FlagEval

Overview

FlagEval (Tiancheng) is a large model evaluation system and open platform launched by the Beijing Academy of Artificial Intelligence (BAAI). It is aimed at researchers, developers, and enterprise teams, providing relatively systematic model evaluation tools and methods. The platform emphasizes science, fairness, and openness, and is suitable for performance analysis of foundation models, training algorithms, and multimodal models.

FlagEval adopts a three-dimensional evaluation framework of "Capability-Task-Metric" to measure model performance in real-world applications from multiple dimensions, covering common scenarios such as dialogue, question answering, and sentiment analysis, and supporting multiple data types such as text, images, and video. According to public information, the platform has provided more than 22 datasets and 80,000 evaluation questions, and covers a large number of open-source and closed-source models, making horizontal comparison and result analysis more convenient.

Main Features

• Three-dimensional evaluation framework: The evaluation system is designed based on "Capability-Task-Metric," making it more suitable for analyzing model performance from both cognitive ability and task effectiveness.
• Rich evaluation data: Provides more than 22 datasets and 80,000 evaluation questions, covering different scenarios, difficulty levels, and language types.
• Multimodal model evaluation: Supports multiple modalities such as text, images, and video, suitable for unified evaluation of large language models and multimodal models.
• Automated evaluation process: Supports automated pipelines for subjective and objective evaluation, helping users improve evaluation efficiency.
• Broad model compatibility: Supports multiple AI frameworks and hardware architectures, including PyTorch, MindSpore, and various domestic/mainstream computing platforms.
• Rankings and result display: Provides evaluation result tables and rankings, making it easy to view the scores of different models across tasks.
• Task creation and upload capabilities: Users can upload models, code, and configurations, create evaluation tasks, and view results online.
• Community co-building mechanism: Supports continuous updates to evaluation content and encourages researchers to contribute datasets, models, and evaluation schemes.

Product Pricing

At present, public information does not clearly provide standardized pricing details. FlagEval is more oriented toward a scientific research and open evaluation platform. For specific usage methods, resource limits, or service rules, it is recommended to refer to the latest pages on the official website and the platform's actual instructions.

Frequently Asked Questions

Who is FlagEval suitable for?

It is suitable for large model researchers, algorithm engineers, model platform teams, and enterprise users who need to conduct model selection and performance validation.

What models can FlagEval evaluate?

Public materials show that the platform supports evaluation of various types of models such as text and multimodal models, and covers a large number of open-source and closed-source models.

Does it support automated evaluation?

Yes. The platform provides automated pipelines for subjective and objective evaluation. Users can submit tasks and have the system complete the evaluation process.

What needs to be prepared before use?

Usually, you need to prepare the model to be evaluated, inference code, and related configuration files; when creating a task, you also need to fill in parameters such as the evaluation domain, task type, image, and computing configuration.

What is the core value of FlagEval?

Its core value lies in providing a relatively unified and standardized evaluation framework, helping users compare model capabilities more efficiently, analyze strengths and weaknesses, and provide a basis for model optimization and selection.