Which Chinese LLM Is Best in 2026?
There is no single best Chinese LLM in 2026. DeepSeek V4 stands out for long-context reasoning and agents, Qwen3.8 for coding and multimodal work, Kimi K3 for long-horizon multi-agent tasks, and GLM-5.3 for coding and tool use. The best choice depends on your workload, hardware, context requirements, and whether you need open weights.
When this article was first published, Securities Times, a Chinese national financial newspaper, reported that local regulators had granted approvals to 14 large language models (LLMs) for public use. That first batch of approvals included 40 AI models in total.
Among the 14 LLMs greenlit for commercial deployment were products from well-known companies such as smartphone maker Xiaomi and the startup 01.AI started by Kai-Fu Lee.
The full list was not disclosed, so we focused on four prominent Chinese model families. We have kept that original snapshot below and added a 2026 update to each entry.
Model family | Lab | Latest model | Total / active params | Context (tokens) | License | Best for |
|---|---|---|---|---|---|---|
DeepSeek | DeepSeek | DeepSeek V4-Pro | 1.6T / 49B | 1M | MIT | Deep reasoning, long-context coding, complex agents |
DeepSeek | DeepSeek | DeepSeek V4-Flash | ~200B / 13B | 1M | MIT | Faster reasoning, coding, agents, more efficient deploy-ment |
Qwen | Alibaba | Qwen3.8-Max | 2.4T / 95B | 262K native, up to 1M | Qwen3.8-Max License | Long-running coding, complex agents |
Qwen | Alibaba | Qwen3.8-27B | 27B dense | 262K native, up to 1M | Apache 2.0 | Coding, research, more practical deploy-ment |
Kimi | Moonshot AI | Kimi K3 | 2.8T / 104B | 1M | Kimi K3 License | Multi-agent research, long-horizon coding, tool use |
GLM | Z.ai | GLM-5.3 | 753B / 40B | 1M | GLM-5.3 License | Long-running coding, agents, cyber-security |
GLM | Z.ai | GLM-5.3-Flash | 320B / 18B | 1M | MIT | Coding, agents, efficient multi-modal workloads |
ERNIE | Baidu | ERNIE 5.1 | Not disclosed | Not officially disclosed | Proprie-tary | Agents, search, reasoning, creative writing |
MiniCPM | OpenBMB | MiniCPM-V 4.6 | ~1.3B | 256K | Apache 2.0 | On-device image/ video under-standing |
MiniCPM | OpenBMB | MiniCPM-o 4.5 | 9B | 40,960 tokens | Apache 2.0 | On-device multi-modal interaction vision, speech |
How China's LLM Families Evolved From 2020 to 2026
CPM (2020; 2.6B): China’s first large-scale pre-trained model created by the Beijing Academy of Artificial Intelligence (BAAI) and Tsinghua University.
2026 update: CPM is now best understood as a historical starting point. The broader OpenBMB ecosystem has moved toward the smaller MiniCPM family, including multimodal and on-device models such as MiniCPM-o 4.5, MiniCPM-V 4.5 and MiniCPM-V 4.6.
ERNIE 3.0 Titan (2021; 260B): A big brother of Baidu’s ERNIE 3.0 foundation model having 10B parameters. It was designed to explore the performance of scaling up the original model. At the time of its creation, it was the largest Chinese dense pre-training model.
2026 update: Baidu’s open family has advanced to ERNIE 5.1. This new model is an exercise in doing more with much less. It reaches flagship-level performance while using just 6% of the pre-training compute of comparable models, with particular strengths in agents, deep search, reasoning, and, a bit unusually, creative writing.
Yi family (2023; 6B, 34B): These models, developed by 01.AI, represent a pioneering force in bilingual large language models, demonstrating exceptional strength in language understanding and commonsense reasoning. Particularly notable for their performance on both English and Chinese benchmarks, they secured top rankings on leaderboards such as AlpacaEval and the Hugging Face Open LLM Leaderboard at the time.
2026 update: The latest major open update in 01.AI’s official Yi repository remains Yi 1.5, released in 2024 with improvements in coding, mathematics, reasoning, and instruction following. Yi remains useful as a bilingual open model family, although its public release cadence has been quieter than DeepSeek’s or Qwen’s.
Baichuan 2 (2023; 7B, 13B): By providing both Base and Chat models in various sizes, including an efficient 4-bit quantized version, Baichuan 2 facilitated a wide range of research and commercial applications, lowering the barrier for innovation and enabling more developers to incorporate advanced AI capabilities into their projects.
2026 update: Baichuan’s open work has expanded beyond the original general-purpose text models. Its official repositories now include Baichuan-Omni-1.5 for text, image, audio, and video understanding, plus the medical-focused Baichuan-M3-235B released in 2026.
Chinese LLMs in 2026: What's New
China’s top models aren’t just fighting over who can build the biggest model anymore. Now it’s about who can reason better, code longer (even for days), use tools, handle huge context, and work across text and images. And there’s no clear winner. Developers have several strong ecosystems to choose from.
DeepSeek V4 is going big on million-token context and agents that know what to do with it. Qwen3.8 is a 2.4T giant multimodal model that can keep coding for days. Kimi K3 is another huge step toward long-running, multi-agent work, while GLM-5.3 doubles down on real-world coding and shows surprisingly strong cybersecurity skills.
DeepSeek V3/V3.2 and R1 (2025): DeepSeek-V3 uses a mixture-of-experts architecture with 671B total parameters and 37B activated per token, while R1 is the company's open reasoning line and the first baseline for all reasoning models. V3.2 adds newer experimental work, but references to “R2” should be treated as speculation until DeepSeek publishes an official model or repository. Official DeepSeek repositories
2026 update:
DeepSeek-V4: DeepSeek is pushing two things at once: a million-token memory and agents that can use it. V4-Pro is the 1.6T heavyweight for deep reasoning and long coding runs, while V4-Flash squeezes surprisingly similar intelligence into just 13B active parameters. Both can dial reasoning effort up or down, and the new experimental Flash-Vision adds images to the agent loop, bringing visual understanding and tool use into the same workflow. DeepSeek API Docs
Qwen3 (2025): Alibaba's Qwen3 family includes dense and mixture-of-experts models in multiple sizes. Its signature feature is the ability to switch between thinking mode for harder reasoning tasks and non-thinking mode for faster general responses, with official support for more than 100 languages and dialects. Official repository, model collection
2026 update:
Qwen3.8-27B: Alibaba packs its latest Qwen generation into a relatively compact 27B dense model built for coding and long-running agent tasks, professional work, and research. It natively understands text, images, and video, supports adjustable thinking depth, and comes with a huge 262K native context window that can stretch to 1M tokens. Official repository
Qwen3.8-Max: It is Alibaba’s biggest Qwen yet. Qwen3.8-Max is a 2.4T-parameter MoE model with 95B active at a time — and the first Max-class Qwen getting open weights. The more interesting part is how long it can keep working: Qwen shows it coding autonomously for 10+ days, iterating from feedback, and handling complex projects end-to-end with little human help. It also gets a 1M-token context window and native multimodal capabilities. Official Qwen blog post
Kimi K2 (2025): Moonshot AI's open-weight Kimi K2 is a 1T-parameter mixture-of-experts model with 32B activated parameters. It is optimized for coding, tool calling, and agentic tasks, and the official deployment guide supports engines including vLLM, SGLang, KTransformers, and TensorRT-LLM. Official repository
2026 update:
Kimi K3: This Moonshot AI’s newest open model has 2.8T parameters, with 104B active per token. It can actually do long-horizon coding, research, tool use, and even video editing. It gets native vision, a 1M-token context window, and can orchestrate 20+ subagents on complex research tasks. Moonshot calls it the first open 3T-class model. Original paper, official Kimi blog post, official repository
GLM-4.5 (2025): Z.ai’s first open MoE model (355B parameters in total, 32B active) is designed to think, code, and act in the same loop. It combines thinking/non-thinking modes, native function calling, and a 128K context window. Where it really shines is agentic work: using tools, browsing, building full-stack apps, and carrying multi-step tasks through to the end.
2026 update:
GLM-5.3: This newest Z.ai’s 744B-parameter MoE model with 40B active parameters is trained to stay on long, messy, real-world tasks – coding, testing, debugging, and iterating until the job is done. For example, it can take on coding tasks spanning days of human work, with reasoning effort that scales from low to max. The surprise is cybersecurity: as agent training scaled, GLM-5.3 started getting dramatically better at tracing vulnerabilities through entire exploitation chain. Official Z.ai blog post
Can Chinese LLMs be deployed locally?
Many can. Open-weight models from DeepSeek, Qwen, Kimi, Z.ai, and other Chinese labs can be self-hosted, but the hardware requirements vary enormously. Smaller and quantized models can run on consumer GPUs or local workstations. At the other extreme, trillion-parameter MoE models such as Qwen3.8-Max or Kimi K3 require substantial multi-GPU infrastructure to make hosted inference much more practical for most users.
We also did a deep dive into Chinese models: Kimi K2 vs DeepSeek-R1 vs Qwen3 vs GLM-4.5
FAQ
What are the leading Chinese LLMs in 2026?
Some of the biggest families to watch are DeepSeek V4, Alibaba’s Qwen3.8, Moonshot AI’s Kimi K3, Z.ai’s GLM-5.3, and Baidu’s ERNIE 5.1. There’s no obvious frontrunner anymore: different models stand out for reasoning, coding, long-running agents, multimodality, search, or efficient deployment.
Is Qwen3.8 open source?
Qwen3.8 has open models and publicly available weights through Alibaba’s official repositories. The family ranges from the relatively compact Qwen3.8-27B to Qwen3.8-Max, a 2.4T-parameter MoE model and the first Max-class Qwen to get open weights.
What is DeepSeek-R2, and has it been released?
DeepSeek-R2 was widely discussed as a possible successor to R1, but DeepSeek moved on to the V4 family instead. DeepSeek-V4-Pro and V4-Flash both support a 1M-token context window, adjustable reasoning effort, and agentic workflows, while the experimental V4-Flash-Vision adds multimodal capabilities.
What is Kimi K3 best used for?
Kimi K3 is built for long-horizon agentic work: coding, research, tool use, and complex multi-step projects. It has a 1M-token context window, native vision, and can coordinate multiple subagents — Moonshot demonstrated it running more than 20 subagents on a research task.
What makes GLM-5.3 different?
GLM-5.3 is especially focused on long, real-world coding and agent tasks rather than short benchmark-style problems. One unexpected result of scaling this training was much stronger cybersecurity performance, including vulnerability discovery and reasoning across exploitation chains.
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