Goldman Sachs Report Analyzes China's AI Large Model Competitive Landscape: Who Will Be the Long-Term Winner?

Author: Wall Street Watch, Bu Shuqing

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

China's AI large models are at a historic turning point. Goldman Sachs believes that the intelligent performance of China's open-source/open-weight large models is approaching that of the world's top proprietary models, and the adoption scale by domestic companies and global SMEs is rapidly expanding. The resulting data flywheel effect will further drive model iteration and upgrades.

According to the 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 Zhizhu GLM's model intelligence moment this year." The team led by Goldman Sachs analyst Ronald Keung conducts a systematic assessment in this 50-page report around four core issues: how Chinese AI models achieve high performance at low cost, why they choose the open-source route and how to monetize, where the core addressable market is, and who will be the long-term winner.

In terms of competitive landscape judgment, Goldman Sachs has introduced a "competitive positioning framework" based on pricing power, cost advantages, and financial strength. Accordingly, it identifies that in the field of foundational text models, Zhizhu (first coverage) and DeepSeek (unlisted) are positioned the strongest; in the multimodal field, ByteDance (unlisted) leads. Goldman Sachs also maintains a buy rating on MiniMax and Kuaishou.

Small Investment, Big Returns, Efficiency Wins

Chinese large models can achieve comparable performance at a cost far lower than similar products in the United States, primarily due to breakthroughs in architecture innovation and parameter efficiency.

Goldman Sachs' report points out that the parameter scale of China's open-source models generally ranges from 200 billion to 1.6 trillion, which is only 2% to 10% of the world's top models. This is mainly due to limited access to high-end computing power. Meanwhile, innovations such as the mixture of experts architecture (MoE) and sparse attention mechanisms have resulted in the actual activated parameters accounting for only 3% to 5% of the total parameters, significantly reducing training and inference costs.

At the model level, DeepSeek V4 Pro has a parameter count of 1.6 trillion, Zhizhu GLM5.2 has 0.7 trillion, and MiniMax M3 has 0.4 trillion.

Goldman Sachs attributes the recent leap in programming 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, which has been deployed in the online services of V4-Flash and V4 Pro, enhancing user generation speed by 60% to 85% (V4-Flash) and 57% to 78% (V4 Pro) without changing model weights or output quality.

Meituan's LongCat 2.0, released on June 30, is seen by Goldman Sachs as an important milestone in the localization of China's AI infrastructure—this is the first open-source MoE model with 1.6 trillion parameters trained and deployed entirely based on 50,000 domestic computing cards. Goldman Sachs believes this demonstrates the feasibility of a localized hardware stack during the compute-intensive pre-training phase, which has far-reaching significance for Chinese AI models to break free from reliance on foreign high-end chips.

Market Polarization, Stronger Stronger

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

In the high-end market, top models represented by Zhizhu GLM5.2 and Alibaba Qwen3.7 Max are priced at about $1 per million tokens, five times that of low-end models, with a gross margin of about 10% to 20% (Goldman Sachs estimates). In contrast, top models in the United States are priced at $4 to $8 per million tokens, while Chinese high-end models are only 10% to 25% of that, yet they can still maintain positive gross margins due to lower parameter activation ratios.

In the low-end market, models aimed at intelligent tasks are priced as low as $0.06 to $0.2 per million tokens, targeting price-sensitive global SMEs and individual users. MiniMax derives 60% to 70% of its revenue from overseas. Notably, DeepSeek has announced that starting mid-July, it will introduce a peak and off-peak pricing mechanism for the V4 series, with peak rates being twice that of off-peak, and mixed pricing at about $0.35 per million tokens (V4 Pro) and $0.12 (V4 Flash).

Goldman Sachs predicts that the API and subscription revenue of Chinese AI models will grow from an estimated 35 billion RMB in 2026 to 879 billion RMB in 2030, corresponding to a daily token consumption increase from 350 trillion to 4,600 trillion, an increase of about 25 times.

Open Source Strategy: Broad Penetration, Monetization Path Needs Upgrading

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 advantage of the open-source strategy lies in deployment flexibility and community ecology. Alibaba's Qwen series, DeepSeek, Zhizhu GLM, and MiniMax M3 all adopt open-source or open-weight methods, with ByteDance's Seed model being a major exception, adopting a fully closed-source proprietary route. The open-source model allows for flexible deployment of models both inside and outside mainland China and accelerates iteration through community feedback.

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

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 must sign a revenue-sharing agreement with the model company. The MiniMax M series has already adopted this model. Goldman Sachs believes this shift will significantly improve the unit economics of AI model companies, as they can benefit from revenue-sharing agreements with platforms like AWS Bedrock and Alibaba Cloud Bailian without bearing the inference computing costs themselves.

From "Token Maximization" to ROI Priority

Goldman Sachs characterizes the international market expansion as the most important upward space for Chinese AI models, especially in non-U.S. markets.

Goldman Sachs' U.S. research team estimates that by 2030, intelligent AI will drive global token consumption to grow 24 times, reaching 120 trillion tokens per month, with enterprise intelligent agents contributing a 55-fold increase and consumer intelligent agents contributing a 12-fold increase. In global markets (excluding China), Chinese AI models have achieved significant token share growth due to performance improvements and price advantages.

Goldman Sachs' report points out that the AI usage paradigm among global enterprises 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 intelligent agent counts, backend process automation, and actual output. A Jellyfish AI engineering trend study shows that heavy AI users in enterprises consumed 10 times the tokens but only doubled their output.

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

Who Will Be the Long-Term Winner?

Goldman Sachs has constructed a three-dimensional competitive positioning framework to quantitatively assess the long-term winning probability of each player, with the core formula being: ARR scale × gross margin advantage + financial strength.

The pricing power dimension examines the speed of listing (compared to previous and similar models), LMArena arena scores (based on large-scale blind user evaluations), and the mixed pricing level per million tokens.

The cost advantage dimension assesses throughput (tokens per second), cache hit rate, parameter activation ratio, and gross margin on inference. The financial strength dimension evaluates cash on hand, net cash as a percentage of total assets, and valuation multiples.

In the foundational text model field, Goldman Sachs identifies Zhizhu (first coverage, neutral rating, target valuation of $110 billion) and DeepSeek (unlisted) as the strongest positions, both performing outstandingly in pricing power and cost advantages. The overall implied valuation of independent AI model companies exceeds $200 billion.

In the multimodal/video generation field, ByteDance leads with Seedance, with reports from LatePost and 36Kr indicating a gross margin as high as 70%, and an ARR operating rate exceeding $2 billion. Kuaishou's Keling and MiniMax Hailuo/upcoming H3 model are also viewed positively by Goldman Sachs, expected to benefit in the second half of 2026 from functional breakthroughs in video generation and LLM integration, as well as healthy pricing due to supply tightness.

Goldman Sachs maintains a buy rating on MiniMax, with a target price of HKD 860, reasoning that its M3 model is positioned in the ARR maximization quadrant with high token volume and attractive pricing, and its current valuation is only 13 times the ARR by the end of 2026, indicating a significant discount compared to the valuation multiples of similar companies in China and globally, with a risk-reward ratio skewed upward.

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