EcomBench - A benchmark for evaluating e-commerce AI capabilities launched by Alibaba and Tongyi, among others.

What is EcomBench?

EcomBench is an AI capability evaluation benchmark for e-commerce scenarios, jointly launched by Tongyi Labs and Skylenage. Built on real-world data, EcomBench covers seven major categories of e-commerce tasks, including policy consultation, cost estimation, and product selection decisions, comprehensively measuring the overall capabilities of intelligent agents in e-commerce environments. EcomBench effectively evaluates the actual performance of AI assistants in complex business scenarios, providing direction for model optimization and driving the development of e-commerce AI towards greater intelligence and reliability.

EcomBench's main functions

• Comprehensive competency assessmentIt covers seven typical tasks in e-commerce operations, such as policy compliance, cost and pricing, fulfillment, marketing strategy, intelligent product selection, business opportunity discovery and inventory management, to ensure that the comprehensive capabilities of the AI assistant are evaluated from multiple dimensions.
• Real-world scenario simulationBased on real user questions and business requests from major global e-commerce platforms, each evaluation task originates from real-world scenarios, truly reflecting the actual needs of e-commerce practitioners.
• Difficulty levelsThe system sets up three levels of difficulty, ranging from basic common sense to complex reasoning, to clearly define the model's capability boundaries and help developers understand the strengths and weaknesses of the AI assistant.
• Dynamic updatesA quarterly update mechanism is adopted to promptly incorporate the latest policies, regulations, market dynamics, and business hotspots, ensuring the timeliness and challenge of the evaluation tasks.
• Professional labeling and verificationThrough a rigorous human-machine collaborative process, including question selection, editing and rewriting, and expert annotation and verification, we ensure high-quality data and accurate answers.

EcomBench's technical principles

• Data collection and filteringData is collected from real user interactions on major global e-commerce platforms (such as Amazon) to ensure data authenticity and diversity. A large language model is used to initially screen a massive number of user questions, eliminating subjective, open-ended, or unanswerable requests, and retaining representative questions with clear answers.
• Problem Optimization and AnnotationThe selected data is manually polished by experienced e-commerce experts to ensure that the questions are clearly worded, the background is complete, and the objectives are clearly defined. Each question is independently annotated by at least three experts for cross-validation, and questions with inconsistent answers are eliminated to ensure the accuracy and reliability of the data.
• Task design and classificationThe problems are divided into seven categories of e-commerce tasks, covering all key aspects of e-commerce operations. Based on the complexity of the tasks, the problems are divided into three difficulty levels. High-difficulty tasks are selected through "tool capability level" to ensure that the three levels of tasks are sufficiently challenging.
• Dynamic update mechanismThe question bank is iterated every three months to incorporate the latest policies, regulations, market dynamics, and business hotspots, maintaining the timeliness and challenge of the benchmark.
• Assessment and FeedbackThis evaluation comprehensively assesses the AI assistant's information integration, logical reasoning, rule application, and decision-making consistency in e-commerce scenarios through various task types and difficulty levels. It provides developers with detailed evaluation reports to help them understand the model's shortcomings and offer clear directions for future optimization.

EcomBench project address

• Project official websitehttps://ecombench.ai/
• HuggingFace model libraryhttps://huggingface.co/datasets/Alibaba-NLP/EcomBench
• arXiv technical paper: https://arxiv.org/pdf/2512.08868

Application scenarios of EcomBench

• AI Assistant Capability AssessmentIt provides developers and enterprises with standardized evaluation tools to accurately identify the strengths and weaknesses of AI assistants in e-commerce scenarios, and to help with optimization and selection.
• E-commerce operation optimizationThrough features such as policy compliance, cost pricing, and intelligent product selection, it helps e-commerce companies optimize their operational processes and improve decision-making efficiency and profitability.
• E-commerce education and trainingAs a teaching resource, it provides practical cases for practitioners and developers, promoting the popularization of e-commerce AI knowledge and skills training.
• Industry standard settingEstablish capability standards for e-commerce AI assistants, standardize industry evaluation systems, and promote best practice cases.
• Market Dynamics MonitoringThe quarterly update mechanism promptly reflects policies, regulations, and market trends, helping businesses and developers quickly adapt to market changes.