In a performance comparison in 2025, DeepSeek's AI model DeepSeek-R1 showed remarkable performance, showing results on-par with OpenAI's OpenAI-01-1217 model. This result has garnered a lot of attention because DeepSeek's model is considered to be notably more efficient and was trained with a significantly lower budget. In addition to that, it has shown the capabilities of the Chinese artificial intelligence industry despite being sanctioned by the United States.

China WMWMP: Average Performance Benchmark: On Sale Open-end: State Owned Large Commercial Bank data was reported at 2.900 % in Feb 2025. This stayed constant from the previous number of 2.900 % for Jan 2025. China WMWMP: Average Performance Benchmark: On Sale Open-end: State Owned Large Commercial Bank data is updated monthly, averaging 2.330 % from Aug 2022 (Median) to Feb 2025, with 31 observations. The data reached an all-time high of 3.850 % in Aug 2022 and a record low of 2.150 % in Jul 2024. China WMWMP: Average Performance Benchmark: On Sale Open-end: State Owned Large Commercial Bank data remains active status in CEIC and is reported by Puyi Standard. The data is categorized under China Premium Database’s Financial Market – Table CN.ZAM: Puyi Standard: Average Performance Benchmark: On Sale: Whole Market Wealth Management Product.

CN: WMCP: Average Performance Benchmark: On Sale Close-end: State Owned Large Commercial Bank data was reported at 2.380 % in Feb 2025. This records a decrease from the previous number of 2.450 % for Jan 2025. CN: WMCP: Average Performance Benchmark: On Sale Close-end: State Owned Large Commercial Bank data is updated monthly, averaging 3.100 % from Aug 2022 (Median) to Feb 2025, with 31 observations. The data reached an all-time high of 4.210 % in Sep 2022 and a record low of 2.380 % in Feb 2025. CN: WMCP: Average Performance Benchmark: On Sale Close-end: State Owned Large Commercial Bank data remains active status in CEIC and is reported by Puyi Standard. The data is categorized under China Premium Database’s Financial Market – Table CN.ZAM: Puyi Standard: Average Performance Benchmark: On Sale: Wealth Management Company Product.

In a performance comparison on Chinese language benchmarking in 2025, DeepSeek's AI model Deepseek-R1 outperformed all other representative models, except the DeepSeek V3 model. The models from DeepSeek performed best in the mathematics and Chinese language benchmarks, and the weakest in coding.

Construction project and waste management performance

In a performance comparison on coding benchmarking in 2025, DeepSeek's AI model DeepSeek-R1 showed the weakest results among all tested skills. Here, OpenAI's OpenAI-o1-1217 performed the best. However, what was remarkable about the results was that DeepSeek's model achieved comparable results with a much smaller model and much lower training costs, calling in question the models of the competition. In addition, the Chinese AI industry is subjected to sanctions by the United States.

This dataset contains annual building and performance data for those properties required to report. Property data is pulled from the Office of Property Assessment. Energy and water data is self-reported by building owners using the EPA Portfolio Manager tool. This data will be updated annually.

Performance Testing Service Market Outlook

The global Performance Testing Service Market size was valued at USD 2.5 billion in 2023 and is projected to reach USD 5.8 billion by 2032, growing at a CAGR of 9.7% during the forecast period. The growth of this market is primarily driven by the increasing complexity of software applications, coupled with the rising demand for high-quality performance and user experience. As businesses across various industries digitize their operations, the need for robust performance testing services to ensure the reliability and efficiency of software applications has become more critical than ever. This demand is fueled by the growing adoption of cloud-based solutions, the proliferation of mobile applications, and the increasing importance of customer satisfaction and retention.

One of the key growth factors for the performance testing service market is the rapid technological advancements and the increasing complexity of applications. Modern applications require performance testing to ensure they can handle large volumes of users and transactions simultaneously. As businesses adopt more advanced technologies such as artificial intelligence, machine learning, and big data analytics, the need for sophisticated performance testing services is amplified. These technologies demand high processing power and seamless integration, making performance testing an essential component to verify that applications can deliver optimal performance under varying load conditions.

Another significant factor contributing to the market's growth is the increasing focus on enhancing user experience and customer satisfaction. In today's competitive business environment, providing a flawless user experience is crucial for the success of any application. Performance testing services help identify and resolve performance bottlenecks, ensuring that applications run smoothly and efficiently. This, in turn, leads to improved user satisfaction, reduced downtime, and increased customer loyalty. Moreover, the rise of e-commerce and online services has further emphasized the need for performance testing, as businesses aim to provide a seamless and reliable experience for their customers.

The growing adoption of cloud computing is also driving the demand for performance testing services. Cloud-based applications offer several advantages, such as scalability, flexibility, and cost-efficiency. However, they also present unique challenges in terms of performance and reliability. Performance testing services are essential to ensure that cloud-based applications can handle fluctuating workloads and maintain consistent performance levels. As more organizations migrate their operations to the cloud, the demand for performance testing services is expected to rise significantly. Additionally, the increasing use of DevOps and agile methodologies in software development has further accelerated the need for continuous performance testing throughout the development lifecycle.

In terms of regional outlook, North America is expected to dominate the performance testing service market, owing to the presence of major technology companies and early adopters of advanced technologies. The region's well-established IT infrastructure and the increasing focus on digital transformation are key factors driving the market's growth. Europe is also anticipated to witness significant growth, driven by the rising adoption of cloud computing and the growing emphasis on enhancing user experience. The Asia Pacific region is poised to experience the highest growth rate, fueled by the rapid digitalization of businesses and the increasing demand for performance testing services in emerging economies such as China and India. Other regions, including Latin America and the Middle East & Africa, are also expected to contribute to the market's growth, albeit at a slower pace.

Service Type Analysis

Load testing is a critical segment within the performance testing service market. This type of testing assesses how well an application or system performs under a specific load, identifying the maximum capacity and any bottlenecks that could impact performance. Load testing is particularly important for applications that expect high traffic volumes, such as e-commerce platforms, financial services applications, and social media networks. As businesses increasingly rely on digital platforms to engage with customers and manage operations, the need for load testing services is expected to grow. Additionally, the rise in cyber-attacks and the need for robust security measures further underscore the importance of load testing to ensu

Big Data Testing Market Outlook

The Big Data Testing market size is witnessing an impressive trajectory, with a projected growth from $6.5 billion in 2023 to $18.3 billion by 2032, reflecting a robust compound annual growth rate (CAGR) of 12.1% during the forecast period. This substantial growth is driven by the increasing adoption of big data analytics across various industries, as organizations seek to leverage data for strategic decision-making and competitive advantage. The proliferation of data generated from digital technologies, IoT devices, and other data sources provides a fertile ground for big data testing solutions to ensure data integrity, performance, and security.

The growth of the Big Data Testing market is significantly influenced by the increasing complexity and volume of data that organizations have to manage. As data-driven strategies become central to business success, the need for efficient testing solutions that can handle massive, diverse datasets is critical. Furthermore, the integration of artificial intelligence (AI) and machine learning (ML) in testing processes enhances the ability to identify patterns and anomalies, thus improving the accuracy and speed of testing procedures. The trend towards digital transformation across industries further fuels the demand for comprehensive testing solutions that can ensure seamless data integration and functionality.

Another key growth factor is the heightened focus on data security and compliance. With stringent regulations such as GDPR and CCPA in place, businesses are compelled to invest in robust testing frameworks that can safeguard sensitive information and ensure compliance with legal requirements. Security testing, therefore, becomes a pivotal component of the big data testing ecosystem. Moreover, the risk of cybersecurity threats necessitates continuous testing and monitoring to detect vulnerabilities and protect data assets. This trend is expected to drive significant investment in security testing solutions, contributing to overall market growth.

The increasing adoption of cloud-based solutions also plays a crucial role in the growth of the Big Data Testing market. Cloud platforms offer scalability, flexibility, and cost-effectiveness, making them an attractive option for enterprises looking to expand their big data initiatives. The shift towards cloud deployment enables businesses to leverage advanced testing tools and technologies without the need for substantial infrastructure investments. As more companies transition to the cloud, the demand for cloud-based testing services is anticipated to rise, further propelling the market's expansion.

As organizations increasingly transition to cloud environments, the importance of Cloud Data Quality Monitoring and Testing becomes more pronounced. This process ensures that data integrity is maintained as it moves to and from cloud platforms, which is crucial for businesses that rely on accurate data for decision-making. Cloud Data Quality Monitoring and Testing involves a series of checks and validations to ensure that data is complete, consistent, and accurate, regardless of its source or destination. By implementing these practices, organizations can mitigate risks associated with data discrepancies and enhance their overall data governance strategies. This is particularly important in today's digital landscape, where data is a key asset driving innovation and competitive advantage.

Regionally, North America leads the Big Data Testing market due to the presence of major technology companies and early adoption of advanced analytics solutions. The region's robust technological infrastructure and focus on innovation provide a conducive environment for market growth. Meanwhile, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by rapid digitalization, increasing internet penetration, and government initiatives supporting data analytics and cloud computing advancements. Europe also presents significant opportunities, with growing investments in data-driven technologies across various sectors.

Component Analysis

The Big Data Testing market is segmented by components into software and services, each playing a vital role in the ecosystem. Software solutions are at the core of the testing process, providing tools and platforms that facilitate data validation, performance benchmarking, security analysis, and functional testing. The increa

throughput

This dataset contains annual building and performance data for those properties which reported between years 2013 to 2016. Property data is pulled from the Office of Property Assessment. Data is self-reported by building owners using the EPA Portfolio Manager tool. This data will be updated annually.

Trouble downloading or have questions about this City dataset? Visit the OpenDataPhilly Discussion Group

The global Benchmarking Services for Transportation Rates and Logistics Performance Metrics market size was valued at USD XXX million in 2025 and is projected to grow at a compound annual growth rate (CAGR) of XX% during the forecast period, reaching USD XXX million by 2033. The market is primarily driven by the increasing need for transportation rate benchmarking to optimize logistics costs, improve supply chain performance, and stay competitive in the global marketplace. Additionally, the growing adoption of digital technologies, such as cloud computing, big data analytics, and artificial intelligence (AI), is fueling market growth as it enables more sophisticated and comprehensive benchmarking analysis. Key trends in the market include the growing focus on data quality and accuracy, the integration of predictive analytics and machine learning algorithms to enhance benchmarking capabilities, and the rise of online benchmarking platforms that provide user-friendly interfaces and customizable reports. The market is segmented by type (online and offline services) and application (small and medium-sized enterprises and large enterprises), with the online segment expected to gain significant traction due to its cost-effectiveness and accessibility. Major companies operating in the market include Benchmarking Success, PwC, Xeneta, enVista, Drewry, AFS, E2open, CLX Logistics, CT Logistics, DAT, Data2Logistics, Establish, Freightos, FreightWaves, Interlog Group, and Tim Consult, among others.

CN: WMCP: Average Performance Benchmark: New Issuance Close-end: State Owned Large Commercial Bank data was reported at 2.390 % in Feb 2025. This records a decrease from the previous number of 2.440 % for Jan 2025. CN: WMCP: Average Performance Benchmark: New Issuance Close-end: State Owned Large Commercial Bank data is updated monthly, averaging 3.090 % from Aug 2022 (Median) to Feb 2025, with 31 observations. The data reached an all-time high of 4.220 % in Sep 2022 and a record low of 2.390 % in Feb 2025. CN: WMCP: Average Performance Benchmark: New Issuance Close-end: State Owned Large Commercial Bank data remains active status in CEIC and is reported by Puyi Standard. The data is categorized under China Premium Database’s Financial Market – Table CN.ZAM: Puyi Standard: Average Performance Benchmark: New Issuance: Wealth Management Company Product.

BIG-Bench (Srivastava et al., 2022) is a diverse evaluation suite that focuses on tasks believed to be beyond the capabilities of current language models. Language models have already made good progress on this benchmark, with the best model in the BIG-Bench paper outperforming average reported human-rater results on 65% of the BIG-Bench tasks via few-shot prompting. But on what tasks do language models fall short of average human-rater performance, and are those tasks actually unsolvable by current language models? In this work, we focus on a suite of 23 challenging BIG-Bench tasks which we call BIG-Bench Hard (BBH). These are the task for which prior language model evaluations did not outperform the average human-rater. We find that applying chain-of-thought (CoT) prompting to BBH tasks enables PaLM to surpass the average humanrater performance on 10 of the 23 tasks, and Codex (code-davinci-002) to surpass the average human-rater performance on 17 of the 23 tasks. Since many tasks in BBH require multi-step reasoning, few-shot prompting without CoT, as done in the BIG-Bench evaluations (Srivastava et al., 2022), substantially underestimates the best performance and capabilities of language models, which is better captured via CoT prompting. As further analysis, we explore the interaction between CoT and model scale on BBH, finding that CoT enables emergent task performance on several BBH tasks with otherwise flat scaling curves."

The IT Benchmarking Services market is experiencing robust growth, driven by the increasing need for organizations to optimize IT operations and enhance efficiency. The market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching an estimated $45 billion by 2033. This growth is fueled by several key factors. Firstly, the rising adoption of cloud computing and digital transformation initiatives compels businesses to regularly assess their IT performance against industry best practices. Secondly, the increasing complexity of IT infrastructures, including diverse software and hardware components, necessitates professional benchmarking services to identify bottlenecks and areas for improvement. Finally, the growing pressure to reduce IT costs and enhance Return on Investment (ROI) is further driving demand for these services. The market is segmented by application (SMEs and Large Enterprises) and service type (Internal and External Benchmarking). Large enterprises currently dominate the market due to their higher IT budgets and greater need for comprehensive performance analysis. However, the SME segment is expected to witness significant growth in the coming years, driven by increasing cloud adoption and the availability of cost-effective benchmarking solutions. While internal benchmarking services offer cost advantages, external benchmarking services provide valuable industry insights and best-practice comparisons, attracting organizations seeking to gain a competitive edge. Geographically, North America and Europe currently hold the largest market share, but the Asia-Pacific region is predicted to showcase the fastest growth due to rapid IT adoption and economic expansion in countries like China and India. Competition within the market is intense, with established players like Gartner, Forrester, and IDC vying for market share alongside specialized providers such as Avasant, BMC Software, and The Hackett Group.

Benchmarking Services for Transportation Rates Market Outlook

The global Benchmarking Services for Transportation Rates Market is poised to witness a substantial growth, with projections indicating a growth rate of 8.2% CAGR from 2024 to 2032. The growth of this market is primarily driven by the escalating demand for cost-efficient and optimized transportation solutions among various industries worldwide.

The increasing emphasis on cost reduction and efficiency enhancement within the transportation and logistics sector is a pivotal growth factor for the Benchmarking Services for Transportation Rates Market. With the global supply chain becoming more complex and competitive, companies are compelled to adopt benchmarking services to analyze and compare transportation rates. This aids in identifying potential areas for cost savings and operational improvements. Additionally, the growing e-commerce industry, with its associated high demand for efficient logistics and transportation solutions, further fuels the need for these benchmarking services.

Technological advancements and the proliferation of big data analytics are also significantly contributing to the growth of this market. The integration of advanced analytics enables companies to derive actionable insights from vast amounts of data, facilitating better decision-making processes. As a result, businesses can effectively compare rates, performance, and routes, leading to enhanced operational efficiency and cost-effectiveness. The adoption of AI and machine learning in benchmarking services is also expected to revolutionize the market, providing more accurate and timely data analysis.

Moreover, regulatory compliance and sustainability initiatives are driving the demand for transportation rate benchmarking services. Governments worldwide are increasingly imposing regulations on transportation emissions and efficiency. Companies are, therefore, leveraging these services to ensure compliance with environmental standards and to optimize their operations in line with sustainability goals. This trend is particularly notable in developed regions such as North America and Europe, where stringent regulations are in place.

Regionally, North America is anticipated to dominate the Benchmarking Services for Transportation Rates Market due to the presence of robust logistics infrastructure and a high adoption rate of advanced technologies. The Asia Pacific region is expected to witness significant growth, driven by the rapid expansion of the e-commerce sector and increasing investments in transportation infrastructure. Europe, Latin America, and the Middle East & Africa are also projected to experience steady growth, supported by regulatory mandates and the rising need for efficient logistics solutions.

Service Type Analysis

The Benchmarking Services for Transportation Rates Market is segmented by service type into Freight Rate Benchmarking, Carrier Performance Benchmarking, Route Optimization Benchmarking, and others. Freight Rate Benchmarking is a crucial segment, providing companies with critical insights into prevailing market rates for various transportation modes. This service allows businesses to negotiate better rates with carriers and achieve significant cost savings. The growing emphasis on cost-efficiency and the need to stay competitive in a global market are driving the demand for freight rate benchmarking services.

Carrier Performance Benchmarking is another vital segment where businesses evaluate the performance metrics of different carriers. This includes on-time delivery rates, handling of goods, and overall service quality. By leveraging these insights, companies can make informed decisions about carrier selection and partnership, ensuring that they work with the most reliable and efficient service providers. This segment is expected to grow as businesses increasingly prioritize service quality and efficiency.

Route Optimization Benchmarking focuses on analyzing and improving transportation routes to enhance efficiency and reduce costs. This involves studying various route options, considering factors such as distance, traffic conditions, and fuel consumption. The rise in fuel prices and the need to minimize carbon footprints are driving the demand for route optimization benchmarking services. Companies are increasingly adopting these services to ensure the most cost-effective and environmentally friendly transportation routes.

Other services in this market include customized benchmarking solutions tailored to meet specif

LLM Health Benchmarks Dataset The Health Benchmarks Dataset is a specialized resource for evaluating large language models (LLMs) in different medical specialties. It provides structured question-answer pairs designed to test the performance of AI models in understanding and generating domain-specific knowledge.

Primary Purpose This dataset is built to: - Benchmark LLMs in medical specialties and subfields. - Assess the accuracy and contextual understanding of AI in healthcare. - Serve as a standardized evaluation suite for AI systems designed for medical applications.

Key Features

Covers 50+ medical and health-related topics, including both clinical and non-clinical domains. Includes ~7,500 structured question-answer pairs. Designed for fine-grained performance evaluation in medical specialties.

Applications

LLM Evaluation: Benchmarking AI models for domain-specific performance. Healthcare AI Research: Standardized testing for AI in healthcare. Medical Education AI: Testing AI systems designed for tutoring medical students.

Dataset Structure The dataset is organized by medical specialties and subfields, each represented as a split. Below is a snapshot:

Each split contains: - Questions: The medical questions for the specialty. - Answers: Corresponding high-quality answers.

Usage Instructions Here’s how you can load and use the dataset:

from datasets import load_dataset

Load the dataset
dataset = load_dataset("yesilhealth/Health_Benchmarks")

Access specific specialty splits
oncology = dataset["Oncology"]
internal_medicine = dataset["Internal_Medicine"]

View sample data
print(oncology[:5])

Evaluation Workflow

Model Input: Provide the questions from each split to the LLM. Model Output: Collect the AI-generated answers. Scoring: Compare model answers to ground truth answers using metrics such as: Exact Match (EM) F1 Score Semantic Similarity

Citation If you use this dataset for research or development, please cite:

plaintext @dataset{yesilhealth_health_benchmarks, title={Health Benchmarks Dataset}, author={Yesil Health AI}, year={2024}, url={https://huggingface.co/datasets/yesilhealth/Health_Benchmarks} }

License This dataset is licensed under the Apache 2.0 License.

Feedback For questions, suggestions, or feedback, feel free to contact us via email at [hello@yesilhealth.com].

In a performance comparison on mathematics benchmarking in 2025, DeepSeek's AI model Deepseek-R1 outperformed all other representative models. The models from DeepSeek performed best in the mathematics and Chinese language benchmarks, and the weakest in coding.

In a performance comparison on English language benchmarking in 2025, DeepSeek's AI model DeepSeek-R1 showed strong results, outperforming most other representative models. However, what was remarkable about the results was that DeepSeek's model achieved comparable results with a much smaller model and much lower training costs, calling in question the models of the competition. In addition, the Chinese AI industry is subjected to sanctions of the United States.

The BuildingsBench datasets consist of: - Buildings-900K: A large-scale dataset of 900K buildings for pretraining models on the task of short-term load forecasting (STLF). Buildings-900K is statistically representative of the entire U.S. building stock. - 7 real residential and commercial building datasets for benchmarking two downstream tasks evaluating generalization: zero-shot STLF and transfer learning for STLF. Buildings-900K can be used for pretraining models on day-ahead STLF for residential and commercial buildings. The specific gap it fills is the lack of large-scale and diverse time series datasets of sufficient size for studying pretraining and finetuning with scalable machine learning models. Buildings-900K consists of synthetically generated energy consumption time series. It is derived from the NREL End-Use Load Profiles (EULP) dataset (see link to this database in the links further below). However, the EULP was not originally developed for the purpose of STLF. Rather, it was developed to "...help electric utilities, grid operators, manufacturers, government entities, and research organizations make critical decisions about prioritizing research and development, utility resource and distribution system planning, and state and local energy planning and regulation." Similar to the EULP, Buildings-900K is a collection of Parquet files and it follows nearly the same Parquet dataset organization as the EULP. As it only contains a single energy consumption time series per building, it is much smaller (~110 GB). BuildingsBench also provides an evaluation benchmark that is a collection of various open source residential and commercial real building energy consumption datasets. The evaluation datasets, which are provided alongside Buildings-900K below, are collections of CSV files which contain annual energy consumption. The size of the evaluation datasets altogether is less than 1GB, and they are listed out below: 1. ElectricityLoadDiagrams20112014 2. Building Data Genome Project-2 3. Individual household electric power consumption (Sceaux) 4. Borealis 5. SMART 6. IDEAL 7. Low Carbon London A README file providing details about how the data is stored and describing the organization of the datasets can be found within each data lake version under BuildingsBench.