Alibaba Qwen (Qwen3.8): the open family most respected for Chinese, and the two routes open to Hong Kong schools

Short answer: Qwen is the open family most respected for Chinese, and a Hong Kong school has two ways to use it — Qwen3.8-27B on a server inside the school, where no data leaves the campus network, or the Qwen3.8-Max cloud API, which is a cross-border transfer and needs a privacy assessment first.

What this is: one Chinese-language family running both an open and a closed track

Qwen is Alibaba's model family. What most distinguishes it from other Chinese vendors is that each generation ships both a cloud-only flagship and a set of downloadable open weights, with an unusually complete range of sizes — from models that fit on a laptop up to ones that need a data centre.

For a Hong Kong school that structure maps neatly onto two entirely different decisions. Go to the cloud and you get the strongest Chinese performance and the longest context window, at the cost of personal data leaving the territory. Self-host and you buy a graphics card and take on the maintenance, but the whole compliance question disappears. Neither route is better in the abstract; the question is which cost the school prefers to carry.

One caveat worth stating: Qwen's training context leans towards mainland usage. Its Chinese is reliable, but the output may use mainland rather than Hong Kong terminology and may assume mainland grade names and examination systems. Prompts must ask explicitly for Traditional Chinese in Hong Kong conventions, and a teacher must check the result.

The current line-up: one closed flagship, two open sizes, one cheap small model

• Qwen3.8-Max (generally available 3 August 2026, with the 0902 snapshot on 3 September): a 2.4T-parameter mixture-of-experts model activating about 95B per token, with a 1M-token context window and text, image and video input. Pricing is $2 per 1M input tokens and $6 per 1M output tokens, with cached input at about $0.25. The Max endpoint is closed and its weights are not published.
• Qwen3.8-2.4T-A95B (12 August 2026): the open-weight build of the same architecture, but under a bespoke licence — the public release omits image input, and providers above US$50M annual revenue need a commercial licence. At 2.4T parameters it needs institutional hardware.
• Qwen3.8-27B (14 August 2026): a 27.8B dense vision-language model under Apache 2.0, documented to run in 24GB of VRAM. This is the one in the batch best suited to school self-hosting.
• Qwen3.8-Flash (26 August 2026): a small mixture-of-experts model with 125B total and 6B active parameters at $0.15 / $0.47 per 1M tokens, suited to high-volume batch work.
• The Qwen2.5 series (19 September 2024): still the most-used Chinese open generation on Ollama, and the generation qwen2.5:14b in our on-premises suggestion list belongs to.

The full comparison of versions, dates and prices is in the automatically generated series table further down this page.

Strengths: the four that matter most to a school

1. The steadiest Chinese in its open-weight class. Chinese reading comprehension, document summarising, script conversion and understanding of Chinese prompts are all clearly ahead of English-first open models of similar size, which helps most in Chinese, General Studies and Chinese History planning.
2. There is a size that genuinely runs. 24GB of VRAM is one high-end consumer card, so Qwen3.8-27B turns "self-host a competent Chinese model in school" from a theory into a purchase order.
3. Apache 2.0 lets a sponsoring body replicate the deployment. The 27B licence has no user cap, so one set of weights can serve several schools with no additional licence fee.
4. Cloud prices are low, so evaluation is cheap. The flagship at $2 / $6 and Flash at $0.15 / $0.47 mean a teacher can run capability tests, on material containing no personal data, at almost no cost.

Limits: what to say plainly to a principal

• The Max endpoint is a cross-border data transfer. This is the point that matters most. Sending a student's composition or name to Qwen's cloud API moves personal data out of Hong Kong. The school either completes a privacy impact assessment with informed parental consent, or self-hosts instead.
• The open flagship is not Apache 2.0. Qwen3.8-2.4T-A95B carries a bespoke licence, the public build omits image input, and providers above US$50M annual revenue need a commercial licence. School use is unlikely to be affected, but offering a service externally requires reading the terms.
• Terminology needs calibrating. Output often carries mainland vocabulary and assumptions about the mainland education system. That is a context problem rather than a capability one, and the fix is prompt templates plus the school knowledge base.
• Answers on sensitive and political topics are guarded or skewed. On history, politics and social issues, expect answers that are not neutral. It is usable for gathering material in humanities subjects, but should never be the only source.
• The hidden costs of self-hosting remain. A graphics card is the start; power, cooling, backups and version upgrades follow. Without IT staffing headroom, the total may not beat a cloud subscription.

Where each tier fits

Teachers: Chinese-language lesson plans, classical and modern Chinese comparisons, variants of Chinese worksheet questions, summarising long Chinese documents. Qwen3.8-27B is enough for all of this; the flagship is unnecessary.

Students: suitable for Chinese reading comprehension hints and vocabulary explanations, in tutor mode only, where the platform holds it to hints rather than answers and keeps conversations a teacher can review. Questions involving value judgements should be led by a teacher rather than handed to the model.

IT coordinators: confirm you have 24GB of VRAM, then choose between qwen2.5:14b and Qwen3.8-27B. Running both in parallel for a term and measuring latency, concurrent users and teacher satisfaction is the cleanest way to settle on one.

Prompts worth testing

Paste these into a Edor.ai teacher tool or any interface connected to local Qwen weights. The terminology constraint is the important part — without it the output mixes in mainland usage.

You are a Hong Kong primary Chinese-language teacher. Design a 30-minute reading and writing activity on the origins and customs of the Mid-Autumn Festival for Primary 5.
Output: one passage under 350 words, a vocabulary table of six terms with example sentences, four comprehension questions, two writing prompts, and three teacher observation points.
Constraints: write everything in Traditional Chinese using Hong Kong conventions, using Hong Kong grade and material names rather than mainland ones; avoid phrasing typical of mainland textbooks; if any historical date or allusion is uncertain, write "teacher to verify" rather than inventing it.

Below is a Secondary 3 student's Chinese narrative composition. Score Content, Expression and Structure out of 5 each, write two specific comments per criterion, then list three things this student should practise next.
Constraints: write in Traditional Chinese using Hong Kong conventions; do not rewrite the composition, give feedback only; quote the student's own sentences unchanged; point to specific paragraphs in your comments; if you are unsure about Hong Kong Chinese-subject marking conventions, mark it "teacher to verify".
[paste composition]

Availability inside Edor.ai

Qwen is not a native provider. Edor.ai natively supports OpenAI, Azure OpenAI, Anthropic, Poe and local Ollama, and Qwen arrives through the last of those. A school runs Qwen3.8-27B or qwen2.5:14b with Ollama on its own server and sets the provider to local Ollama in the admin panel. Our on-premises suggestion list already includes qwen2.5:14b, so an IT coordinator is not starting from nothing.

This is how a school keeps student data inside the campus network, and with Qwen it also settles the cross-border question. Once self-hosted, conversations reach no external cloud, while four-layer gating, teacher monitoring, the ai_audit_logs trail and knowledge-base retrieval all work as normal with identical features to the cloud version. The data still belongs to the school and can be exported in full at any time. Insisting on the Qwen3.8-Max cloud API instead means accepting a cross-border transfer and completing a privacy assessment first.

A school that wants to go further can fine-tune the open weights on its own Chinese lesson plans and model answers, so the output matches school conventions directly. LoRA, QLoRA and INT4 quantisation are what make that possible on a single consumer GPU, and they are the same techniques taught in the LLM Classroom — our on-premises option and the course cover the same concepts.

Alternatives

• To compare the other Chinese vendors on capability and price, read the DeepSeek, Kimi, Qwen and GLM comparison or see DeepSeek.
• For Apache 2.0 open weights at the 30B mark aimed more at agentic work, see Meta open weights.
• To avoid managing hardware and to get Cantonese oral practice, see the native provider OpenAI.
• To settle the cost and privacy question first, read the open versus closed cost and privacy trade-off and see our security overview.