Goldman Sachs In-Depth Report: Who Will Be the Long-Term Winners in China's AI Large Model Industry?

marsbitPublished on 2026-07-10Last updated on 2026-07-10

Abstract

Goldman Sachs Report: China's AI Models at an Inflection Point China's open-source/open-weight large language models (LLMs) have reached performance parity with top global proprietary models, according to a Goldman Sachs report. This is driven by architectural innovations and higher parameter efficiency, allowing Chinese models to achieve comparable capabilities at 2%-10% the parameter size and significantly lower cost. The market is evolving into a two-tiered structure: a high-end segment (e.g., GLM5.2, Qwen3.7 Max) with premium pricing and a low-end, price-sensitive segment for global SMEs and individual users. Key points: * **Cost & Performance:** Innovations like Mixture of Experts (MoE) enable high performance with smaller models. Projects like Meituan's LongCat 2.0, trained on domestic hardware, highlight progress in tech self-sufficiency. * **Open-Source Strategy:** Most Chinese players use open-source/open-weight models for flexibility and ecosystem growth. However, Goldman notes this may underreport actual deployment and revenue. A shift toward "open-weight + community license" models with revenue sharing (e.g., MiniMax) could improve monetization. * **Market Shift & Global Expansion:** Enterprise AI adoption is shifting from "token maximization" to "ROI-first." International expansion, especially in non-US markets, is a major growth driver. Chinese models are increasingly available on global platforms like AWS Bedrock and Microsoft Copilot. * **Competitive...

Author: Wall Street News

China's AI large models are standing at a historic inflection point. Goldman Sachs believes that the intelligence performance of China's open-source/open-weight large models is approaching the world's top proprietary models, with rapid adoption by domestic enterprises and global SMEs, thereby creating a data flywheel effect that will further drive model iteration and upgrades.

According to Chasing Wind Trading Desk, Goldman Sachs' latest report points out that this evolutionary trajectory can be summarized as 'from DeepSeek's cost-efficiency moment last year to Zhipu GLM's model intelligence moment this year.' A team led by Goldman Sachs analyst Ronald Keung systematically evaluates four core questions in this 50-page report: how Chinese AI models achieve high performance at low cost, why the open-source path is chosen and how to monetize it, where the core addressable market lies, and who will be the long-term winners.

In assessing the competitive landscape, Goldman Sachs introduced a "competitive positioning framework" based on pricing power, cost advantage, and financial strength, and concluded accordingly that in the foundational text model field, Zhipu (initiating coverage) and DeepSeek (unlisted) have the strongest positioning; in the multimodal field, ByteDance (unlisted) leads. Goldman Sachs simultaneously maintains its Buy ratings on MiniMax and Kuaishou.

Achieving More with Less, Efficiency Wins

China's large models can achieve performance close to their US counterparts at a far lower cost, with the core lying in the dual breakthrough of architectural innovation and parameter efficiency.

The Goldman Sachs report points out that the parameter scale of Chinese open-source models generally ranges from 200 billion to 1.6 trillion, only 2% to 10% of the world's top models, mainly due to restricted access to high-end computing power. Meanwhile, innovations like the Mixture of Experts (MoE) architecture and sparse attention mechanisms have reduced the actual activated parameters to only 3% to 5% of the total parameters, significantly lowering training and inference costs.

At the specific model level, DeepSeek V4 Pro has 1.6 trillion parameters, Zhipu GLM5.2 has 0.7 trillion, and MiniMax M3 has 0.4 trillion.

Goldman Sachs attributes the recent leap in coding capabilities of Chinese models to the synergistic effects of data filtering, reinforcement learning post-training, and other factors. On June 27, DeepSeek launched the speculative decoding framework DSpark, already deployed in the online services of V4-Flash and V4 Pro, which increases generation speed per user by 60% to 85% (V4-Flash) and 57% to 78% (V4 Pro) without changing model weights or output quality.

Meituan's release of LongCat 2.0 on June 30 is regarded by Goldman Sachs as a significant milestone in the self-sufficiency of China's AI infrastructure—it's China's first open-source 1.6 trillion parameter MoE model fully trained and deployed based on 50,000 domestic compute cards. Goldman Sachs believes this proves the feasibility of a localized hardware stack during the compute-intensive pre-training phase, holding profound significance for China's AI models to reduce reliance on foreign high-end chips.

A Bifurcating Market, the Strong Get Stronger

Goldman Sachs describes the Chinese AI model market as forming a "two-tier structure" and identifies two ARR-maximizing quadrants.

In the high-end market, top models represented by Zhipu GLM5.2 and Alibaba's Qwen3.7 Max are priced at about $1 per million tokens, which is 5 times that of low-end models, with estimated inference gross margins around 10% to 20%. In comparison, US top models are priced at $4 to $8 per million tokens; Chinese high-end models are only 10% to 25% of that price, yet can maintain positive gross margins thanks to a lower parameter activation ratio.

In the low-end market, models oriented for agent tasks are priced as low as $0.06 to $0.2 per million tokens, opening up markets among price-sensitive global SMEs and individual users. MiniMax derives 60% to 70% of its revenue from overseas. Notably, DeepSeek has announced the introduction of peak/off-peak pricing for the V4 series from mid-July, with peak rates twice the off-peak rate, with a blended pricing of about $0.35 (V4 Pro) and $0.12 (V4 Flash) per million tokens.

Goldman Sachs forecasts that Chinese AI model API and subscription revenue will grow from an estimated 35 billion yuan in 2026 to 879 billion yuan in 2030, corresponding to daily token consumption increasing from 35 trillion to 460 trillion, an approximately 25-fold increase.

Open-Source Strategy: Widespread Penetration, Monetization Path Awaits Upgrade

The Goldman Sachs report details the strategic logic behind the widespread adoption of open-source/open-weight routes by Chinese AI models and their monetization limitations.

The core advantages of the open-source strategy lie in deployment flexibility and community ecosystem. Alibaba's Qwen series, DeepSeek, Zhipu's GLM, and MiniMax's M3 all adopt open-source or open-weight methods, with ByteDance's Seed model being a major exception, following a fully closed-source proprietary route. The open-source model allows flexible deployment both within and outside mainland China and accelerates iteration through community feedback.

However, Goldman Sachs points out that the ARR numbers disclosed by open-source model companies likely significantly underestimate the actual deployment scale and revenue potential. Taking Zhipu as an example, its ARR target for the end of 2026 is $1 billion, but the actual global deployment volume of GLM5.2 will far exceed the token volume and revenue through Zhipu's own API channels—Alibaba Cloud's Bailian MaaS platform can directly host the open-source GLM5.2 model without paying Zhipu any fees.

Goldman Sachs expects the industry to gradually shift from pure open-source (MIT license, completely free) to an "open-weight + community license" model—where commercial use requires signing a revenue-sharing agreement with the model company. MiniMax's M series has pioneered this model. Goldman Sachs believes this transition will significantly improve the unit economics for AI model companies, as they can benefit from revenue-sharing agreements with platforms like AWS Bedrock and Alibaba Cloud Bailian without bearing the inference compute costs themselves.

From 'Token Maximization' to ROI Priority

Goldman Sachs characterizes international market expansion as the most important upside for Chinese AI models, especially in non-US markets.

Goldman Sachs' US research team estimates that by 2030, agent AI will drive a 24-fold increase in global token consumption to 12 quintillion tokens per month, with enterprise agents contributing a 55-fold growth and consumer agents contributing a 12-fold growth. In global (excluding China) markets, Chinese AI models have already achieved significant token share growth due to performance improvements and price advantages.

The Goldman Sachs report notes that the global enterprise AI usage paradigm is undergoing a fundamental shift from 'token maximization' to 'ROI priority'. The former prevailed from late 2025 to early 2026, where enterprises equated high token consumption with organizational productivity; the latter focuses more on clear task boundaries, daily active agent count, backend process automation, and actual output. Data from a Jellyfish AI engineering trends study shows that heavy AI users in enterprises consume 10 times the tokens but only increase output by 2 times.

At the channel level, Alphabet's Gemini Enterprise Agent Platform and Amazon's AWS Bedrock already offer hosting services for Chinese AI models like DeepSeek, MiniMax, Moonshot, GLM, and Qwen. According to the Wall Street Journal, Microsoft's CEO recently stated that Microsoft is considering hosting a version of DeepSeek in Copilot as an optional low-cost model, emphasizing that if DeepSeek is hosted, the model would run within Microsoft's cloud ecosystem, ensuring customer data remains within Azure.

Who Are the Long-Term Winners?

Goldman Sachs constructed a three-dimensional competitive positioning framework to evaluate each player's long-term winning probability with quantitative metrics, with the core formula being: ARR scale × gross margin advantage + financial strength.

The Pricing Power dimension examines release speed (compared to previous generation and same-level models), LMArena Arena score (based on large-scale blind user evaluations), and blended pricing level per million tokens.

The Cost Advantage dimension examines throughput (tokens per second), cache hit rate, parameter activation ratio, and inference gross margin. The Financial Strength dimension examines cash on hand, net cash as a percentage of total assets, and valuation multiples.

In the field of foundational text models, Goldman Sachs identifies Zhipu (initiating coverage, Neutral rating, target valuation $110 billion) and DeepSeek (unlisted) as having the strongest positioning, with both excelling in pricing power and cost advantage. The overall implied valuation of independent AI model companies exceeds $200 billion.

In the field of multimodal/video generation, ByteDance leads with Seedance. According to LatePost and 36Kr reports, Seedance has a gross margin as high as 70%, with an ARR run rate already exceeding $2 billion. Kuaishou's Kling and MiniMax's Hailuo/upcoming H3 models are also favored by Goldman Sachs, expected to benefit from functional breakthroughs in video generation and LLM integration and healthy pricing due to tight supply in the second half of 2026.

Goldman Sachs maintains a Buy rating on MiniMax with a target price of HK$860, citing its M3 model being in the ARR-maximizing quadrant of high token volume and attractive pricing, and its current valuation being only 13 times its ARR at the end of 2026, showing a significant discount compared to the valuation multiples of Chinese and global peers, with risk-reward skewed to the upside.

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Related Questions

QAccording to the Goldman Sachs report, which Chinese companies are identified as the strongest players in the foundational text model space?

AAccording to the Goldman Sachs report, Zhipu (首次覆盖) and DeepSeek (未上市) are identified as the strongest players in the foundational text model space, excelling in both pricing power and cost advantage.

QWhat key architectural and efficiency innovations have enabled Chinese AI models to achieve performance close to top global models at significantly lower costs?

AKey innovations enabling Chinese models' cost efficiency include architectural advances like Mixture-of-Experts (MoE) and sparse attention mechanisms. These innovations reduce the percentage of activated parameters during training and inference to only 3% to 5% of the total parameters, dramatically lowering costs. Chinese models also typically use smaller total parameter sizes (2% to 10% of top global models) due to constraints on accessing high-end compute.

QHow does Goldman Sachs describe the current market structure for Chinese AI models and the key characteristics of its high-end and low-end segments?

AGoldman Sachs describes the Chinese AI model market as forming a "two-tiered structure." The high-end market includes top models like Zhipu GLM5.2 and Alibaba Qwen3.7 Max, priced around $1 per million tokens (about 10-25% of US top models), with estimated inference gross margins of 10-20%. The low-end market targets price-sensitive global SMEs and individuals, with models priced as low as $0.06 to $0.2 per million tokens.

QWhat challenges and evolution paths does Goldman Sachs highlight for the open-source/ open-weight business models adopted by many Chinese AI model companies?

AGoldman Sachs notes that open-source models' reported ARR likely significantly underestimates actual deployment scale and revenue potential, as models can be hosted on third-party platforms (e.g., Alibaba Cloud Bailian) without direct payments to the model creator. The industry is expected to evolve from pure open-source (MIT license, free) towards an "open-weight + community license" model, where commercial use requires a revenue-sharing agreement. This shift, pioneered by MiniMax's M-series, is predicted to significantly improve unit economics for model companies.

QWhat is the three-dimensional competitive positioning framework used by Goldman Sachs to assess long-term winners, and which company leads in the multimodal/video generation space according to it?

AGoldman Sachs uses a three-dimensional framework evaluating: 1) Pricing Power (release speed, LMArena scores, pricing level), 2) Cost Advantage (throughput, cache hit rate, parameter activation ratio, inference gross margin), and 3) Financial Strength (cash on hand, net cash to assets ratio, valuation multiples). According to this framework, ByteDance (未上市) leads in the multimodal/video generation space with its Seedance model, reportedly boasting 70% gross margins and an ARR run-rate exceeding $2 billion.

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What is AGENT S

Agent S: The Future of Autonomous Interaction in Web3 Introduction In the ever-evolving landscape of Web3 and cryptocurrency, innovations are constantly redefining how individuals interact with digital platforms. One such pioneering project, Agent S, promises to revolutionise human-computer interaction through its open agentic framework. By paving the way for autonomous interactions, Agent S aims to simplify complex tasks, offering transformative applications in artificial intelligence (AI). This detailed exploration will delve into the project's intricacies, its unique features, and the implications for the cryptocurrency domain. What is Agent S? Agent S stands as a groundbreaking open agentic framework, specifically designed to tackle three fundamental challenges in the automation of computer tasks: Acquiring Domain-Specific Knowledge: The framework intelligently learns from various external knowledge sources and internal experiences. This dual approach empowers it to build a rich repository of domain-specific knowledge, enhancing its performance in task execution. Planning Over Long Task Horizons: Agent S employs experience-augmented hierarchical planning, a strategic approach that facilitates efficient breakdown and execution of intricate tasks. This feature significantly enhances its ability to manage multiple subtasks efficiently and effectively. Handling Dynamic, Non-Uniform Interfaces: The project introduces the Agent-Computer Interface (ACI), an innovative solution that enhances the interaction between agents and users. Utilizing Multimodal Large Language Models (MLLMs), Agent S can navigate and manipulate diverse graphical user interfaces seamlessly. Through these pioneering features, Agent S provides a robust framework that addresses the complexities involved in automating human interaction with machines, setting the stage for myriad applications in AI and beyond. Who is the Creator of Agent S? While the concept of Agent S is fundamentally innovative, specific information about its creator remains elusive. The creator is currently unknown, which highlights either the nascent stage of the project or the strategic choice to keep founding members under wraps. Regardless of anonymity, the focus remains on the framework's capabilities and potential. Who are the Investors of Agent S? As Agent S is relatively new in the cryptographic ecosystem, detailed information regarding its investors and financial backers is not explicitly documented. The lack of publicly available insights into the investment foundations or organisations supporting the project raises questions about its funding structure and development roadmap. Understanding the backing is crucial for gauging the project's sustainability and potential market impact. How Does Agent S Work? At the core of Agent S lies cutting-edge technology that enables it to function effectively in diverse settings. Its operational model is built around several key features: Human-like Computer Interaction: The framework offers advanced AI planning, striving to make interactions with computers more intuitive. By mimicking human behaviour in tasks execution, it promises to elevate user experiences. Narrative Memory: Employed to leverage high-level experiences, Agent S utilises narrative memory to keep track of task histories, thereby enhancing its decision-making processes. Episodic Memory: This feature provides users with step-by-step guidance, allowing the framework to offer contextual support as tasks unfold. Support for OpenACI: With the ability to run locally, Agent S allows users to maintain control over their interactions and workflows, aligning with the decentralised ethos of Web3. Easy Integration with External APIs: Its versatility and compatibility with various AI platforms ensure that Agent S can fit seamlessly into existing technological ecosystems, making it an appealing choice for developers and organisations. These functionalities collectively contribute to Agent S's unique position within the crypto space, as it automates complex, multi-step tasks with minimal human intervention. As the project evolves, its potential applications in Web3 could redefine how digital interactions unfold. Timeline of Agent S The development and milestones of Agent S can be encapsulated in a timeline that highlights its significant events: September 27, 2024: The concept of Agent S was launched in a comprehensive research paper titled “An Open Agentic Framework that Uses Computers Like a Human,” showcasing the groundwork for the project. October 10, 2024: The research paper was made publicly available on arXiv, offering an in-depth exploration of the framework and its performance evaluation based on the OSWorld benchmark. October 12, 2024: A video presentation was released, providing a visual insight into the capabilities and features of Agent S, further engaging potential users and investors. These markers in the timeline not only illustrate the progress of Agent S but also indicate its commitment to transparency and community engagement. Key Points About Agent S As the Agent S framework continues to evolve, several key attributes stand out, underscoring its innovative nature and potential: Innovative Framework: Designed to provide an intuitive use of computers akin to human interaction, Agent S brings a novel approach to task automation. Autonomous Interaction: The ability to interact autonomously with computers through GUI signifies a leap towards more intelligent and efficient computing solutions. Complex Task Automation: With its robust methodology, it can automate complex, multi-step tasks, making processes faster and less error-prone. Continuous Improvement: The learning mechanisms enable Agent S to improve from past experiences, continually enhancing its performance and efficacy. Versatility: Its adaptability across different operating environments like OSWorld and WindowsAgentArena ensures that it can serve a broad range of applications. As Agent S positions itself in the Web3 and crypto landscape, its potential to enhance interaction capabilities and automate processes signifies a significant advancement in AI technologies. Through its innovative framework, Agent S exemplifies the future of digital interactions, promising a more seamless and efficient experience for users across various industries. Conclusion Agent S represents a bold leap forward in the marriage of AI and Web3, with the capacity to redefine how we interact with technology. While still in its early stages, the possibilities for its application are vast and compelling. Through its comprehensive framework addressing critical challenges, Agent S aims to bring autonomous interactions to the forefront of the digital experience. As we move deeper into the realms of cryptocurrency and decentralisation, projects like Agent S will undoubtedly play a crucial role in shaping the future of technology and human-computer collaboration.

773 Total ViewsPublished 2025.01.14Updated 2025.01.14

What is AGENT S

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