E-Commerce Bench – A Long-Term E-Commerce Business Evaluation Benchmark Jointly Open-Sourced by Qwen and Taotian

1. What is E-Commerce Bench

E-Commerce Bench is a long-term e-commerce business evaluation benchmark jointly open-sourced by Alibaba's Qwen and Taotian Group. This benchmark enables LLM Agents to operate an online store in a simulated real market environment with a starting capital of 100,000 yuan for 365 consecutive days, covering the entire business process from product selection, supplier negotiation, pricing, inventory management, to cash flow control. The environment is built using de-identified data from real platforms, including 576 suppliers (152 of which are fraudulent), 8 promotional events, and 10 market emergencies. It ensures experimental reproducibility through a deterministic core, and evaluates the model's business operation capabilities from seven dimensions: profitability, negotiation, anti-fraud, long-term learning, and more.

Image source: Official article
Image source: official article

Technical positioning and domain: E-Commerce Bench belongs to the field of large language model (LLM) evaluation benchmarks, focusing on the assessment of Agent capabilities in long-term autonomous business scenarios. Its core innovation lies in extending evaluation from traditional short-term single-turn tasks to a dynamic simulation environment that encompasses the full business cycle, filling the gap in existing benchmarks regarding the evaluation of composite capabilities such as long-term decision-making, multi-round negotiations, and risk identification.

Development background: This benchmark was jointly developed by the Qwen team from Alibaba and Taotian Group. As a leading domestic e-commerce platform, Taotian Group possesses extensive real transaction data and business operation experience, while the Qwen team has deep expertise in LLM Agent technology research and evaluation system construction. The collaboration aims to address the shortcomings of existing evaluation benchmarks, such as short task cycles, oversimplified scenarios, and the difficulty in measuring the model's real-world business decision-making capabilities.

Core value: This benchmark innovatively shifts LLM evaluation from a "question-and-answer" format to a "business-operation" format, requiring the model to make a series of interrelated business decisions in a dynamic, uncertain, and potentially fraudulent environment. It not only assesses the model's knowledge base and reasoning ability, but also emphasizes its long-term planning, risk management, cash flow control, and ability to continuously learn from past experiences. It provides a standardized testing ground and training sandbox for the development of e-commerce Agents.

Technical features: The core technical innovation of this benchmark is the introduction of a deterministic negotiation kernel and a dynamic economic engine, ensuring the reproducibility of evaluations while maintaining the authenticity of business competition. It deliberately creates cash flow pressure through a three-account delayed settlement mechanism and quantifies the model's long-term learning effectiveness using the AnchorRatio metric, ensuring the rigor and completeness of the evaluation across multiple dimensions.

2. Key Features

• Long-term Business Simulation: Enable the LLM Agent to operate an online store for 365 days in a dynamic simulated market with an initial capital of 100,000 yuan, aiming to maximize total assets at year-end. This feature breaks through the limitations of traditional evaluation tasks with short durations, requiring the model to formulate and dynamically adjust business strategies throughout the entire year, comprehensively testing its long-term planning and adaptability.

• Multi-round Supplier Negotiations: The system includes 576 suppliers, and the Agent must engage in multi-round price negotiations to secure lower procurement costs. The negotiation process is driven by a deterministic core, with supplier quotes and concession strategies calculated by fixed algorithms. The LLM only renders numerical decisions into natural language dialogue, preserving the authentic haggling experience while eliminating evaluation bias caused by random sampling.

• Fraud Detection and Avoidance: The simulated environment contains 152 fraudulent suppliers and sets up five typical scams, including VIP fees, stockouts, quality reduction, false urgency, and discount traps. The Agent must identify and avoid these risks in procurement decisions. This feature quantitatively evaluates the model's risk identification capabilities and awareness of capital protection.

• Response to Dynamic Market Events: The environment includes eight platform promotional events and ten unexpected incidents (such as disasters and supply chain shocks), with demand fluctuations generated by a dynamic economic engine. The Agent must recognize external environmental signals and promptly adjust pricing and inventory strategies, testing the model's adaptive decision-making capabilities in non-stationary environments.

• Refined Inventory and Cash Flow Management: The system simulates the management of 6,886 SKUs, calculating storage costs per item per day. It also establishes a three-account settlement system, where sales revenue takes nine days of platform guarantee before returning to the bank account. This forces the Agent to actively manage working capital and avoid short-sighted operations that result in "profitable yet bankrupt" scenarios.

• Seven-dimensional Comprehensive Evaluation System: The Agent is systematically scored across seven independent dimensions: profitability, negotiation, fraud prevention, solvency, efficiency, execution, and long-term learning. The long-term learning dimension uses the AnchorRatio metric, comparing historical quotes for repeated purchases of the same product to determine whether the model is getting progressively cheaper, thus quantifying its ability to accumulate experience and optimize strategies.

3. How to Use

The deployment and usage process of E-Commerce Bench is designed to be relatively straightforward, with the development team providing full engineering support. The following are detailed operational steps and configuration instructions.

1. Get the code: Visit the E-Commerce Bench GitHub repository (https://github.com/QwenLM/E-CommerceBench), and use the git clone command to clone the project to your local environment. It is recommended to use a stable main branch version to ensure consistency between the environment and the evaluation logic.

2. Environment setup: The project requires Python 3.10 or higher. It is recommended to use a virtual environment (such as conda or venv) for isolated installation, and install all dependencies using the provided requirements.txt file, including LLM inference client libraries, third-party packages for data processing and evaluation.

3. Integrate the model to be evaluated: Enter the API Key and model name of the LLM to be evaluated in the project configuration file. E-Commerce Bench uses a model-agnostic interface design, supporting integration with various mainstream models such as Qwen, GPT, and Claude via OpenAI-compatible interfaces. It also supports deploying local models through frameworks like vLLM.

4. Start the simulation run: Run the main program to launch the long-term business simulation. The Agent will automatically begin progressing through the 365-day business decision-making cycle. During the run, real-time monitoring can be performed through log outputs to observe the Agent's daily decision-making behavior and changes in assets.

5. View the evaluation report: After the simulation ends, the system automatically generates a seven-dimensional evaluation report, including quantitative scores for each dimension, annual asset change curves, and detailed behavioral trajectory data. You can analyze the model's long-term learning performance by combining it with the AnchorRatio metric, and conduct in-depth analysis using the complete log data in JSON format.

Notes: The time required for the full 365-day business cycle will vary significantly depending on the inference speed and calling frequency of the selected model. It is recommended to adjust the business cycle length or perform multiple rounds of experiments to obtain stable statistical results. Additionally, since the environment emphasizes reproducibility, using a fixed random seed ensures that results from multiple experiments can be effectively compared.

4. Pros and Cons Analysis

5. Comparative Analysis with Similar Tools

E-Commerce Bench belongs to the niche category of LLM Agent evaluation benchmarks. Currently, there are few direct counterparts in the industry. We selected three evaluation benchmarks—TERMS-Bench, SWE-bench, and GAIA—that intersect with E-Commerce Bench in terms of task design for comparative analysis, as shown in the table below:

Selection Recommendations: If the research objective focuses on the long-term decision-making capabilities, risk identification, and multi-turn negotiation performance of LLMs in real business environments, E-Commerce Bench is currently the most comprehensive professional benchmark in terms of coverage. It is especially suitable for e-commerce Agent development teams to use for baseline model capability testing and iterative effectiveness validation. If the goal is to evaluate only the negotiation skills or price bargaining capabilities of a model, TERMS-Bench serves as a more lightweight and flexible specialized benchmark.

For Agent evaluation in the software engineering domain, SWE-bench remains the most suitable mature solution. For scenarios requiring assessment of general question-answering and open-domain tool calling capabilities, GAIA provides a more general reference framework. The value of E-Commerce Bench lies in its unique business operation positioning, which complements the aforementioned general or engineering benchmarks, collectively forming a multi-layered Agent capability evaluation matrix.

6. Editor's Summary

The release of E-Commerce Bench marks a significant evolution in LLM Agent evaluation, shifting from "task-oriented" to "business-oriented." Its core technological innovations manifest in three key areas: first, by decoupling the LLM from random sampling through a deterministic negotiation kernel, it maintains the semantic realism of multi-round interactions while ensuring strict reproducibility of evaluations, offering a methodological reference for the architectural design of similar evaluation benchmarks; second, the cash flow pressure mechanism introduced by the three-account delayed settlement captures the real-world business trap of "being profitable yet bankrupt," compelling models to pay attention to liquidity management beyond profit metrics, significantly enhancing the ecological validity of the evaluation; third, the AnchorRatio metric introduces a quantifiable measure of long-term learning capabilities for the first time, providing an objective tool for assessing a model's ability to continuously optimize.

In terms of practical value, this benchmark serves dual purposes: evaluation and training. On one hand, it provides a standardized platform for comparing the commercial capabilities of cutting-edge models such as GPT, Claude, and Qwen; on the other hand, it functions as a training sandbox for reinforcement learning and Agent architecture. It should be noted that objective limitations such as high evaluation costs and bounded decision spaces still exist, but these do not detract from its pioneering value in niche areas.

The primary target users of this benchmark include large model evaluation researchers, e-commerce Agent algorithm engineers, supply chain risk control algorithm teams, and business school teaching scenarios involving business decision simulations. As the intelligent transformation of e-commerce progresses and LLM Agents are increasingly applied in commercial contexts, the long-term, dynamic, and multi-dimensional evaluation philosophy promoted by E-Commerce Bench will gradually become an important reference standard in this field. Its design approach also holds significant implications for the development of evaluation benchmarks in other vertical domains, such as financial investment and logistics scheduling.

7. Application Scenarios

• Cross-model Commercial Capability Evaluation: Research institutions or enterprises can run a full 365-day business simulation for cutting-edge models such as GPT, Claude, and Qwen under a unified environment. By leveraging the seven-dimensional evaluation report, they can quickly obtain comparative insights into the commercial decision-making capabilities of each model. This scenario is ideal for regularly publishing model capability reports or conducting institutionalized capability reviews prior to model selection.

• E-commerce Agent Strategy Iteration and Validation: E-commerce Agent development teams can use E-Commerce Bench as a training and testing sandbox to rapidly validate new pricing algorithms, procurement strategies, or inventory management logic in a simulated environment. Based on the detailed scores in the evaluation report, they can precisely identify strategic weaknesses, significantly reducing the time and cost of real-world sampling.

• Supply Chain Risk Control Algorithm Research: In a simulated environment containing 152 fraudulent suppliers and five types of scams, risk control teams can systematically test the effectiveness of supplier screening models and risk identification algorithms. They can quantify the detection rates and false positive rates of different algorithms on specific scam types such as VIP fee traps and discount traps.

• Commercial Decision-making Training and Education: Business schools or internal corporate training teams can use E-Commerce Bench as a business simulator to enable trainees or managers to make operational decisions under uncertain conditions such as cash flow pressure, market fluctuations, and supplier fraud. Post-simulation seven-dimensional reports can be used for review and analysis, training their comprehensive decision-making and management skills in real-world business contexts.

8. FAQ

Q: How does E-Commerce Bench ensure the fairness and reproducibility of evaluation results?
A: The system achieves reproducibility through a deterministic negotiation kernel and a dynamic economic engine. All supplier quotes, concessions, and decisions are calculated using a fixed algorithm, while the LLM is only responsible for rendering the numerical results into natural language, eliminating noise from random sampling. Market demand is controlled by the episode seed to ensure reproducible randomness, guaranteeing consistent results for the same strategy across different experiments.

Q: How is the AnchorRatio metric specifically calculated to measure long-term learning effectiveness?
A: AnchorRatio measures the price change trend of an Agent when repeatedly purchasing the same product. The approach involves randomly shuffling the Agent's historical quotes to serve as a zero-hypothesis baseline. If the actual quote sequence is significantly lower than 1.0, it indicates that the model is not relying solely on random fluctuations, but instead continuously optimizing its procurement strategy based on past negotiation experiences.

Q: Which LLMs are supported for evaluation?
A: E-Commerce Bench is designed to be model-agnostic and can integrate with mainstream commercial models such as the Qwen series, GPT series, and Claude series via an OpenAI-compatible interface. It also supports open-source models through local inference frameworks like vLLM. The specific integration method involves entering the corresponding model's API Key and name in the configuration file.

Q: Can the fraudulent supplier schemes in the simulation environment be configured?
A: The current version includes five fixed scam patterns: VIP fees, stockouts, quality degradation, false urgency, and discount traps. Whether custom fraud strategies or adjustments to the proportion of fraudulent suppliers are supported can be checked in the configuration section of the official documentation. If this feature is not available, it can be implemented through modifications to the open-source code for custom expansion.

Q: How long does it take to run a full 365-day simulation?
A: The runtime primarily depends on the inference speed of the selected model and the API call latency, which can vary significantly between models. A full simulation using a cloud-based API model may take several hours to tens of hours. It is recommended to adjust the business cycle length or use parallel multi-process methods to accelerate experiments based on evaluation needs.

Q: How can I obtain detailed data from the seven-dimensional evaluation report?
A: After the simulation completes, the system automatically generates a seven-dimensional evaluation report, which includes quantitative scores for each dimension, annual asset change curves, and full behavioral trajectory data. The raw data is saved in JSON format in the output directory, and researchers can perform secondary analysis using parsing scripts to meet personalized research requirements.

9. Project Links

1. Project Website: https://ecbench.github.io/
2. GitHub Repository: https://github.com/QwenLM/E-CommerceBench
3. arXiv Technical Paper: https://arxiv.org/pdf/2608.30730 (Note: The year information in the paper number on the original text may be incorrect. Please refer to the actual arXiv page for accuracy.)