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GAOKAO-Bench

Open Source
Evaluation and Monitoring Updated Feb 15, 2026
Compared with 49 other Evaluation and Monitoring agents Open source Self-hostable Updated Feb 2026
💰 Open Source 🔄 Updated Feb 2026 🖥️ Self-hostable

💰 Pricing

Open Source

Pricing not publicly listed.

🎯 Use Cases

Benchmarking Chinese-language LLMs against human academic standards Evaluating models' multi-disciplinary knowledge (e.g., math, literature, sciences) Testing reasoning skills under exam-style constraints Comparing model generations against human student responses Monitoring model improvement across training iterations

⚖️ Pros & Cons

✅ Pros

  • Culturally specific benchmark not covered by Western-centric evaluations
  • High-quality dataset derived from rigorously designed exam questions
  • Transparent methodology through open-source implementation
  • Measures both breadth and depth of knowledge across subjects

❌ Cons

  • Primarily relevant for Chinese-language applications
  • Limited to static question sets without adaptive testing
  • Requires translation/localization for non-Chinese contexts
  • Human grading may be needed for subjective questions

Overview

GAOKAO-Bench is an open-source evaluation framework that leverages questions from the Chinese National College Entrance Examination (GAOKAO) to test large language models' proficiency in language comprehension, logical reasoning, and subject-specific knowledge. It provides standardized metrics for comparing model performance on a culturally relevant, high-stakes academic benchmark.

Problem It Solves

It enables objective measurement of AI models' capabilities in handling complex, knowledge-intensive tasks mirroring human academic assessments.

Target Audience: Developers and teams working with evaluation and monitoring automation.

Inputs

  • User configuration
  • API credentials (if required)
  • Task parameters

Outputs

  • Automated task results
  • Status reports
  • Generated content or actions

Example Workflow

  1. 1 User configures the agent with required parameters
  2. 2 Agent receives input data or trigger
  3. 3 Agent processes the request using its core logic
  4. 4 Agent interacts with external services if needed
  5. 5 Results are returned to the user

Sample System Prompt


              You are GAOKAO-Bench, an AI assistant. Help the user accomplish their task efficiently.

            

Tools & Technologies

LLM APIs Python

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FAQs

Is this agent open-source?
Yes
Can this agent be self-hosted?
Yes
What skill level is required?
Intermediate

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GAOKAO-Bench