Best Chinese AI Models (2026): DeepSeek, Qwen, GLM, Kimi

Chinese AI models went from a single viral release to a crowded, credible field in under two years. If you are trying to make sense of DeepSeek, Qwen, GLM, Kimi, and the rest, the noise is the real obstacle: most coverage is either breathless or dismissive, and almost none of it is current, because the labs ship new versions every few weeks. This is a straight comparison of the major Chinese AI models as they stand in November 2026: who makes them, how good they actually are, whether they are safe to use, and what their rise means if you care about being found in AI answers.

The Major Chinese AI Models at a Glance

A year ago, "Chinese AI model" mostly meant DeepSeek. In November 2026 it means a crowded field of labs shipping frontier-grade models, most of them with downloadable open weights, and most of them far cheaper than the closed American systems. The US has since answered with its own open models, Thinking Machines' Inkling, Meta's Apache-licensed Muse Glimmer and, since October 15, its larger Muse Spark Open, but the field below is the one they are chasing. Here is the current lineup, with the version that is actually live as of this writing.

Prices move almost weekly and depend on tier, so treat them as orders of magnitude rather than quotes. The pattern that holds: the strongest Chinese models are cheaper per token than the closed American flagships, and most of them you can download and run yourself.

DeepSeek, Qwen, GLM, and Kimi: The Big Four

Four labs do most of the heavy lifting in this conversation. They are the ones whose names show up in AI Overviews, the ones Western startups quietly build on, and the ones you are actually choosing between when you ask which Chinese model to use.

DeepSeek (V4): The Cost Disruptor

DeepSeek is the lab that started the panic. Its R1 reasoning model in January 2025 matched American frontier systems at a tiny fraction of the cost, became the most-downloaded free app in the US App Store within days, and helped trigger the January 27, 2025 selloff in which Nvidia alone lost about $589 billion in market value in a single day. The lab sits in Hangzhou and is funded by the quantitative hedge fund High-Flyer, which is part of why it optimizes so aggressively for cost. Our DeepSeek pricing guide has the current V4.1 Flash and V4 Pro rates in full.

DeepSeek's current flagship is V4, which TechCrunch reported was previewed in late April 2026, built on the same mixture-of-experts efficiency the lab is known for; its flagship V4-Pro reached general availability on August 13, 2026, and V4.1 Flash replaced the original Flash model on September 10 (updated to V4.1-Flash-1017 on October 17). It is genuinely good at reasoning and agentic coding, and on price it is close to unbeatable: the Flash tier costs fifteen cents per million input tokens off-peak, where Western frontier models charge dollars. On August 16, 2026, DeepSeek replaced its old flat all-day rate with a peak/off-peak split that doubles the price during seven hours on weekdays (peak hours 01:00-04:00 and 06:00-10:00 UTC), but even at peak it stays a fraction of Western pricing. If your question is "which Chinese model has among the lowest per-token prices," DeepSeek is an answer (Z.ai's GLM-5.3-Flash is level on input at $0.15 and cheaper on output at $0.50), and it is where most DeepSeek-versus-Qwen comparisons land on cost alone.

Alibaba Qwen (Qwen3.8-Max): The Default Base Model

If DeepSeek grabbed the headlines, Qwen quietly became the foundation. Alibaba's Qwen family is the most-downloaded open model line in the world: MIT Technology Review reports it accounted for more than 30 percent of all Hugging Face model downloads in 2024, and by August 2025 Qwen derivatives made up over 40 percent of new language-model variants on Hugging Face, versus about 15 percent for Meta's Llama. Alibaba ships new Qwen models at a relentless pace, several a month.

The practical takeaway is that when a developer anywhere fine-tunes "an open model," it is very often a Qwen underneath. The smaller Qwen models ship with open weights under permissive licenses, which is what makes them the default starting point (the 180B Qwen3.8-Flash-Next preview uses a new Qwen Community License). The catch: Alibaba long treated its very largest models as a closed API rather than an open download, and only partly reversed that in August 2026. Qwen's strengths are breadth: strong multilingual coverage, agentic tool use, and a one-million-token context window. The current flagship, Qwen3.8-Max, was released on August 3, 2026 at 2.4 trillion parameters with 95 billion active, priced at $2 per million input tokens and $6 per million output. Alibaba published its weights on August 12, 2026, the first open Max-tier Qwen, though as a text-only variant under a bespoke "Qwen3.8-Max" license rather than Apache 2.0.

Zhipu GLM (GLM-5.3): The Frontier Challenger

Zhipu's GLM line closed the gap, and its current flagship GLM-5.3 launched August 14, 2026. It comes from Zhipu AI, a Beijing lab spun out of Tsinghua University that operates internationally as Z.ai, and it launched on August 14, 2026 with a one-million-token context window, keeping the same 753B/40B base as GLM-5.2 (all the gains come from post-training). Unlike GLM-5.2's clean MIT terms, GLM-5.3 ships under a custom license: model-as-a-service operators with more than $10B in aggregate revenue over any 12 months must pass a Z.ai security review. On Artificial Analysis's Intelligence Index v4.3.2 (October 2026, before the October 27 move to v5) it scored 44.8, the #3 open-weight model behind Xiaomi's MiMo-V2.6-Pro (46.3) and DeepSeek's V4.1 Flash (45.1), with StepFun's Step 5 Preview #4 (43.7) and Kimi K3 #5 (43.6), and Reuters reported in June that GLM-5.2, the same base model, narrowly trailed the leading closed models at roughly a sixth of the cost. Z.ai went public in Hong Kong in January 2026.

What sets GLM apart from DeepSeek is focus. Where DeepSeek optimizes for raw cost, GLM-5.3 is tuned for long-horizon software engineering: full-codebase debugging, autonomous self-correction, and the kind of multi-step agent runs that fall apart on weaker models. If your work is serious coding or agent orchestration and you want open weights, this is the current pick.

Moonshot Kimi (K3.1 Pro and K2.7-Code): The Agent Specialist

Kimi, from Moonshot AI, is built for agents that run for hours, and its current lineup splits the job by cost. Kimi K3.1 Pro, launched October 23, 2026, is the flagship: Moonshot describes it as a post-trained version of Kimi K3 with much more post-training compute, and it keeps the one-million-token context window. Moonshot said the full weights would be released by November 4, 2026, and as of November 6, 2026 they are on Hugging Face under a custom Kimi K3.1 Pro License, so the downloadable Kimi flagship is now K3.1 Pro; K3, launched July 16, 2026 at 2.8 trillion parameters, had its open weights published July 27, 2026 under a bespoke Kimi K3 License. K2.7-Code, a coding-specialized variant Moonshot shipped in mid-June 2026, is the everyday workhorse: it still ships open weights under the K2 line's modified MIT license and the same agent-swarm orchestration Moonshot is known for, coordinating many sub-agents across long background tasks rather than answering one prompt at a time. K3.1 Pro lists at $2 per million input tokens and $10 per million output, a third below K3's $3 and $15, and its output price runs 2.5 times K2.7-Code's ($4). The practical split for existing K2 users is K2.7-Code for routine coding and agent work and K3.1 Pro for the sessions that specifically need the extra scale, reasoning, or the full context window; our Kimi K3 vs Claude comparison weighs K3 against the Claude lineup task by task. The prior-generation K2.6 is still live on the API at the same $0.95/$4.00 rate as K2.7-Code, but Moonshot's pricing page, checked November 2, 2026, says K2 series models will no longer be available to newly registered users from October 31, 2026 (existing users are not affected) and that it plans to retire them by the end of December 2026. On the consumer side Moonshot sells subscription plans metered in credits rather than messages, which we break down in our Kimi pricing guide.

In a head-to-head, comparing Kimi with DeepSeek, or Qwen with Kimi, usually comes down to job shape. DeepSeek is among the cheapest generalists, Qwen is the most adaptable base, GLM is the coding frontier, and Kimi is the one you reach for when an agent needs to keep its footing across a very long, multi-step run.

The Next Tier: MiniMax, Tencent Hunyuan, Xiaomi, StepFun, Baidu Ernie, and ByteDance Doubao

Six more labs round out the field and show up constantly in any serious comparison.

MiniMax launched M3 on June 1, 2026 and published its open weights on Hugging Face on June 12. Its hook is architectural: a sparse-attention design that handles a one-million-token context window at far lower compute cost than a standard transformer, which lets it stay cheap while going long. It went public in Hong Kong the same week as Z.ai. On October 19, 2026 MiniMax added M3.1 Flash at $0.15 input and $0.60 output per million tokens, and published its open weights on Hugging Face under the same MiniMax Community License (repository first committed October 23, 2026).

Tencent Hunyuan has been the field's quiet permissive-license play. Its current flagship, Hy4, launched on November 4, 2026 with image understanding and computer use, at the same $0.834 list input price; Tencent says the final weights will follow shortly. The Hy4 Preview it replaces shipped open weights on August 28, 2026 at 770 billion total parameters (49 billion active) with a one-million-token context window, and unlike GLM-5.3 and Kimi K3, which both carry custom licenses with revenue or usage conditions, it ships under a plain Apache 2.0 license. On Tencent's own blind-eval benchmark it edges out GLM-5.3 and Kimi K3. Its predecessor, the full Hy3 released July 6, 2026 (also Apache 2.0, not to be confused with the more restrictive April Hy3-Preview), remains available at roughly a sixth of Hy4's input price for teams that do not need the newer model's scale.

Xiaomi's MiMo-V2.6-Pro (MIT, September 21) topped Artificial Analysis's open-weight index at 46 on v4.3.2 (on v5, launched October 27, it scores 30, behind DeepSeek V4.1 Flash 1017 at 33 and GLM-5.3 at 32), and StepFun's Step 5 Preview (an Apache-style license with attribution and commercial-license conditions) scored 44 on v4.3.2 (29 on v5) and ranked second on OpenRouter by tokens in the week to October 17.

Baidu Ernie is the veteran. Its current flagship, Ernie 5.1, launched in spring 2026. The genuinely open piece is the older Ernie 4.5 family, which Baidu released under an Apache 2.0 license as a range of mixture-of-experts models you can download and run. On November 5, 2026 Baidu also published ERNIE 5.5 Flash Base, a 280B-parameter base checkpoint (14B active), under its own license, which excludes the European Union, the United Kingdom and South Korea. Ernie's edge is tight search grounding, which fits Baidu's search-engine roots.

ByteDance Doubao is the odd one out: it is closed. The Doubao Seed 2.1 Pro line is served only through ByteDance's Volcano Engine cloud, and it is built for raw scale, with Volcano Engine saying Doubao's daily token usage passed 120 trillion as of March 2026 and, per its president at the June 2026 FORCE conference, 180 trillion. It is cheap and reliable for high-volume production, but you cannot download it, and the ByteDance name carries the same political baggage TikTok does in the US.

One scope note before going further: this guide is about text and chat models, the ones you reason, write, and code with. The other place Chinese labs are winning is generative media, where they arguably lead the world. Kuaishou's Kling, MiniMax's Hailuo, ByteDance's Seedance, and Alibaba's Wan top most video-generation rankings. Tencent's Hunyuan brand spans both worlds, it ships the Hy text models covered above and a separate video-generation line, so do not confuse the two when you see "Hunyuan" in a ranking. If your interest is AI video or image generation rather than chat, that is a different shortlist than the one here.

Open Weights vs Open Source: What "Open" Really Means

Almost every article on this topic calls these models "open source," and almost every one is being loose with the term. The distinction matters enough to get right, because it changes what you are actually allowed to do.

Most Chinese models are open weights, not open source. The lab publishes the trained model file so you can download, run, and fine-tune it, but it does not publish the training data or the full training code. So you can use the model freely, but you cannot fully reproduce or audit how it was built. A genuinely open-source AI model would release all three; an open-weights model releases only the weights. It is the difference between being handed a working engine and being handed the engine plus the factory blueprints. For the model-by-model license breakdown, see our guide to open weights vs open source.

"Open" also does not automatically mean "free for any use." The license attached to the weights decides that, and the licenses vary.

The surprise for many people is that the permissive ones are genuinely permissive. MIT and Apache 2.0 are about as open as licenses get, with no user-count caps. That makes DeepSeek, Xiaomi's MiMo, Hunyuan Hy4 Preview, and the open Qwen and Ernie 4.5 models legally easier to build a commercial product on than Meta's Llama 4, whose license adds a large-user restriction (Meta's smaller Muse Glimmer flips that: plain Apache 2.0 since August 2026, no such cap, though its larger Muse Spark Open, released October 15, uses the Muse Model License, which requires a "Built with Muse Spark Open" notice and a separate agreement for model-as-a-service businesses above $20M in revenue). GLM is a partial exception now: GLM-5.3's flagship weights carry a custom license with their own revenue-threshold review, closer in spirit to Llama's restriction than to MIT, though the smaller GLM-5.3-Flash kept MIT. The thing to watch is not the openness of the small models but the trend at the top: Baidu's Ernie 5.1 and all of Doubao stay closed (Baidu's only new open Ernie, the 5.5 Flash Base checkpoint, is not licensed for the EU, UK or South Korea), and Alibaba opened only a text-only version of Qwen3.8-Max in August, keeping the multimodal flagship API-only.

Are Chinese AI Models Safe? Privacy, Censorship, and Bans

This is the question that actually stops people, and it deserves a straight answer. There are three real concerns, and for each one the answer depends heavily on how you use the model.

The first is data privacy. When you use DeepSeek's hosted app or API, your prompts are processed on servers in China (its privacy policy says personal data is stored in the People's Republic of China), and China's 2017 National Intelligence Law can compel companies to hand data to the state. That is a genuine problem for regulated work, and it is why many employers already restrict the consumer DeepSeek and Qwen apps. The key distinction is hosting: that risk attaches to the hosted service, not to the open weights. Download the weights and run them on your own hardware or a Western cloud, and your data never leaves your environment. Same model, different data path.

The second is censorship. Hosted Chinese assistants refuse or deflect on politically sensitive topics like Tiananmen, Taiwan, and the Uyghurs, and a September 2025 report from the US government's AI safety body, covered by NBC News, found DeepSeek models showed weakened safety protocols and a measurable lean toward pro-Chinese framing compared with US models. For most marketing or coding work this never comes up, but if you are generating content where political neutrality matters, test for it. Self-hosting an open-weight version reduces but does not fully remove the bias baked in during training.

The third is bans. US federal agencies and several states restrict DeepSeek on government devices, and a few allied countries have moved against the hosted apps. These bans almost always target the hosted service on official devices, not the underlying open weights running in a private deployment. So "is DeepSeek banned" depends entirely on who and where you are.

Restriction is not a one-way street, either. In June 2026 the US itself briefly made Anthropic pull its top Claude models offline (Fable 5 and Mythos 5) to comply with an order restricting foreign access, a restriction lifted on July 1, 2026, but one that, while it lasted, pushed developers in other countries toward exactly these Chinese open-weight models. CrowdStrike has reported that DeepSeek-R1 is up to 50 percent more likely to produce code with severe security vulnerabilities when prompts include politically sensitive triggers; CrowdStrike did not establish the cause. Review the output, and keep these models away from government or safety-critical systems, the same as you would any model you cannot fully audit.

How They Compare to ChatGPT, Claude, and Gemini

Chinese models closed most of the gap on most tasks on v4.3.2, but Artificial Analysis's v5 index (October 27) shows it widening again, and a real gap remains at the very top. On Artificial Analysis's Intelligence Index (v4.3.2, October 2026), the top open-weight Chinese model before K3.1 Pro, Xiaomi's MiMo-V2.6-Pro, scores 46.3, against 57.6 for Claude Opus 5.5 ($4/$20 per million tokens), 56.0 for Claude Sonnet 5.5 ($2/$10), 52.6 for Gemini 4 Argon ($2/$10 until November 30, then $4/$20) and 51.8 for GPT-6.1 Sol ($2/$10). Moonshot's Kimi K3.1 Pro, released October 23, 2026 with its weights promised by November 4 and on Hugging Face as of November 6, 2026, narrowed that on v4.3.2: Artificial Analysis scored it 55, above Gemini 4 Argon and GPT-6.1 Sol and one point behind Sonnet 5.5, at the same $2/$10 list price, and said the gap between the best open and closed models would shrink from 12 points to 3 once the weights were out. On v5, launched October 27, the open-weights gap is 13 points instead: K3.1 Pro scores 46 against 59 for Claude Opus 5.5, the top closed model on general release (Google's limited-access Gemini 4 Carbon scores 60), and among the other released weights DeepSeek V4.1 Flash 1017 (33) and GLM-5.3 (32) lead (Tencent's new Hy4 scores 35, but its weights are not out yet). On cost the gap is wide: most Chinese options run a fraction of the price, DeepSeek's fifteen cents against dollars for the Western flagships (Claude Haiku 5.5 and GPT-6 Luna, at $0.10, undercut it), which is the single biggest reason Western developers reach for them. OpenAI shipped GPT-6 Astra on September 3, 2026, so the closed frontier kept moving, not standing still.

Two cautions matter. First, a lot of "beats the closed flagship" framing comes from the labs themselves, and independent observers still credit US labs with a lead at the frontier: Epoch AI finds Chinese models have trailed the US frontier by about seven months on average (January 2, 2026), and its May 29, 2026 analysis puts the average lag of the best open-weight models behind closed ones at about four months, even as Chinese labs have drawn level on specific axes like coding and agent benchmarks. Second, benchmarks are easy to over-read. Many leaderboards have thin English-language coverage, and there are ongoing questions about test contamination, so a single eye-catching score is weaker evidence than a consistent pattern across independent evaluations. It is also worth remembering that these labs still train largely on Nvidia hardware and US cloud infrastructure, so "independent Chinese AI" is a simplification.

For practical work, the split is clean. If you want maximum capability and have the budget, the top closed American models still have an edge on the hardest tasks and the smoothest tooling. If you want most of that capability at a tenth of the cost, or you need to run a model in your own environment, the Chinese open models are now a serious answer rather than a curiosity. If you are weighing one against a specific Western system, our Gemini vs ChatGPT comparison lays out the same framework for the closed side. For how Kimi, DeepSeek and GLM compare with the closed leaders on current boards, see which AI model is best.

What the Rise of Chinese AI Means for Your Visibility

Here is the part the benchmark roundups skip, and it is the one that matters if your job is marketing rather than machine learning. Chinese open models are no longer a niche. On OpenRouter, a major model-routing service, open-weight models of all origins carried about 72 percent of tokens in the week of October 5, 2026, DeepSeek V4.1 Flash was the most-used single model, and the five Chinese labs on its market-share chart (DeepSeek, Z.ai, Xiaomi, Tencent, StepFun) took 60 percent between them. In February, MIT Technology Review put Chinese open models at nearly 30 percent of OpenRouter usage in some weeks. MIT Technology Review's framing is the right one: these models have become infrastructure for global AI builders. Microsoft, for one, added DeepSeek's R1 to its Azure AI Foundry catalog within days of the model's release.

That changes the visibility picture in two ways. The first is that a growing share of the agents and applications reading your site are powered by these models, so how AI search works increasingly runs through Chinese model weights even on Western products. The second is that the big Chinese assistants, DeepSeek, Kimi, Doubao, Ernie, and Qwen's Tongyi, are their own answer engines (QuestMobile's May 2026 count put Doubao at 382 million monthly users, Qwen at 167 million and DeepSeek at 130 million in China), and almost nobody in Western marketing is thinking about whether they appear there.

In our experience at geotoolbox, the brands that stay visible as the model field splinters are the ones that treat AI reachability and citation as a first-class channel rather than an afterthought. Our AI Crawler Checker covers 34 crawler user-agents, including ByteDance's, so you can at least see which AI systems your robots.txt currently lets in. The open question nobody has answered well yet, ourselves included, is how consistently these Chinese engines cite and link back to the Western sources they draw on, which is exactly the kind of gap worth watching rather than guessing about. The same logic applies closer to home: monitoring how AI engines mention your brand across the engines your buyers already use turns each new engine into one more column instead of a blind spot.

Which Chinese AI Model Should You Use?

There is no single winner, because the right pick depends on the job. Here is the short version. For the global field including Llama, Gemma, and gpt-oss, our best open-source LLMs ranking covers all eleven.

A practical note on access: you rarely need a Chinese account to use these. The open models are on Hugging Face, and Western-friendly gateways like OpenRouter let you call most of them through one API without touching a native cloud. That avoids a Chinese account and cloud while keeping the price advantage, though a gateway still routes your prompt through a third party, so check its retention terms if data residency matters.

So there is no one best Chinese AI model. The field is good enough now that the right pick is the one that fits your specific job, budget, and risk tolerance, and the table above is where to start.

The Models Will Keep Changing. Your Visibility Strategy Shouldn't Have To

The specific version numbers in this guide will be stale within months, because that is the pace these labs ship at. What will not change as fast is the underlying shift: capable AI is getting cheaper, more open, and more fragmented across providers, and a rising share of it now runs on Chinese weights.

For anyone whose livelihood depends on being found, that fragmentation is the real story. When a dozen engines, Western and Chinese, can all answer a buyer's question, the brands that win are the ones cited across them, not the ones optimized for a single search box. That is the problem geotoolbox is built for: our Citation Interceptor tracks where you appear, and where you are missing, across ChatGPT, Perplexity, Gemini, Claude, Google AI Overviews, Google AI Mode, Bing Copilot, and Grok, so you can fix the gaps instead of guessing at them. Start with the engines that already send you buyers, and keep one eye on the Chinese ones coming up fast behind them.

Frequently Asked Questions

What is the best Chinese AI model right now?

There is no single best one. GLM-5.3 is among the strongest Chinese models on coding and agent leaderboards (Xiaomi's MiMo-V2.6-Pro and DeepSeek V4.1 Flash edged it on Artificial Analysis's v4.3.2 overall index; on v5 it scores 32, one point behind DeepSeek V4.1 Flash 1017 (33) and ahead of MiMo at 30), DeepSeek V4.1 Flash is among the cheapest per token, Qwen is the most-adopted open base, and Kimi is strongest for long agent runs, K2.7-Code for routine work (on Moonshot's API only for accounts registered before October 31, 2026) and K3.1 Pro when a session specifically needs the extra scale. The right pick depends on whether you care most about cost, coding, fine-tuning, or agent work.

Are Chinese AI models safe to use, and does my data go to China?

If you use DeepSeek's hosted app or API, your prompts are processed in China and can be subject to Chinese data laws, which is a real concern for sensitive work. If you self-host the open weights on your own hardware or a Western cloud, your data never reaches China. A gateway like OpenRouter avoids a Chinese account and cloud, though your prompt still passes through a third party, so check its routing and retention terms.

What is the difference between open-weight and open-source?

Open-weight means the lab publishes the trained model so you can download, run, and fine-tune it. Open-source would also include the training data and code needed to rebuild it from scratch. Most of the leading Chinese models, like DeepSeek and GLM, are open-weight but not fully open-source, so you can use them freely without being able to fully audit how they were made.

Which Chinese AI models do ChatGPT, Gemini, and Perplexity cite?

That is a different question from which models are good. Western answer engines cite sources based on their own retrieval, not on which model a lab built, so being mentioned by ChatGPT or Gemini is about your content earning the citation. The newer wrinkle is that Chinese assistants like DeepSeek and Kimi are their own answer engines, and how they cite Western sources is still an open and largely unmeasured question.

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• Kimi K3.1-Pro License - Moonshot AI, Hugging Face - huggingface.co/moonshotai/Kimi-K3.1-Pro/blob/main/LICENSE
• MiniMax-M3 weights (first commit June 12, 2026) - MiniMax, Hugging Face - huggingface.co/MiniMaxAI/MiniMax-M3
• DeepSeek API change log (V4.1-Flash-1017, October 17, 2026) - DeepSeek - api-docs.deepseek.com/updates
• Leaderboards, Intelligence Index v5.0.1 - Artificial Analysis, checked November 7, 2026 - artificialanalysis.ai/leaderboards/models
• Chinese AI models have lagged the US frontier by 7 months on average since 2023 - Epoch AI, January 2, 2026 - epoch.ai/data-insights/us-vs-china-eci
• Nvidia loses $589 billion in market cap - Tom's Hardware, January 28, 2025 - tomshardware.com/tech-industry/artificial-intelligence/nvidia-loses-usd589-billion-in-market-cap-broad-stock-plunge-triggered-by-deepseek-ai-release