FlagEval (Libra) is a large model evaluation platform launched by BAAI. It provides multi-dimensional model capability evaluation and rankings around language, multi-modality and other directions, helping researchers and enterprises understand the capability boundaries of different models.

Tool text

Core parameters and statistics

FlagEval (Libra) is a large model evaluation platform launched by Zhiyuan Research Institute. The core problem it wants to solve is "what are the strengths and weaknesses of models that also claim to be very strong" - through unified and multi-dimensional evaluation, model capabilities can be transformed into comparable results.

Projects	Public Information
Producer	Beijing Zhiyuan Artificial Intelligence Research Institute (BAAI)
Platform alias	Libra
Platform positioning	Large model evaluation platform / evaluation system
Evaluation direction	Language, multi-modality and other multiple dimensions (subject to the official version)
Output form	Evaluation list and results
Place of Belonging	China

Evaluation value: For model selection, it is easy to distort the official publicity indicators alone. The significance of FlagEval is to provide a relatively independent and unified evaluation perspective to help users cross-validate the true capability distribution of the model.

Multi-dimensional orientation: In the name of "Libra", it emphasizes multi-dimensional trade-offs rather than a single score, which is more valuable for understanding the differences in models on different tasks. The specific evaluation dimensions and indicators are subject to the official platform.

User and market recognition

FlagEval's recognition mainly comes from the demand for an independent evaluation system from the research community and industry. As an output of the Intelligent Source Research Institute, it has a certain degree of neutrality and authority.

Institutional Endorsement: Intelligent Source Research Institute is an important AI research institution in China, and FlagEval, as its evaluation system, has a foundation in methodology and credibility.

Selection Reference: For teams that need to make model selections, third-party evaluation lists are an important source of cross-reference, which can reduce the judgment bias caused by relying solely on manufacturer propaganda.

Prerequisites for use: The reference value of the evaluation results depends on the matching between the evaluation set and real business tasks - leading the list does not necessarily mean the best performance in a specific business scenario, and needs to be retested based on your own tasks.

Cost advantage

As an evaluation platform provided by research institutions, FlagEval has obvious cost advantages for users: evaluation lists and results are usually publicly available and serve as public reference resources.

• Publicly available: The evaluation list and results are open to the public, and researchers and companies can refer to them for free.
• Reduce selection costs: Use third-party evaluations with a unified caliber to reduce the cost of the team building its own evaluation system.
• Hidden costs: Before directly applying the list conclusions to specific businesses, you still need to retest based on your own data to avoid the misjudgment that "the one who leads the list is the best".

Main functions

FlagEval organizes its capabilities around the "multi-dimensional evaluation model":

• Multi-dimensional capability evaluation: Measure model capabilities from multiple dimensions instead of a single indicator.
• Evaluation List: The relative performance of different models is presented in the form of a list to facilitate horizontal comparison.
• Multi-modal coverage: The evaluation scope covers language and multi-modality (subject to the official version).
• Evaluation method system: As a "Libra" system, it emphasizes the systematicness and comparability of evaluation methods.

The specific evaluation set, indicator definitions and coverage models are subject to real-time information from the official platform.

Model and version evolution

FlagEval continues to evolve in the form of evaluation systems and lists, and iteration is mainly reflected in the expansion of evaluation sets and updates to coverage models.

• System release stage: Zhiyuan Research Institute launches FlagEval (Libra) to establish a multi-dimensional evaluation system.
• Continuous expansion: With the rapid development of large models, the evaluation set and coverage model are continuously updated to maintain reference value.

Since the evaluation platform is continuously updated with the model ecosystem, please refer to the official platform for specific evaluation dimensions and list versions.

Technical advantages

FlagEval’s advantage comes from “research institution methodology + multi-dimensional evaluation system”.

Systematic Method: As the evaluation system of Zhiyuan Research Institute, it emphasizes systematicness and comparability in the design of evaluation methods and indicators.

Multi-dimensional trade-off: Cover multi-dimensional evaluation with the "Libra" concept to avoid a single score from covering up the difference in model capabilities.

Neutral Reference: As a third-party review, it provides a perspective that is relatively independent of manufacturer propaganda and is an important cross-reference for model selection.

How to use

How to use	Suitable for people	Features
Visit the evaluation platform	Researchers, selection team	View the list and evaluation results
Compare model performance	Technical decision-makers	Compare different models horizontally
Combined with own retest	Implementation team	Use business data for secondary verification

Typical usage is: check the multi-dimensional evaluation results of the target model on the platform, judge its reference value based on your own business tasks, and then use real business data to conduct small-scale retests, instead of directly equating the ranking of the list with business performance.

Product Pricing

The pricing model is subject to the official real-time page. Usually a freemium or subscription system is used, and basic functions can be used for free. Advanced functions or high-frequency use require paid subscriptions, and users are advised to evaluate the optimal solution based on actual usage.

Application scenarios

• Model Selection Reference: Make horizontal comparisons between multiple candidate models to assist in technology selection.
• Research and benchmark comparison: Provide a unified evaluation standard and benchmark for research work.
• Capability Boundary Analysis: Understand the strength and weakness distribution of the model on different tasks through multi-dimensional evaluation.
• Industry awareness building: Help the industry establish an objective understanding of the capabilities of large models.

Applicable people

• Researcher: A unified and comparable large model evaluation caliber is needed.
• Technical Decision Maker: Third-party evaluation and cross-validation are required when selecting models.
• Industry Practitioners: People who want to objectively understand the distribution of capabilities of different models.

The situation that is not very suitable is: for teams that need to conduct customized and personal evaluations for specific vertical businesses, the public list can only be used as a starting point, and ultimately it still needs to be based on retesting of its own business data.

Summary and Outlook

The core value of FlagEval (Libra) is to provide a relatively independent, multi-dimensional large model evaluation perspective, and it is an important public reference resource for model selection and research. Its limitation is that the evaluation set may not completely match each specific business, and the conclusion of the list needs to be combined with its own data and retested before it can be used for decision-making.

What is worth observing in the future is the update speed of its evaluation set with new models and new modalities, as well as the expansion of the evaluation dimensions. For users, it is recommended to use FlagEval as one of the cross-references for model selection, and then use real business tasks for final verification. For specific evaluation dimensions and results, please refer to the official platform’s real-time information.

Related tools: hugging-face, replicate

Version Info

• FlagEval (Libra) current version ：FlagEval is a continuously updated evaluation platform and list. The evaluation set and coverage model expand with the version and are not unified into a single software version number. The specific evaluation dimensions and list updates are subject to the official platform. （~2026-05）
• FlagEval evaluation system released ：Zhiyuan Research Institute launched the FlagEval (Libra) large model evaluation platform, established a multi-dimensional evaluation system and released the list to the outside world. It will continue to expand the evaluation set and coverage model in the future. The specific time is subject to official information. （~2023-11）