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There is no single “best” Chinese open-weight model. Qwen, DeepSeek, Kimi and GLM are separate families optimized for different jobs. In the latest structured snapshot available from Stanford HAI and DigiChina (information current to December 4, 2025), Qwen emphasizes multilingual and multimodal breadth, DeepSeek-R1 emphasizes mathematical reasoning, Kimi-K2 emphasizes coding and agentic work, and GLM-4.5 targets general reasoning, coding, tool use and vision. Llama is a useful baseline, but the available evidence does not provide a current, independently controlled, same-version scorecard that ranks every family against Llama. Choose by exact checkpoint, workload, license and deployment route rather than by a permanent leaderboard position.
What “Chinese open-weight model” means
Open-weight models let you download model parameters (the weights) and usually provide enough inference software or documentation to run them yourself. That is not the same as fully open-source artificial intelligence: training data, data-cleaning pipelines, reinforcement-learning recipes and all foundational code may remain unavailable. A model can therefore be highly useful for local or private deployment while still having substantial limits on transparency.
The phrase “Chinese open-source AI model” is common in searches, but “open-weight” is more precise for this comparison. Access to weights does not automatically grant unrestricted commercial rights, and hosted access is not equivalent to possessing the weights. If you call a model through an API, the provider still controls the endpoint, data route, retention settings and service policy.
ITPro quotes Amanda Brock, CEO of OpenUK, saying: “Their reviews and ratings show their capacity is moving toward matching the biggest and best of US frontier AI models.” That is an expert assessment, not a universal benchmark result. Capabilities, licenses and operating conditions differ by checkpoint.
#1 Best Overall
Family snapshot: dates, scale and stated strengths
The following figures come from the Stanford HAI/DigiChina family table, whose information was current on December 4, 2025. Parameter counts for mixture-of-experts (MoE) systems show both total weights and the subset activated for each token; they are not directly comparable to the parameter count of a dense model.
| Family and checkpoint | First-release date in the snapshot | Reported scale and context | Stated emphasis | License label in the cited source |
|---|---|---|---|---|
| Qwen3 (Alibaba) | April 29, 2025 | Family range 0.6B–235B; largest noted MoE activates 22B per token; context up to approximately 128K | Multilingual and multimodal breadth; dense, MoE and specialized variants | Apache 2.0 for Qwen3 |
| Qwen3-Omni | September 22, 2025 | Specific size not stated in the table | Multimodal family extension | Check the exact checkpoint license |
| DeepSeek-R1 | January 20, 2025 | 671B total / 37B active MoE; distilled Qwen- and Llama-based models from 1.5B to 70B | Mathematics and reasoning | MIT for R1 |
| Kimi-K2 (Moonshot AI) | July 11, 2025 | 1T total / 32B active MoE; context up to approximately 256K | Coding and agentic use | Modified MIT; attribution requirements are noted for large-scale commercial deployment |
| GLM-4.5 (Z.ai) | July 28, 2025 | Air: 106B total / 12B active; Base: 355B / 32B active | Generalist reasoning, coding, tool use and vision; GLM-4.5V supplies vision | MIT for GLM-4.5 |
These are dated family-level snapshots, not specifications for every later release. The table does not establish matched latency, memory, energy or serving cost. A 1T-parameter MoE does not necessarily perform like a 1T dense model, and its 32B active count does not tell you the complete memory required to load weights, maintain the key-value cache or run the serving stack.
How Qwen, DeepSeek, Kimi and GLM differ
Qwen: broad language and modality coverage
Qwen3 is the broadest general-purpose family in the snapshot. Its 0.6B-to-235B range gives developers small local checkpoints as well as very large models, while the largest noted MoE activates 22B parameters per token. The family includes dense, MoE and task-specialized releases, and Qwen3-Omni extends the line into multimodal work. A context window of approximately 128K is reported for the family snapshot.
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For multilingual applications, compare the exact languages and scripts you need rather than assuming that a family-level “multilingual” label means equal quality everywhere. Test terminology from your own domain, especially when prompts mix languages, code and images. Qwen3 is labeled Apache 2.0 in the Stanford table, but verify the license attached to the particular weights you intend to deploy.
DeepSeek-R1: reasoning-first, with smaller distilled choices
DeepSeek-R1 is described as a reasoning-focused 671B MoE model with 37B active parameters. Its practical importance is the accompanying distilled line: Qwen- and Llama-based variants span 1.5B to 70B parameters, making experimentation possible on hardware that cannot host the full model. Distillation can change behavior, speed and accuracy, so a distilled checkpoint should be evaluated as its own model rather than treated as a miniature R1 with identical results.
Rank #2
The Stanford table labels R1 MIT. A newer Alibaba Cloud license list, updated October 11, 2026, labels the separately named DeepSeek-V4-Pro MIT. That later entry does not create a blanket MIT license for every DeepSeek release.
Kimi-K2: long context, coding and agents
The Stanford snapshot describes Kimi-K2 as a 1T-parameter MoE family with 32B active parameters and context up to approximately 256K. Its stated center of gravity is coding and agentic use: tasks in which a model writes or edits software, calls tools, plans multiple steps and maintains a large working context.
Long context is not the same as reliable long-document recall. Test retrieval of facts at the beginning, middle and end of documents, and measure tool-call accuracy over the number of steps your agent actually performs. The family is labeled Modified MIT in the Stanford table, with attribution requirements noted for large-scale commercial deployment. The October 11, 2026 Alibaba Cloud list labels Kimi-K2.5 and Kimi-K2.6 Modified MIT, so read the operative license for the exact checkpoint.
GLM-4.5: generalist reasoning, tools and vision
GLM-4.5 is represented by Air (106B total, 12B active) and Base (355B total, 32B active), plus the GLM-4.5V vision model. The family is positioned as a generalist option spanning reasoning, coding, tool use and visual inputs. It can be a sensible candidate when one workflow combines text, software tasks and images, but the family label does not guarantee the same quality across every modality.
The Stanford table lists MIT for GLM-4.5. The newer October 11, 2026 list labels GLM-5 and GLM-5.1 MIT. Those are version-specific entries, not evidence that every GLM release carries identical terms.
What the evidence actually says about Llama comparisons
Llama is a useful comparator because DeepSeek-R1’s distilled series includes Llama-based variants, and the surfaced SuperCLUE leaderboard includes Llama 4 Maverick among overseas models. However, the available sources do not establish a current, controlled comparison of all these families against Llama using the same checkpoints, prompts, hardware and independent evaluators.
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The Stanford brief characterizes several family results as “high self-reported.” Vendor-reported numbers can be useful for discovering promising checkpoints, but they are not interchangeable with independently reproduced tests. Record the checkpoint, benchmark version, prompt format, scoring method, date and whether the score was self-reported before drawing a conclusion.
Which family fits which workload?
| Primary need | Best starting candidates | What to verify before choosing |
|---|---|---|
| Mathematical or multi-step reasoning | DeepSeek-R1 and an appropriate distilled variant | Whether your benchmark rewards the reasoning style; latency and accuracy on your problem format |
| Multilingual text applications | Qwen3 | Quality for each required language, script, terminology and safety policy |
| Coding and software agents | Kimi-K2, DeepSeek variants and GLM-4.5 | Repository-scale context, tool-call reliability, edit correctness and recovery from failed steps |
| Mixed text, tools and images | GLM-4.5/4.5V or a Qwen multimodal checkpoint | Vision accuracy on your image types and consistency between visual and text outputs |
| Small local experiments | Qwen’s smaller checkpoints or DeepSeek distilled models | Quantization quality, memory use and throughput on your hardware |
This is a candidate map, not a ranking. A smaller model that fits your existing infrastructure can be more useful than a larger model that requires a new serving stack, and a hosted endpoint may be preferable when you cannot operate GPUs or manage model updates.
License checks before commercial deployment
Do not infer license terms from a laboratory or family name. Inspect the license file shipped with the exact weights and record its version and date. In particular:
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- DeepSeek-R1 is labeled MIT; DeepSeek-V4-Pro is separately labeled MIT on the October 11, 2026 Alibaba Cloud list.
- Kimi-K2 is labeled Modified MIT, and the Stanford brief notes attribution requirements for large-scale commercial deployment. Kimi-K2.5 and Kimi-K2.6 are also labeled Modified MIT on the newer list.
- GLM-4.5 is labeled MIT; GLM-5 and GLM-5.1 are separately labeled MIT on the newer list.
Read clauses covering attribution, redistribution, acceptable use, geographic restrictions, notices and modifications. Keep a copy of the license with the model artifact in your software bill of materials. If a provider serves the model through an API, review the provider’s contract and data-processing terms in addition to the weight license.
Understanding parameter counts and local serving
Total parameters describe the complete set of weights in an MoE model; active parameters describe the subset selected for one token. Active counts help explain per-token computation, but they do not specify the full memory needed for loading weights. Memory also depends on numerical precision, quantization, framework overhead, tokenizer and runtime, plus the key-value cache for long contexts.
For that reason, the published figures do not support a universal claim such as “this model needs a particular GPU” or “this model is cheaper to run.” The reviewed material contains no apples-to-apples measurements of current hardware requirements, latency, power or cost for Chinese releases versus Llama. Obtain those numbers for your exact checkpoint, quantization, context length, batch size, hardware and serving software.
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Self-hosting
Self-hosting can keep prompts and outputs inside infrastructure you control, allow custom monitoring and avoid dependence on one endpoint. It also makes you responsible for hardware capacity, patching, model updates, access controls, incident response and license compliance. A long-context MoE model may require substantially more memory than its active-parameter figure suggests.
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Hosted endpoints reduce infrastructure work and can expose large models that are impractical to load locally. They also introduce a provider-controlled data route, retention policy, region and availability contract. Confirm where requests are processed, whether prompts are retained for training, how deletion works and which exact checkpoint and revision the endpoint runs.
Best Value
Stanford HAI/DigiChina notes that adoption is influenced by usage cost, compatibility with existing infrastructure and performance on relevant workflows. It describes Alibaba marketing cloud integration for Qwen and collaborative deployment patterns involving DeepSeek and Z.ai. These observations explain why deployment context matters; they do not establish a preferred provider or a permanent service offer.
Privacy, censorship and governance questions
ITPro reports expert concerns about privacy, censorship, bias and uncertainty around training data. Evaluate those issues for the actual route you select. A self-hosted checkpoint and a hosted API can have very different data exposure, moderation behavior and auditability even when their model names match.
For regulated or sensitive workloads, define what data may be sent, where it may travel, who can access logs, how outputs are reviewed and what happens when the model refuses or alters a request. Test politically or culturally sensitive prompts relevant to your users instead of assuming that an English-language safety evaluation predicts behavior in every language.
A practical comparison procedure
- Freeze the candidates. Write down exact repository names, checkpoint revisions, quantization, context limit and license files. “Qwen,” “DeepSeek” or “Llama” alone is not a reproducible test target.
- Define the workload. Assemble representative prompts for reasoning, coding, tool use, multilingual text, images or long documents. Include failure cases and sensitive data-handling scenarios.
- Use a common protocol. Keep prompts, temperature, token limits, tools, retrieval corpus and scoring rules identical. Record the date, hardware and software versions.
- Separate capability from operations. Measure answer quality separately from latency, memory, throughput, failure recovery and operational effort. Do not substitute active-parameter count for a serving benchmark.
- Check independent evidence. Mark every result as vendor-reported, independently reproduced or your own test. Treat leaderboard search snippets as pointers, not complete evidence.
- Audit the deployment path. For local serving, test access controls and update procedures. For an API, document region, retention, training use, uptime commitments and the exact model revision.
- Review licensing with the release. Preserve the license and attribution notices with the deployed artifact, and obtain legal advice when the terms or intended distribution are unclear.
- Choose the smallest model that passes. A model that meets quality and compliance requirements on infrastructure you already operate may be the better production choice, even if another model has a higher headline benchmark score.
Bottom line for “Chinese models vs. Llama”
Use Qwen3 when multilingual or multimodal breadth is central, DeepSeek-R1 when reasoning is the priority, Kimi-K2 when coding and long-context agents dominate, and GLM-4.5 when a generalist workflow combines reasoning, tools and vision. Compare each exact checkpoint with the Llama version you are considering under one documented test protocol. Current public evidence supports promising, sometimes frontier-level capability, but it does not justify a timeless overall winner. License terms, data governance and the practical cost of operating the chosen route are part of the comparison, not afterthoughts.
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