FlagEval Libra
The large model evaluation system and open platform of the Beijing Academy of Artificial Intelligence (BAAI) is officially claimed to cover four evaluation scenarios: language, multimodality, computer vision and speech, and provides rankings, a large model arena, as well as a collection of evaluation methods and tools that combine automatic and manual evaluation.
Updated: 2026-10-30
What is FlagEval Libra?
FlagEval (Libra) is a large model evaluation system and open platform launched by the Beijing Academy of Artificial Intelligence (BAAI). Officially, it aims to establish scientific, fair and open evaluation benchmarks, methods and tool sets, help researchers comprehensively evaluate the performance of foundation models and training algorithms, and improve evaluation efficiency and objectivity by introducing AI-assisted subjective evaluation. The platform provides two main entry points: a leaderboard and a large model arena.
Who is it for?
- Model R&D teams: Use a unified benchmark to locate the model's position across different capability dimensions.
- Academic researchers: Cite evaluation results with method descriptions and dataset sources for comparative experiments. The official goal is to establish scientific, fair and open evaluation benchmarks, methods and tool sets.
- Enterprise selection personnel: Check model performance based on business-related dimensions to assist in procurement and access decisions.
- Evaluation method researchers: Refer to the platform's evaluation process to design their own evaluation systems.
- People concerned about model safety: Review the evaluation conclusions on the dimensions of security and values. The official states that the platform provides two main entry points: the leaderboard and the LLM Arena.
Key features
6Language Large Model Evaluation
It is stated that it covers dimensions such as mathematical ability, coding ability, knowledge application, reasoning ability, task solving, instruction following, as well as security and values.
Multimodal Large Model Evaluation
It is stated that the official evaluation covers tasks such as image-text classification, image-text matching and image-text generation, and includes visual-language models, text-to-image and text-to-video evaluation datasets.
Computer Vision Evaluation
It is stated that the platform supports the adaptation evaluation of backbone foundation models, covering tasks such as image classification, image retrieval, semantic segmentation, depth estimation, and few-shot classification.
Speech and Language Large Model Evaluation
It is officially stated that it covers ten types of tasks such as speech perception, audio perception, speech generation and spoken language understanding, using multiple datasets and rich metrics.
Large Model Arena
It is stated by the official that this provides an entry for adversarial comparative evaluation, used to collect subjective preference-based evaluations. It is also stated by the official that the evaluation system is launched by the Beijing Academy of Artificial Intelligence (BAAI).
Evaluation Tools and Processes
The official statement indicates that it provides evaluation processes covering both public and self-constructed datasets, combining automatic evaluation and manual evaluation. The official also states that the platform improves the efficiency and objectivity of evaluation by introducing AI-assisted subjective evaluation.
How to use FlagEval Libra
- 1Pay attention to the release time of evaluation data and model versions. Models iterate rapidly, and old conclusions are likely to become invalid.
- 2Select relevant dimensions based on business scenarios, and a high comprehensive score does not mean the best performance on your specific tasks.
- 3When building your own evaluation dataset, refer to its task division method to cover both basic capabilities and downstream task perspectives.
- 4Subjective evaluations have preference differences; conclusions from manual evaluations require attention to sample size and annotation criteria.
- 5When citing evaluation results, retain the platform name and evaluation time to ensure traceability of conclusions.
Traffic & popularity
Data month Sep 2026
FlagEval Libra receives about 10.4K visits per month.
Monthly visits
12 monthsTraffic sources
- Direct43.3%
- Search32.3%
- Referrals12.2%
- Social5.7%
- Mail1.9%
- Other4.7%
Top countries
- China45.3%
- Vietnam17.3%
- Germany13.1%
- Hong Kong, China8.4%
- United States7.4%
Estimated from third-party traffic data.
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