China's Foundation Models

Learning Objectives

• Identify the major Chinese AI companies and their leading foundation models
• Explain why DeepSeek's efficiency breakthrough mattered so much to the global AI industry
• Understand the data privacy implications of using Chinese-hosted AI APIs vs. running Chinese open-source models locally

The Data Privacy Warning You Need to Read First

Before exploring Chinese AI models, there is an important distinction you must understand.

Why Chinese AI Models Matter

Until late 2024, the assumption in the global AI industry was that frontier AI required American labs, American chips, American talent, and hundreds of millions of dollars in training compute. DeepSeek changed that assumption overnight.

China has an enormous AI ecosystem built across university research (Tsinghua, Peking University, Shanghai Jiao Tong), tech giants (Alibaba, Baidu, Tencent, ByteDance, Huawei), and well-funded startups. The models coming out of this ecosystem are not derivatives of US models — they are independently developed, often built under significant hardware constraints due to US chip export controls.

The result is a set of models that are competitive with US frontier models on key benchmarks, often with dramatically higher cost efficiency.

The Hardware Story Has Flipped — Inside China

The hardware-constraint framing also needs an update. As of May 2026, NVIDIA CEO Jensen Huang publicly acknowledged that NVIDIA has "largely conceded" the Chinese AI accelerator market — the company's share inside China is now near zero. After more than a year of US export controls and a stalemate where H200 customs clearance letters have sat unprocessed, Beijing's homegrown-stack push has paid off: Alibaba, ByteDance, and Tencent are all running production AI workloads on Huawei's Ascend silicon, and DeepSeek's V4 release in April 2026 was optimized for Ascend before CUDA. Huawei now expects roughly $12 billion in AI accelerator revenue in 2026 — up from $7.5 billion in 2025 — on the strength of those three customers alone. Inside China, the chip-export constraint that motivated DeepSeek's original efficiency push has effectively been replaced by a parallel domestic supply chain.

DeepSeek — The Efficiency Revolution

DeepSeek AI is a research lab founded by the Chinese hedge fund High-Flyer Capital Management, based in Hangzhou. It released a series of models beginning in 2024 that stunned the global AI community.

The DeepSeek Market Shock

On January 27, 2025, DeepSeek R1 triggered the largest single-day market loss in US stock market history. NVIDIA lost $589 billion in market value in a single day — more than double the previous record. The Nasdaq fell 3.1%, the S&P 500 dropped 1.5%, and approximately $1 trillion was wiped from US markets in total.

The trigger: DeepSeek demonstrated ChatGPT-level capabilities at a claimed training cost of $5.6 million, versus hundreds of millions for US competitors. The market question was existential: if frontier AI could be built cheaply, was the massive investment in AI infrastructure justified?

The answer, ultimately, was yes. Markets fully recovered. NVIDIA became the first company to reach a $5 trillion valuation by October 2025. Cheaper AI increased demand for AI compute rather than reducing it. But the DeepSeek shock permanently changed assumptions about the relationship between compute spending and AI capability.

DeepSeek V4 — Current Flagship (April 2026)

On April 24, 2026, DeepSeek released V4-Pro (1.6 trillion total parameters, 49 billion active, mixture-of-experts) and V4-Flash (284 billion total / 13 billion active), both MIT-licensed and shipping with a 1 million-token context window — the first DeepSeek models to match Claude and Gemini on context length. V4-Pro is the largest open-weights model ever released. The accompanying paper claims V4-Pro uses approximately 27% of V3.2's FLOPs and 10% of the KV cache at 1 million-token context — meaningful both for training cost and on-device inference.

API pricing positions V4-Pro and V4-Flash significantly below US frontier rivals:

• V4-Pro: $1.74 / $3.48 per million input/output tokens — undercuts Claude Sonnet and the larger GPT-5.4 tier
• V4-Flash: $0.14 / $0.28 per million input/output tokens — cheapest frontier-adjacent model in the market

Both are available via DeepSeek's API, on Hugging Face for self-hosting, and through third-party providers (Together.ai, Fireworks AI, Groq).

DeepSeek R2 is still pending — rumored at 1.2 trillion mixture-of-experts parameters targeting OpenAI's o-series competition.

First Outside Venture Round — AGI Mandate (May 2026)

DeepSeek's first outside venture round has grown to approximately 70 billion yuan (about $10 billion) at a $45 billion pre-money valuation, Bloomberg reported on May 22, 2026 — more than double the $20 billion valuation circulating weeks earlier. The round is led by Beijing's National Artificial Intelligence Industry Investment Fund, China's state-backed strategic AI vehicle (distinct from the chip-focused "Big Fund"), with Tencent, IDG Capital, and Monolith Capital participating. The state-backed fund is expected to contribute roughly 10 billion yuan, and founder Liang Wenfeng another 20 billion yuan from his own holdings.

Liang told prospective investors that the lab will keep developing open-source models and pursue artificial general intelligence as its core goal, resisting the usual pressure to chase near-term commercialization. Liang retains roughly 90% of the company through High-Flyer and had not previously sought outside capital — the round is framed as a way to offer employee equity and retain talent against intensifying domestic competition.

DeepSeek R1 — The Open-Source Reasoning Breakthrough

DeepSeek R1 was the first open-source reasoning model to match OpenAI's o1 on challenging benchmarks (AIME math, GPQA science).

Reasoning models spend more compute at inference time "thinking through" a problem before responding — similar to how a human might work through a math proof step by step rather than guessing immediately. DeepSeek R1 demonstrated that this "extended thinking" capability — previously exclusive to OpenAI's o1 — could be replicated and open-sourced.

R1-0528 (May 2025) was a major revision that improved logic and programming benchmarks, and added JSON output and function-calling capabilities. Distilled versions (1.5 billion, 7 billion, 14 billion, 32 billion, 70 billion parameters) allow the reasoning capability to run on modest hardware.

Previous Generation — V3.2 and V3.2-Speciale

V3.2 was DeepSeek's flagship before V4 shipped in April 2026. It is still widely deployed and remains available via API for cost-sensitive workloads, but new builds should target V4.

DeepSeek V3.2 is a 671-billion-parameter mixture-of-experts model released under the MIT license. What made it remarkable in early 2025:

• Training cost: Approximately $5.9 million in compute — compared to estimates of $100 million+ for comparable US models
• Performance: Ranked #2 most intelligent open-weight model on the Artificial Analysis benchmark, ahead of Grok 4 and Claude Sonnet 4.5 (Thinking)
• Coding: 40%+ improvement on SWE-bench Verified over V3.1
• MIT license: Completely free for commercial and research use

V3.2-Speciale (December 2025) pushed even further — a high-compute variant that won a gold medal at the 2025 International Mathematical Olympiad (35/42 points), placed 10th at the International Olympiad in Informatics, and scored 96.0% on AIME (vs. GPT-5-High's 94.6%). The Speciale API was only available until December 15, 2025, due to extreme compute costs.

DeepSeek Bans and Restrictions

DeepSeek's success has been accompanied by significant regulatory pushback:

• Italy blocked DeepSeek from app stores (January 2025) over GDPR failures
• Banned on government devices in South Korea, Australia, and Taiwan
• US restrictions: banned by NASA, the US Navy, the Pentagon, and on Texas state government devices; restricted in US House offices

These bans target the hosted API (where data flows to Chinese servers), not the open-source weights that can be run locally.

Alibaba — Qwen Series

Alibaba DAMO Academy has built one of the broadest open-source model portfolios in the world with the Qwen (通义千问, Tongyi Qianwen) series.

Qwen3.5

The Qwen3.5 family (February–March 2026) represents the current generation, spanning from 0.8 billion to 397 billion parameters.

The flagship is Qwen3.5-397 billion-A17 billion — a 397 billion total parameter MoE model with only 17 billion active per forward pass. It uses a novel Gated DeltaNet + MoE architecture that alternates linear and full attention in a 3:1 ratio for efficiency.

Key characteristics:

• 262K native context window, extensible to 1 million tokens
• 100+ languages — broader multilingual coverage than most US models, with particular strength in Chinese, Japanese, Korean, Arabic, and less-resourced languages
• The 9 billion model matches or surpasses GPT-OSS-120 billion (13x its size) on GPQA Diamond and MMMU-Pro benchmarks — exceptional efficiency
• The 122 billion-A10 billion variant scores 72.2 on tool use benchmarks (vs. GPT-5 mini's 55.5)
• Released under Apache 2.0 license

The Qwen3.5 family includes medium models (27 billion, 35 billion, 122 billion) and small models (0.8 billion, 2 billion, 4 billion, 9 billion), covering everything from on-device deployment to large-scale server inference.

QwQ-32 billion

QwQ-32 billion is Alibaba's reasoning-specialized model at 32 billion parameters. It is designed for mathematical reasoning and step-by-step problem solving, competing directly with DeepSeek R1's smaller distilled variants.

Moonshot AI — Kimi

Moonshot AI is a Beijing-based startup founded in 2023, focused on long-context applications.

Kimi K2.5 (January 2026) was a major leap from the earlier K2:

• 1 trillion parameters MoE architecture with 32 billion active per forward pass
• 256K context window (doubled from K2's 128K)
• Trained on 15 trillion mixed visual and text tokens — natively multimodal
• Outperforms Gemini 3 Pro on SWE-Bench Verified and beats GPT 5.2 on SWE-Bench Multilingual
• Beats GPT 5.2 and Claude Opus 4.5 on VideoMMU (video understanding)

Moonshot also released Kimi Code — an open-source coding tool with integrations for terminals, VS Code, Cursor, and Zed.

Kimi K2.6 + $2 Billion Round (May 2026)

On May 7, 2026, Moonshot AI shipped Kimi K2.6 — the company's new flagship — alongside a $2 billion funding round at a $20 billion valuation. The round was led by Meituan's Long-Z Investments arm, with Tsinghua Capital, China Mobile, and CPE Yuanfeng participating. Kimi K2.6 ranks as the second-most-used model on OpenRouter behind only the highest-volume US frontier vendors, and Moonshot reported $200 million in annualized recurring revenue in April 2026 — the kind of consumption disclosure most Chinese labs do not make publicly.

The Moonshot round landed two days after DeepSeek's reported $45 billion first VC round (covered above). Together, the two announcements within 48 hours signal that open-weights labs out of China are emerging as the primary cost-pressure on US frontier vendors — DeepSeek leads on raw scale and government-aligned capital, while Moonshot leads on direct-API consumption volume and OpenRouter ranking.

K2.6 retains K2.5's 1 trillion parameter MoE architecture (32 billion active per token), extends the context window slightly to 262K tokens, adds native INT4 quantization, and introduces an Agent Swarm system that scales to 300 sub-agents and 4,000 coordinated steps in 12-hour autonomous coding sessions. Open-weights landed on Hugging Face on April 20, 2026 under a Modified MIT License; the commercial launch on May 7 was the press moment. See Kimi K2.6 (Moonshot AI) for the full architecture, benchmark table, and Cursor's deliberate K2.5 + RL versus K2.6 generation-skip decision.

Kimi K2.5

Moonshot AI

Open Source

Strengths

1 trillion MoE (32 billion active); 256K context; multimodal; beats GPT 5.2 on SWE-Bench Multilingual and VideoMMU

Context Window

256K tokens

Pricing

Free (self-hosted); API available

Moonshot AI →

Baidu — ERNIE 5.0

Baidu is China's leading search engine company, analogous to Google. Its ERNIE (文心一言, Wenxin Yiyan) series has reached its fifth generation.

ERNIE 5.0 (November 2025, announced at Baidu World 2025) is a fundamental upgrade:

• 2.4 trillion parameters — a unified multimodal model integrating text, image, video, and audio in a single autoregressive framework
• Comparable to Gemini-2.5-Pro and GPT-5-High on 40+ authoritative benchmarks
• Dominant performance on Chinese-language benchmarks
• Real-time search grounding via Baidu's search integration
• Closed source; available via Baidu Cloud API

Baidu is also developing its own AI chips: Kunlunxin M100 (optimized for inference, releasing early 2026) and Kunlunxin M300 (for training/inference of ultra-large models, following in early 2027).

Best for: Applications where Chinese-language fluency is paramount and integration with Chinese-language web search adds value.

ERNIE 5.0

Baidu

Closed

Strengths

2.4 trillion params; unified multimodal (text/image/video/audio); Chinese-language leader; Baidu search integration

Context Window

Not disclosed

Pricing

Baidu Cloud API

Baidu →

Zhipu AI — GLM-5

Zhipu AI is a spinout from Tsinghua University's AI lab. In January 2026, Zhipu made history as the first publicly listed Chinese AI foundation model company, raising approximately $558 million in a Hong Kong IPO.

GLM-5 (February 2026) is a generational leap from GLM-4.5:

• 744 billion total parameters MoE, with 256 experts and 8 activated per token (~44 billion active)
• Uses DeepSeek Sparse Attention (DSA) for efficiency
• 200K context window
• MIT license (fully open source)
• Claims to surpass Gemini 3 Pro on coding and agentic performance
• Trained entirely on Huawei Ascend chips using MindSpore framework — zero NVIDIA dependency

GLM-5 is significant not just for its performance, but because it proves that frontier-class models can be trained without any NVIDIA hardware — a milestone for Chinese AI independence.

GLM-5-Turbo (March 2026) is optimized specifically for automated agent workflows.

GLM 5.2 (June 2026) extends the line with a 1 million token context window and a coding-first focus, rolling out across Zhipu's GLM Coding Plan tiers and pitched as a permissively licensed alternative to Claude Code and GPT-5.5 for the Asia-Pacific market. A standalone API and MIT-licensed open weights are slated to follow within days — though Zhipu released no benchmarks at launch, so its performance claims remain unverified for now.

GLM-5

Zhipu AI

Open Source

Strengths

744 billion MoE (44 billion active); 200K context; MIT license; trained entirely on Huawei Ascend; competitive with Gemini 3 Pro

Context Window

200K tokens

Pricing

Free (self-hosted); API available

Zhipu AI →

ByteDance — Doubao

ByteDance (the company behind TikTok) has quietly built the most-used AI chatbot in China with Doubao (豆包).

Doubao 2.0 / Seed 2.0 (February 2026) comes in four variants — Pro, Lite, Mini, and Code — with the Pro variant delivering:

• 98.3% on AIME 2025 (math), a 3020 Codeforces rating (competitive programming), and 89.5 on VideoMME (video understanding)
• Performance matching GPT 5.2 and Gemini 3 Pro at roughly 1/10th the cost
• Doubao has exceeded 100 million daily active users and 155 million weekly active users

ByteDance's strategy is distinctive: rather than competing for frontier research prestige, it leverages its massive TikTok/Douyin distribution network to put AI in the hands of hundreds of millions of users at extremely low prices.

Doubao 2.0 Pro

ByteDance

Closed

Strengths

98.3% AIME; 100 million+ DAU; GPT-5.2-competitive at 1/10th cost; most-used AI chatbot in China

Context Window

Not disclosed

Pricing

API available; consumer app free

ByteDance →

MiniMax — Consumer AI Powerhouse

MiniMax is a Shanghai-based AI company that IPO'd in Hong Kong in January 2026, raising approximately $620 million — with its stock surging 109% on debut. Backed by Alibaba, Tencent, and Abu Dhabi Investment Authority.

MiniMax M2.7 (March 2026) is its latest model. MiniMax is known for consumer-facing AI products including AI companion apps and creative tools, with over 100 million users.

MiniMax and Zhipu AI were among the first of China's "Six Tigers" — the six leading Chinese AI startups (Zhipu, Moonshot, MiniMax, Baichuan, StepFun, 01.AI) — to go public. Notably, 01.AI stopped pre-training large models in March 2025, pivoting to selling business solutions using DeepSeek's models — a significant strategic retreat that highlighted the brutal economics of competing in foundation models.

MiniMax M2.7

MiniMax

Closed

Strengths

Consumer AI focus; Hong Kong IPO ($620 million raised); 100 million+ users; backed by Alibaba/Tencent

Context Window

Not disclosed

Pricing

API available; consumer apps free

MiniMax →

Ant Group — InclusionAI / Ling Series

Ant Group is the financial-services affiliate of Alibaba (operator of Alipay) and one of the largest fintech companies in the world. Ant's AGI initiative, InclusionAI, sits inside the firm and ships open-weights frontier models alongside its applied financial-AI work — putting Ant in a different posture than DeepSeek (a hedge-fund spin-out) or Alibaba's Qwen team (a hyperscaler model lab).

In early May 2026, InclusionAI published Ling-2.6 — including a 1 trillion parameter flagship variant — on Hugging Face under the MIT license. The architecture is a hybrid of Multi-head Latent Attention and Linear Attention with a 262,144-token context window. Headline benchmark: 72.2 on SWE-bench Verified, among the strongest scores any open-weights model has posted on a coding evaluation. Inference requires tensor parallelism across 8 GPUs. A companion hosted-only sibling, Ring 2.6 at the same trillion-parameter scale, surfaced on OpenRouter at the same time.

Ling-2.6 strengthens the broader pattern from this module: Chinese labs are not just shipping models that compete on benchmarks — they're shipping them under permissive licenses that anyone can self-host and inspect. That's a different distribution strategy from US frontier labs, where flagship weights are closed and only research-grade or older-generation models go open. For developers and security-conscious enterprises that need to run models inside their own perimeter, the open-weights Chinese stack is increasingly the only option at the trillion-parameter scale.

Ling-2.6 (1T variant)

Ant Group

Closed

Strengths

1 trillion parameter open weights under MIT license; 72.2 on SWE-bench Verified; 262,144-token context; hybrid MLA + Linear Attention; from Ant Group's InclusionAI lab

Context Window

262,144 tokens

Pricing

Free open weights; tensor parallelism across 8 GPUs required for inference

Ant Group →

The US Chip Export Controls Context

Understanding Chinese AI development requires understanding the constraint it operates under: US export controls.

Since 2022, the US Bureau of Industry and Security (BIS) has progressively restricted exports of advanced AI chips to China. The original H100 and H200 remain banned. In December 2025, the Trump administration partially reversed course by allowing H200 exports to China with a 25% surcharge (up to 80,000 chips) — better to sell older chips with surcharges than have China build its own. Blackwell-class chips (B100, B200, GB200) and next-generation Rubin chips remain fully restricted for 18–24 months after domestic launch.

China has responded on multiple fronts:

Huawei Ascend chips are rapidly advancing:

• Ascend 910C: 600,000 units planned for 2026 (double 2025 output), delivering approximately 60% of H100 inference performance
• Ascend 920: 6nm process, exceeding 900 teraflops per card
• Ascend 950PR (debuted March 2026): 1.56 petaflops FP4, featuring Huawei's in-house HBM memory and claiming 2.8x H20 performance
• Atlas 950 Supercomputer: 8,192 Ascend 950 processors, 16 exaflops performance
• Huawei's roadmap targets 4 zettaflops FP4 performance by 2028

Beijing's reciprocal talent controls are mirroring US chip export logic in reverse:

• State-secret travel restrictions originally placed on senior DeepSeek researchers have been extended to AI talent at Alibaba and other private firms, with Bloomberg first reporting the expansion in late May 2026
• Some AI professionals working on strategically important projects now require official government approval before traveling abroad
• The policy frames frontier-AI researchers themselves as restricted assets — a national-security treatment of human capital that parallels the US treatment of advanced chips
• Practical implications include constraints on international conference attendance, lab visits, and cross-border collaboration — a soft decoupling at the talent layer that compounds the already-decoupling at the supply layer

The semiconductor gap is real but narrowing. GLM-5's successful training entirely on Ascend hardware proves that Chinese labs can build frontier models without NVIDIA, and the DeepSeek efficiency story demonstrates that algorithmic innovation can partially compensate for hardware constraints. Beijing's reciprocal talent controls compound the same logic at the human-capital layer — what started as a one-way US export-control regime is hardening into a two-way decoupling that binds frontier-AI talent on both sides.

Key Takeaways

• Chinese foundation models are genuinely competitive — not derivative of US models, independently built under hardware constraints
• DeepSeek V4-Pro and V4-Flash are the current flagships (April 24, 2026, MIT-licensed, 1 million-token context); V4-Pro is the largest open-weights model ever released at 1.6 trillion total / 49 billion active mixture-of-experts. By May 22, 2026, DeepSeek's first outside venture round had grown to roughly $10 billion (70 billion yuan) at a $45 billion pre-money valuation — led by Beijing's National AI Industry Investment Fund with Tencent, IDG Capital, and Monolith Capital — with founder Liang Wenfeng publicly committing the lab to open-source AGI as its core goal
• DeepSeek V3.2 (MIT license, previous generation) was the model that first matched frontier performance and was trained for a fraction of comparable US model costs — its January 2025 release triggered a $589 billion single-day NVIDIA crash that permanently changed AI economics assumptions
• Data privacy is a critical distinction: Chinese-hosted APIs transmit data to Chinese servers subject to PRC law; running open-source weights locally eliminates this concern. Multiple countries have banned DeepSeek's hosted API on government devices
• The Qwen3.5 family (1 million context, 100+ languages), Kimi K2.5 (1 trillion params), ERNIE 5.0 (2.4 trillion multimodal), and GLM-5 (744 billion, Ascend-only training) represent rapid advancement across the Chinese AI ecosystem
• Moonshot AI shipped Kimi K2.6 + a $2 billion raise at a $20 billion valuation on May 7, 2026 — second-most-used model on OpenRouter, $200 million annualized recurring revenue. Combined with DeepSeek's $45 billion round 48 hours earlier, the two May 2026 mega-rounds signal China's open-weights labs are now the primary cost-pressure on US frontier API pricing
• ByteDance Doubao is China's most-used AI chatbot with 100 million+ DAU, while MiniMax and Zhipu AI have IPO'd in Hong Kong
• Huawei Ascend chips are advancing rapidly toward NVIDIA parity, with a roadmap to 4 zettaflops by 2028
• Beijing has extended state-secret travel restrictions originally placed on senior DeepSeek researchers to AI talent at Alibaba and other private firms (Bloomberg, late May 2026), requiring official approval before international travel — a reciprocal-controls signal that mirrors US chip export logic in reverse and hardens a two-way decoupling at the human-capital layer