--- title: "Best Chinese LLMs in 2026: DeepSeek, Qwen, GLM, and Kimi Compared" slug: best-chinese-llm-models-2026-deepseek-qwen-glm-kimi-compared description: "Four Chinese labs now ship the open-weight models most production teams run: DeepSeek, Qwen, GLM, and Kimi. Every current model, what it costs, what it takes to self-host, and which one to pick for each job." author: "Yotta Labs" date: 2026-09-22 categories: ["Inference"] canonical: https://www.yottalabs.ai/post/best-chinese-llm-models-2026-deepseek-qwen-glm-kimi-compared --- # Best Chinese LLMs in 2026: DeepSeek, Qwen, GLM, and Kimi Compared ![](https://cdn.sanity.io/images/wy75wyma/production/92629217c068556edbaaadd7443d7bdb07949bd5-2240x1260.png) *The open-weight frontier is Chinese now. Here’s the whole field in one place: current models, prices, hardware floors, licenses, and which to pick.* Ask which open-weight models are worth running in production in September 2026 and the answer is four labs: DeepSeek, Alibaba’s Qwen, Z.ai’s GLM, and Moonshot’s Kimi. Between them they cover every tier from a 27B model that fits one GPU to a 2.8 trillion parameter model that needs a cluster, and every one of them has shipped a new flagship or a new Flash-class model since July. This post is the map. Each model links to its own breakdown; this page tells you where it sits. One rule up front, because it decides most of what follows. Every big model here is a sparse mixture of experts, which means a small number of parameters fire per token but every parameter has to sit in GPU memory. Low active counts make these models cheap to call; total counts decide what they cost to host. Keep both numbers in view. ## The field at a glance Prices are each vendor’s published rates as of this week, except where noted as [Yotta AI Gateway](https://www.yottalabs.ai/ai-gateway) rates; the Gateway carries most of this table at flat prices with no peak windows. Self-host floors are for the official checkpoints at their shipped precision; community 4-bit builds roughly halve them. ## Which one for which job **Best flagship for most teams: DeepSeek V4 Pro.** 1.6T parameters, MIT weights, a #3 ranking on Artificial Analysis at launch, and a lower price than any other open flagship. Its successor is already named: DeepSeek has confirmed [V4.1 Pro](https://www.yottalabs.ai/post/deepseek-v4-1-pro-release-date-what-is-known-how-to-prepare-2026) is coming, undated, and is keeping V4 Pro on the API until then. **Best Flash-class model right now: a two-way call.**[DeepSeek V4.1 Flash](https://www.yottalabs.ai/post/deepseek-v4-1-flash-pricing-specs-v4-pro-routing-2026) is the newest, fastest (about 214 output tokens per second on Artificial Analysis’s measurement), and natively multimodal; GLM 5.3 Flash scores slightly higher on the same index (42 vs 40 on the current v4.3 scale), costs less, and takes video input. Both need an 8-GPU node. The full head-to-head is [DeepSeek V4.1 Flash vs GLM 5.3 Flash](https://www.yottalabs.ai/post/deepseek-v4-1-flash-vs-glm-5-3-flash-2026). **Best model you can run on one GPU: Qwen3.8-27B.** A dense 27B under Apache 2.0 with image and video input, about 54 GB at BF16 and 16 GB at 4-bit, and on the old Artificial Analysis scale it tied DeepSeek V4 Flash. Nothing else in this table fits a workstation. [Specs and hardware](https://www.yottalabs.ai/post/qwen-3-8-27b-specs-hardware-requirements-how-to-run-2026), and the [local Ollama and GGUF guide](https://www.yottalabs.ai/post/how-to-run-qwen-3-8-27b-locally-ollama-gguf-single-gpu-2026). **Best two-GPU model: DeepSeek V4 Flash.** Retired on DeepSeek’s own API in favor of V4.1, but the MIT weights are still on Hugging Face at 166.9 GB, and it’s the only model at this capability level that runs on 2x H200. [Hardware breakdown](https://www.yottalabs.ai/post/deepseek-v4-flash-hardware-requirements-gpu-memory-2026). **Best for long-horizon agents: GLM 5.3 or Kimi K3.** GLM 5.3 is Z.ai’s deepest post-training run, built for multi-hour agent sessions; Kimi K3’s independent results lead on security work and long terminal loops. Both are 8-GPU-plus to host. [What changed in GLM 5.3](https://www.yottalabs.ai/post/glm-5-3-whats-new-benchmarks-how-to-access-2026), [Kimi K3 specs and benchmarks](https://www.yottalabs.ai/post/kimi-k3-specs-benchmarks-how-to-access-2026). **Cheapest to call: GLM 5.3 Flash and Qwen 3.8-Flash-Next**, at $0.15 per million input tokens and $0.50 or $0.47 output. DeepSeek V4.1 Flash is $0.15 / $0.60 off-peak and double at peak. **Cleanest licenses: DeepSeek (all MIT), Qwen3.8-27B (Apache 2.0), GLM 5.2 and 5.3 Flash (MIT).** Read before building a product on the others: GLM 5.3’s custom license, Qwen 3.8-Max’s custom license, Flash-Next’s community license, and Kimi K3’s license, which adds conditions only above $20M a year or 100M monthly users. ## DeepSeek: the evidence-first lab DeepSeek’s V4 generation shipped in stages, Flash on July 31 and Pro GA on August 13, both MIT, and then V4.1 Flash on September 10 on a new architecture: a causal encoder-decoder with 8B active parameters on input and 16B on output, a large Engram memory component, native image input, and a KV cache of 890 bytes per token. DeepSeek’s own numbers put V4.1 Flash ahead of V4 Pro on agentic benchmarks and behind it on knowledge tests, which is why V4 Pro is staying on the API until V4.1 Pro arrives. The catch with V4.1 Flash is hosting: 510 GB on disk and about 614 GB of GPU memory, a full node where V4 Flash ran on two H200s. Pricing is the lowest in the field for a Flash model, with peak and off-peak windows and cache hits at $0.003. Start here: [DeepSeek V4 release timeline and specs](https://www.yottalabs.ai/post/deepseek-v4-release-date-specs-how-to-access-2026), [V4.1 Flash hardware requirements](https://www.yottalabs.ai/post/deepseek-v4-1-flash-hardware-requirements-gpu-memory-2026). ## Qwen: the widest lineup Alibaba covers more tiers than anyone. Qwen 3.8-Max is a 2.4 trillion parameter flagship with a 1M context, priced at $2 / $6 on Alibaba’s API and $1.50 / $4.50 on the Gateway, with weights under a custom license. Qwen3.8-27B is the dense workhorse and the best single-GPU model available. Qwen 3.8-Flash-Next is an experimental 6B-active MoE that Alibaba explicitly calls a preview of the Qwen 4 architecture. And Qwen 4 itself is the next event in this field: at the Apsara Conference on September 22, Alibaba said it’s in training and coming “very soon.” [Qwen 4: what’s confirmed](https://www.yottalabs.ai/post/qwen-4-release-date-what-is-known-how-to-prepare-2026) tracks it. Start here: [Qwen 3.8 vs Qwen 3.8-Max](https://www.yottalabs.ai/post/qwen-3-8-vs-qwen-3-8-max-differences-which-to-use-2026), [Qwen 3.8 benchmarks, what’s verified](https://www.yottalabs.ai/post/qwen-3-8-benchmarks-what-is-verified-2026). ## GLM: the agent specialists Z.ai’s GLM 5.3 is the same 753B base as GLM 5.2 with a much deeper post-training run aimed at long-horizon engineering work; its weights shipped at the end of August under a custom license, a change from 5.2’s MIT. GLM 5.3 Flash is the more interesting release for most teams: 320B parameters, 18B active, MIT, image and video input, and the lowest API price in the field. Both flagship and Flash need an 8-GPU node to host, with the flagship at 744 GB in FP8 and Flash at about 306 GiB. Start here: [GLM 5.3 vs GLM 5.3 Flash](https://www.yottalabs.ai/post/glm-5-3-vs-glm-5-3-flash-benchmarks-which-to-use-2026), [GLM 5.3 hardware requirements](https://www.yottalabs.ai/post/glm-5-3-hardware-requirements-gpu-memory-2026), [GLM 5.3 Flash hardware requirements](https://www.yottalabs.ai/post/glm-5-3-flash-hardware-requirements-gpu-memory-2026). ## Kimi: the biggest open model Moonshot’s Kimi K3 is 2.8 trillion parameters with 896 experts and about 104B active, native vision, always-on reasoning, and a 1M context, shipped as MXFP4 weights totaling 1.56 TB. It has the most third-party benchmark coverage of any open flagship and leads on coding and long agent loops in independent tests. It is also the one model in this table that no single node can hold; self-hosting means a multi-node cluster with 1.6 TB or more of aggregate GPU memory, and for nearly everyone the API is the answer. At $3 / $15 with $0.30 cached input, it’s the most expensive Chinese model to call and still a fraction of Western frontier pricing. Start here: [Kimi K3 hardware requirements](https://www.yottalabs.ai/post/kimi-k3-hardware-requirements-gpu-memory-2026), [Qwen 3.8 vs Kimi K3](https://www.yottalabs.ai/post/qwen-3-8-vs-kimi-k3-benchmarks-comparison-2026). ## Other Chinese labs MiniMax, StepFun, Tencent’s Hunyuan line, and ByteDance all ship models, and ByteDance’s Seedance is the leading Chinese video model (covered in the [Seedance 2.5 vs 2.0 comparison](https://www.yottalabs.ai/post/seedance-2-5-vs-seedance-2-0-differences-which-to-use-2026)). We haven’t verified current specs, prices, or weights for the text models from those labs, so they’re not in the table above; this post covers the four labs whose models we’ve tested, priced, and sized. ## What’s coming Two confirmed, undated launches will reshuffle this table. Qwen 4 is in training and “very soon” per Alibaba, with Flash-Next as its architecture preview. DeepSeek V4.1 Pro is confirmed by name and is the reason V4 Pro is still on the API. Both have tracking pages linked above and will be covered the day they ship. ## How to run them Every model in the table speaks an OpenAI-compatible interface, which makes the practical setup a routing layer rather than a vendor choice. [Yotta AI Gateway](https://www.yottalabs.ai/ai-gateway) carries DeepSeek V4 Pro and V4 Flash, Qwen 3.8-Max and Qwen3.8-27B, GLM 5.3 and GLM 5.2, and Kimi K3 behind one API key at flat rates, so an A/B between any two is a model-string change. For the models worth self-hosting, [GPU pods by the hour](https://www.yottalabs.ai/pricing) cover the range from a single GPU for Qwen3.8-27B to 8x H200 and B300 nodes for the Flash and flagship tiers; the [vLLM vs SGLang comparison](https://www.yottalabs.ai/post/vllm-vs-sglang-which-inference-engine-should-you-use-in-2026) covers the engine choice. ## Frequently asked questions **What is the best Chinese LLM in 2026?** For a flagship with independent evidence, DeepSeek V4 Pro or Kimi K3. For the best value, DeepSeek V4.1 Flash or GLM 5.3 Flash. For a model that runs on one GPU, Qwen3.8-27B. There’s no single winner; the labs specialize. **Are Chinese LLMs open source?** Most of them. DeepSeek’s V4 line is MIT, Qwen3.8-27B is Apache 2.0, GLM 5.2 and GLM 5.3 Flash are MIT. GLM 5.3, Qwen 3.8-Max, Flash-Next, and Kimi K3 have public weights under custom or community licenses with conditions worth reading. **Which Chinese LLM is cheapest?** By API, GLM 5.3 Flash and Qwen 3.8-Flash-Next at $0.15 per million input tokens, with DeepSeek V4.1 Flash at $0.15 off-peak. By self-hosting, Qwen3.8-27B, the only one that runs on a single GPU. **Which Chinese LLM can I run locally?** Qwen3.8-27B on an 80 GB card at BF16 or a 24 GB card at 4-bit. DeepSeek V4 Flash on two H200s. Everything else needs an 8-GPU node or more. **DeepSeek vs Qwen vs GLM vs Kimi: which is best for coding?** Kimi K3 leads the independent coding results among open models; GLM 5.3 is built for long engineering sessions; DeepSeek V4.1 Flash leads DeepSeek’s own agentic benchmarks at a fraction of the price. Test on your own repo; the differences are task-dependent. **Are these models on Yotta AI Gateway?** DeepSeek V4 Pro and V4 Flash, Qwen 3.8-Max and 27B, GLM 5.3 and 5.2, and Kimi K3 are live. V4.1 Flash and GLM 5.3 Flash are not in the catalog as of this writing; this post will be updated when they are. **What’s coming next from Chinese labs?** Qwen 4 (in training, “very soon” per Alibaba on September 22) and DeepSeek V4.1 Pro (confirmed, undated). Both have tracking pages linked above. ## Bottom line Four labs, roughly a dozen current models, and a clear structure: DeepSeek for evidence and MIT weights, Qwen for the widest range and the only single-GPU option, GLM for agents and the cheapest API tier, Kimi for the biggest and best-benchmarked open flagship. The right answer for most teams is two of them behind one routing layer: a cheap Flash-class model for volume and a flagship for the tail. That’s a configuration change on [Yotta AI Gateway](https://www.yottalabs.ai/ai-gateway), and the per-model breakdowns linked throughout are where to go next.