China Releases "Qiusuo" 4.0: The First Evaluation Benchmark Covering the Entire AI Industry Chain

Why AI Evaluation Needs a "Unified Ruler"

The AI industry has long faced an awkward reality: the same chip, the same model, when benchmarked by different organizations in different environments, often produces scores that cannot be directly compared. Chip manufacturers measure computing power using their own metrics, model teams validate results on their own datasets, and procurement teams are left holding two reports with no way to compare them. This "everyone speaks their own language" situation has become a real friction cost as the industry enters the stage of large-scale deployment.

According to RSS sources, the China Electronics Standardization Institute (CESI), together with relevant organizations, has released "Qiusuo" 4.0, positioned as a national-level AI full-industry-chain evaluation benchmark system. Its goal is clear: to give chip manufacturers, model laboratories, and intelligent system suppliers a unified set of testing procedures and evaluation metrics, while providing real workloads from industries such as power, healthcare, and steel, so that evaluation results become horizontally comparable.

Four-Layer Architecture, 16 Evaluation Tools Covering the Full Stack

"Qiusuo" 4.0 divides the entire evaluation system into four sections: foundational software and hardware, models and intelligent systems, security governance, and industry implementation. Under these four sections, there are 16 evaluation tools, covering everything from underlying computing power, hardware-software co-design, intelligent computing clouds, and AI software engineering, all the way to large language models, agents, embodied intelligence, intelligent terminals, AI security, and industry-specific scenarios.

The significance of this breadth of coverage is that it no longer confines AI evaluation to the single dimension of "model benchmarking." In recent years, industry assessment of AI capabilities has been heavily concentrated on language model benchmark scores, but in real deployments, the adaptation efficiency between chips and frameworks, the scheduling capability of inference clouds, and the reliability of agents in business processes are often the variables that determine project success or failure. Qiusuo 4.0 attempts to bring all of these links into the same discourse framework.

Bao Wei, Director of the Model and Application Innovation Research Office at CESI's Information Technology Center, mentioned that Qiusuo's lightweight code tools for computing, large model, and agent benchmarks have already been open-sourced, allowing the industry to quickly reproduce evaluations and verify hardware-software co-design results. The intent behind this step is clear—to transform abstract benchmark scores into referenceable evidence for actual procurement and selection.

Agent refers to an AI system capable of autonomously perceiving its environment, planning tasks, invoking tools, and executing multi-step operations to achieve goals, as distinguished from language models that only perform single-turn question answering. In industrial scenarios, agents are often used for automated customer service, data analysis, process orchestration, and similar tasks, with evaluation dimensions covering task completion rate, tool invocation accuracy, multi-step reasoning stability, and exception recovery capability. Embodied intelligence refers to embedding AI capabilities into physical entities (such as robots, robotic arms, and autonomous vehicles) so that they can perceive, decide, and act in real physical environments. These two types of systems differ from pure software models in that their performance is highly dependent on underlying chip computing power, sensor fusion, and hardware-software co-design, making them difficult for traditional benchmarks to cover—which is precisely one of the core motivations behind Qiusuo 4.0's expanded evaluation scope.

From "Model Arms Race" to Engineering Deployment

The timing of Qiusuo 4.0's release is worth noting. China's AI industry is undergoing a shift: from an "arms race" pursuing model parameter scale and leaderboard rankings to a stage driven by scenarios and centered on engineering deployment. At this stage, enterprises are no longer concerned about "how many points your model scored on MMLU," but rather "whether this solution can run stably in my business scenario and whether the cost is controllable."

According to source information, Qiusuo 4.0 has completed multiple rounds of industry chain pilot evaluations and has conducted product validation in sectors such as finance, power, and petroleum and petrochemicals, accumulating a set of practical cases. These industries share a common characteristic: high requirements for reliability and security, along with rigid demand for domestic substitution. Unified evaluation standards in these scenarios can directly reduce selection costs and make deployment outcomes more predictable.

For regulators, this system also provides a basis for evaluating the quality, security, and compliance of AI products. This is particularly important in the current regulatory environment—when AI products enter critical industries in large numbers, the lack of unified evaluation standards means regulators can only rely on manufacturers' self-reports, which in practice can hardly form effective constraints.

Relationship with International Benchmarks: Complementary, Not Replacement

Qiusuo 4.0 does not intend to replace mainstream international benchmarks such as MMLU, HumanEval, or SWE-bench. In terms of positioning, it is more like an industrial supplement to these academically oriented benchmarks. MMLU measures a model's breadth of knowledge, HumanEval tests code generation capability, and SWE-bench evaluates software engineering task performance—these benchmarks answer the question "how strong is the model?" Qiusuo 4.0, by contrast, attempts to answer a different question: "Can this AI solution actually be used in a real industrial environment, is it easy to use, and is it secure?"

The two also serve different audiences. International academic benchmarks primarily serve the research community, pursuing comparability and cutting-edge performance; Qiusuo 4.0 targets scenarios such as government procurement, industry selection, bidding, and domestic substitution, pursuing practicality and standardization.

Of course, whether an evaluation system can truly become an industry "ruler" depends on two factors: first, the actual adoption rate of the open-source tools, and second, whether evaluation results can form a stable correspondence with real deployment performance. At present, Qiusuo 4.0 is still in the early stages of promotion, and its actual influence will need to be observed through subsequent industry adoption.

MMLU (Massive Multitask Language Understanding) was proposed in 2020 by UC Berkeley and other institutions. It covers approximately 14,000 multiple-choice questions across 57 subject areas and is one of the most frequently cited benchmarks for measuring the breadth of knowledge and reasoning ability of large models. HumanEval, released by OpenAI, contains 164 Python programming problems and evaluates model coding ability through function-level code generation and unit test pass rates. SWE-bench, introduced by a Princeton University team, draws tasks from real GitHub repository issue fixes, requiring models to locate problems within the context of a complete codebase and submit patches that pass tests, making it significantly more difficult than HumanEval. Together, these three form the core toolchain for academic evaluation of model capabilities, but their common characteristic is that tasks are abstracted and detached from specific deployment environments, making it difficult to map them directly to usability and cost-effectiveness in industrial scenarios.

What It Means for Enterprises

From an enterprise perspective, the most direct benefit of unified evaluation standards is a reduction in selection costs. When products from different suppliers are evaluated under the same standards, procurement decisions have a clearer frame of reference. This is especially true for small and medium-sized enterprises, which often lack the capacity to build complete evaluation environments on their own; standardized evaluation results can significantly reduce information asymmetry in technology selection.

Another signal worth noting is the introduction of open-source code tools. Bao Wei mentioned that lightweight code tools have been open-sourced, meaning enterprises can reproduce the evaluation process themselves rather than only accepting the "official scores" provided by manufacturers. Reproducibility is the foundation of an evaluation system's credibility, and if this step is properly implemented, it will provide substantive help in building industry trust.

However, the vitality of evaluation standards ultimately depends on continuous iteration. AI technology evolves rapidly, and evaluation dimensions that are reasonable today may no longer be applicable six months from now. Whether Qiusuo 4.0 can establish a stable update mechanism will determine whether it becomes a long-term reference framework or yet another standards showcase that "peaks at release."

Background Supplement

Domestic substitution in this context refers to the process of gradually replacing original imported technology stacks with domestic chips, domestic AI frameworks, and domestic large models in key industries such as finance, power, and petroleum and petrochemicals. This process places special demands on evaluation standards: it is necessary not only to assess absolute performance, but also to evaluate migration costs, ecosystem compatibility, and long-term supply chain security. Because the hardware-software combinations of different manufacturers vary greatly, the lack of unified evaluation criteria makes it difficult for procurement teams to judge whether a substitution solution is feasible. Qiusuo 4.0's provision of real workload testing in industry scenarios is precisely intended to provide quantifiable reference evidence for such substitution decisions.

One-line Summary

CESI releases "Qiusuo" 4.0, building a unified evaluation benchmark system covering the entire AI industry chain.

Paragraph Summary

"Qiusuo" 4.0, led by the China Electronics Standardization Institute (CESI), is positioned as a national-level evaluation benchmark system covering the entire AI industry chain. It is divided into four major sections—foundational software and hardware, models and intelligent systems, security governance, and industry implementation—and includes 16 evaluation tools covering computing power, large models, agents, embodied intelligence, AI security, and industry scenarios. Its goal is to provide a unified testing process for chip manufacturers, model teams, and procurement teams, making evaluation results horizontally comparable. The system has completed pilot validation in finance, power, petroleum and petrochemicals, and other sectors, and lightweight code tools for computing and model benchmarks have also been open-sourced. Qiusuo 4.0 is positioned as an industrial supplement to international academic benchmarks such as MMLU, serving government procurement, industry selection, and domestic substitution scenarios, though its long-term influence still depends on adoption rates and iteration mechanisms.