large model defect library and supporting multiple models, ReLE enables users to evaluate and improve language models more effectively.

Built With

Build a custom language model benchmarking platform — ReLE provides a scalable system and structured benchmark for diagnosing capability anisotropy in Chinese LLMs, Build a large language model evaluation framework — ReLE supports multiple models and tasks, including chatgpt, gpt-5.4, and ernie-5.0, Build a research project analyzing language model capabilities — ReLE offers a comprehensive benchmarking system with over 200 million model defects, Build a comparison tool for commercial and open-source language models — ReLE provides a detailed ranking of models, including step3.5-flash, kimi-k2.5, and MiniMax-M2.7, Build a platform for evaluating language model performance on various tasks — ReLE covers 7 domains, including education, healthcare, finance, and law

Getting Started

  1. Install ReLE using the command `pip install rele`
  2. Configure the model and task settings using the `config.json` file
  3. Run the benchmarking process using the command `python run_benchmark.py`
  4. Evaluate the results using the `evaluate_results.py` script
  5. Try running the `example_use_case.py` script to verify that ReLE works as expected

About

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-k2.6、ernie4.5、MiniMax-M2.7、deepseek-v4、Qwen3.6、llama4、智谱GLM-5.1、MiMo-V2、LongCat、gemma4、mistral等开源大模型。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。

Official site: https://nonelinear.com

Category & Tags

Category: development

Tags: agentic-ai, artificial-intelligence, llm-agent, llm-evaluation

Market Context

Competitive AI market with Xiaomi and Z.AI making significant entries