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There is no single “best” Chinese open-weight model, and no defensible 2026 ranking that places every Qwen, DeepSeek, Kimi or GLM checkpoint against every Llama release. The useful comparison is narrower: choose the exact checkpoint, test it on the work you actually need, verify whether results are independently reproduced, and read that checkpoint’s license before deployment. Chinese families can be highly competitive for particular reasoning, coding, multilingual, multimodal and agentic workloads, while Llama remains an important reference because it is widely deployed and is the base for some distilled DeepSeek variants.
What “Chinese open-weight model” means
Open-weight means that a model’s trained parameters can be downloaded and run, subject to the publisher’s terms. It does not automatically mean that the training data, complete training code, data-cleaning pipeline, evaluation harness or every tool used to create the model is public. “Open source AI” is therefore a broader and often less precise label. Check what is actually released before assuming you can audit, modify or commercially redistribute a system.
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The comparison is also between families rather than single products. Qwen, DeepSeek, Kimi and GLM each contain multiple checkpoints, sizes, modalities and release dates. Llama is another family, not one fixed capability level. A statement such as “DeepSeek beats Llama” is incomplete unless it names the DeepSeek checkpoint, Llama version, benchmark, prompt format, scoring method and test date.
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The following figures come from the Stanford HAI/DigiChina issue brief, whose table describes information current on December 4, 2025. They are family snapshots, not specifications for every later release and not matched estimates of serving cost.
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| Family and dated checkpoint | Reported scale and context | Stated emphasis in the snapshot | License label in that snapshot |
|---|---|---|---|
| Qwen3, first released April 29, 2025; Qwen3-Omni listed September 22, 2025 | 0.6B to 235B parameters across dense and mixture-of-experts models; the largest noted MoE variant activates 22B per token; context up to approximately 128K | Multilingual and multimodal breadth, with both general and specialized checkpoints | Apache 2.0 for Qwen3 |
| DeepSeek-R1, first released January 20, 2025 | 671B total parameters and 37B active parameters in the MoE model; distilled Qwen- and Llama-based variants from 1.5B to 70B | Mathematics and reasoning | MIT for R1 |
| Kimi-K2, dated July 11, 2025 | Approximately 1T total and 32B active parameters in the reported MoE family; context up to approximately 256K | Coding and agentic work | Modified MIT; the brief notes attribution requirements for large-scale commercial deployment |
| GLM-4.5, dated July 28, 2025 | Air: 106B total and 12B active; Base: 355B total and 32B active; a GLM-4.5V vision model is also listed | Generalist reasoning, coding, tool use and vision | MIT for GLM-4.5 |
Parameter totals and active counts describe different things. In an MoE model, active parameters are the experts selected for a token, while the complete weight set still has to be stored or otherwise available to the serving system. Active count can help explain computation per token, but it is not a complete memory, GPU, latency or energy requirement.
How the evidence compares with Llama
Llama is a useful baseline because it has a large deployment ecosystem and because DeepSeek-R1’s distilled releases include Llama-based variants. A surfaced SuperCLUE 2025 Annual Chinese LLM Benchmark result, identified as data from January 29, 2026, says Chinese models occupied the top five positions and names DeepSeek-V3.2-Thinking and GLM-4.7 among the top three; the same result identifies Llama 4 Maverick among overseas models. That search-result text is only a limited, attributed snapshot. It does not provide a controlled, same-version comparison of all Chinese families with Llama, nor does it establish a universal winner.
The Stanford brief describes several family results as “high self-reported.” Vendor numbers can be useful for discovering promising checkpoints, but they should be separated from independently reproduced tests. Record the benchmark version, prompts, tools, sampling settings, scoring rules and date. A ranking without those details can change when a model, evaluator or harness changes.
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As Amanda Brock, CEO of OpenUK, is quoted by ITPro: “Their reviews and ratings show their capacity is moving toward matching the biggest and best of US frontier AI models.” That is an expert observation about momentum, not a substitute for a controlled scorecard. Stanford HAI/DigiChina’s own synthesis is similarly cautious: “The rapid adoption of Chinese open-weight models that are still, if only marginally, outperformed by closed models shows that user uptake does not depend solely on benchmarks.”
Choose by workload, not nationality
Reasoning and mathematics
For theorem-style questions, quantitative analysis and multi-step problem solving, start with a reasoning-oriented checkpoint such as DeepSeek-R1 or a comparable reasoning mode from another family. Measure exact-answer accuracy, error recovery and the time or token budget needed to reach a solution. A long visible chain of reasoning is not itself evidence of correctness; use a held-out test set and verify answers with a calculator, interpreter or domain checker where possible.
Coding and software agents
For code generation, repository edits and tool-using agents, evaluate more than pass@1 on short snippets. Test compilation, unit tests, patch quality, secure dependency choices, tool-call syntax and behavior over several turns. Kimi-K2’s stated focus makes it a candidate for this class of work, while GLM and Qwen checkpoints may be preferable when coding is combined with vision, multilingual documentation or broader assistant duties. The winning model is the one that completes your workflow reliably, not the one with the highest isolated leaderboard position.
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Multilingual and regional language work
Build a test set using the scripts, dialects, names, legal terminology and customer vocabulary that matter to your users. A family-level claim of multilingual coverage does not guarantee equal quality in every language. Check translation direction, refusal behavior, romanization, formatting and susceptibility to culturally specific misunderstandings. Include low-resource examples rather than testing only English and Mandarin.
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Use a multimodal checkpoint only when the released interface supports the modality and task you require. Qwen3-Omni and GLM-4.5V are named multimodal or vision releases in the dated snapshot, but their capabilities should be evaluated at the checkpoint level. Test image resolution, document layouts, charts, handwriting and audio conditions separately; text-only benchmark scores cannot establish visual or spoken accuracy.
Long documents and tool use
Advertised context length is an upper bound, not proof of faithful recall. Create tests with information at the beginning, middle and end of documents, then measure retrieval accuracy, citation placement and degradation as context grows. For agents, test permission boundaries, tool-selection errors, retries and what happens when a tool returns malformed or adversarial data.
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Licensing is a model-by-model decision
Do not infer a license for an entire laboratory from one release. The Stanford snapshot labels Qwen3 Apache 2.0, DeepSeek-R1 MIT, Kimi-K2 Modified MIT and GLM-4.5 MIT. A newer Alibaba Cloud license list, last updated October 11, 2026, labels DeepSeek-V4-Pro MIT, Kimi-K2.5 and Kimi-K2.6 Modified MIT, and GLM-5 and GLM-5.1 MIT. Those entries apply to the named releases only.
| Deployment question | What to verify in the exact checkpoint license |
|---|---|
| Commercial use | Whether commercial use, resale, hosted services and internal production are permitted |
| Attribution | Required notices, paper or model-card credit, and whether large-scale deployment has extra conditions |
| Modification and redistribution | Whether derivatives, quantized weights and merged models can be distributed, and under which notice |
| Acceptable-use and jurisdiction terms | Prohibited applications, export restrictions, governing law and any additional policy document incorporated by reference |
Keep the downloaded license, model card and commit identifier with your deployment record. A later checkpoint can change terms even when its family name is unchanged.
Hosted API or self-hosted weights?
Hosted access
A hosted endpoint minimizes infrastructure work and can provide a larger model than your team could load locally. It also means prompts, outputs, logs and operational metadata travel through the provider’s route. Verify the hosting region, retention policy, training-use policy, encryption, subprocessors, outage behavior and contractual remedies. “Open-weight” does not make a hosted endpoint automatically private or locally controlled.
Self-hosting
Self-hosting can keep traffic inside an organization’s network and permits tighter control over model version, quantization, logging and upgrades. It transfers responsibility for hardware, drivers, batching, observability, security patches and incident response. Total parameter count, active parameter count and context window alone do not tell you whether your chosen hardware can serve the model at an acceptable latency. Obtain a measured profile for your quantization, batch size, context length and concurrency before committing.
Stanford HAI/DigiChina describes usage cost, existing infrastructure and workflow performance as adoption factors, and notes cloud-integration and collaborative deployment patterns around several Chinese labs. Those observations explain why deployment architecture matters, but they do not establish a preferred provider or a current service offer.
Privacy, censorship and governance checks
ITPro reports expert concerns about privacy, censorship, bias and uncertainty around training data. Treat these as review areas rather than assumptions about every checkpoint. Run representative prompts through the actual route you will use, document refusals and regional-policy behavior, and have legal or compliance staff assess data residency and sector rules. For sensitive workloads, remove unnecessary personal data, define retention limits and separate model evaluation from production records.
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Assaf Morag, a cybersecurity researcher at Flare, is quoted by ITPro saying: “Based on the benchmarks and independent evaluations available today, many of these models are performing at a level comparable to other leading frontier AI models while often offering lower deployment costs and more open access.” The statement is an expert assessment, not a universal verified cost result. The reviewed evidence does not establish apples-to-apples prices, hardware requirements, power use or latency for current Chinese releases versus Llama. An estimate that some models cost 60%–90% less to run lacks a defined matched workload and pricing method, so it should not be used as a general budget assumption.
A practical selection process
- Define the job. Write down languages, modalities, context size, tool calls, accuracy targets, latency limits and data restrictions.
- Choose exact checkpoints. Record the full model name, release date, parameter variant, quantization and serving software. Do not test only family nicknames.
- Build a representative evaluation set. Include normal, difficult, multilingual, adversarial and failure-recovery cases. Keep a held-out portion for final selection.
- Run controlled comparisons. Use the same prompts, temperature, tool definitions, context and scoring process for each candidate, including the Llama baseline.
- Separate evidence types. Label vendor-reported, leaderboard, independent and your own production-like results. Record benchmark and harness versions.
- Read the license. Check commercial use, attribution, redistribution, acceptable-use language and obligations for the exact weights.
- Measure operations. Test cold start, throughput, tail latency, memory use, failure rates, observability and rollback with your actual hardware or endpoint.
- Review governance. Confirm data route, retention, residency, access controls, abuse monitoring and incident procedures before a pilot.
- Pilot and monitor. Start with a bounded workflow, compare quality and cost against the incumbent, and keep a rollback path when a new checkpoint changes behavior.
Common mistakes and how to avoid them
- Equating open-weight with fully open source: inspect the released artifacts and license instead of assuming training transparency.
- Comparing parameter totals as if they were speed: separate total weights, active experts, memory, context cache and measured throughput.
- Quoting a leaderboard as a universal ranking: name the benchmark, date, versions and whether the result was independently reproduced.
- Generalizing a license: verify every checkpoint, including later revisions with similar names.
- Assuming family-level multilingual or multimodal claims transfer everywhere: test the languages, scripts, images and audio your users actually submit.
- Calling a hosted endpoint private because weights are downloadable: evaluate the provider’s data path and contract separately from the model license.
- Buying hardware from a parameter count alone: require a workload-specific serving test before purchasing infrastructure.
Bottom line for a Llama comparison
Use Llama as a baseline, not a verdict. Qwen, DeepSeek, Kimi and GLM offer distinct checkpoints whose dated snapshots emphasize different workloads, and some can be excellent choices when their license, language coverage and deployment route fit your requirements. The defensible decision comes from an exact-version evaluation, a license review and an operational pilot—not from a family label, an undated benchmark claim or a generic assertion that one country’s models are ahead.
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