While DeepSeek grabs headlines, Alibaba’s Qwen has quietly built the most complete model family in artificial intelligence — from tiny on-device models to a flagship that, in May 2026, became the highest-ranked Chinese model ever on independent intelligence benchmarks. If DeepSeek is the disruptor, Qwen is the platform. Here’s what it is and why it matters.
Punti chiave
- Qwen3.7 Max (May 2026) scored 56.6 on the Artificial Analysis Intelligence Index — top 10 globally, the highest ever for a Chinese model.
- The broadest model family in AI: from 0.5B on-device models to 1T-parameter flagships, open and proprietary.
- 1M-token context on the flagship, with extended thinking on by default.
- Open-weight leader: Alibaba rivals Meta as the biggest contributor of permissively-licensed models.
- Caveat: the top Max models are proprietary and API-only; data residency and moderation caveats apply to the hosted service.
Who is Qwen
Qwen (短 for “Tongyi Qianwen,” 通义千问) is the large-language-model family from Alibaba Cloud, China’s largest cloud provider. Unlike DeepSeek (a focused lab) or Moonshot (a startup), Qwen has the full weight of a trillion-dollar conglomerate behind it: Alibaba Cloud’s infrastructure, e-commerce data scale, and a mandate to make Qwen the default AI layer across Alibaba’s empire and the open-source world.
That backing shows up as breadth. Qwen isn’t one model — it’s a sprawling family covering text, vision, audio, code, math, and embeddings, at sizes from sub-billion-parameter models that run on a phone to trillion-parameter flagships. Alibaba has open-sourced an enormous share of it, making Qwen, alongside Meta’s Llama, one of the two pillars of the global open-weight ecosystem.
The flagship: Qwen3.7 Max
Released on May 19, 2026, Qwen3.7 Max is Alibaba’s most capable model and a genuine milestone for Chinese AI:
- 56.6 on the Artificial Analysis Intelligence Index — a top-10 global placement and the highest any Chinese model has achieved on that independent benchmark.
- 92.4 on GPQA Diamond (graduate-level science) and 97.1 on HMMT February 2026 (competition math) — the highest in its comparison group.
- 1M-token context with extended thinking enabled by default.
- Pricing of $2.50 input / $7.50 output per million tokens, with cached input at $0.25.
The predecessor, Qwen3.6 Max (April 2026), remains available at lower cost (~$1.04/$6.24). Both are proprietary and served through Alibaba Cloud Model Studio, OpenRouter, and Together AI.
The real story: the open-weight family
The Max flagship grabs benchmark headlines, but Qwen’s strategic weapon is its open-weight catalogue. Alibaba has released dozens of Qwen models under permissive licenses (mostly Apache 2.0), spanning:
- Dense and MoE text models from 0.5B to hundreds of billions of parameters.
- Qwen-VL vision-language models.
- Qwen-Coder models tuned for software engineering.
- Qwen-Audio and embedding models.
This matters because it makes Qwen the foundation for thousands of downstream products and fine-tunes worldwide. If you’ve used a Chinese-made open-weight model that wasn’t DeepSeek, it was very likely a Qwen derivative. For builders who want to own their stack — fine-tune, self-host, no API dependency — Qwen offers more size/capability options than any competitor.
Where Qwen wins
1. Family breadth — edge to frontier
No one else covers the full spectrum this well. You can prototype on the 1T Max API, then deploy a fine-tuned 7B open model on your own hardware, staying inside one model family with consistent behavior and tokenization. That coherence is genuinely valuable for production teams.
2. Open-weight depth
Alibaba’s commitment to open weights rivals Meta’s. For anyone building on self-hosted models, Qwen’s catalogue is the deepest menu available — and the licenses are commercial-friendly.
3. Multilingual and multimodal strength
Trained on Alibaba’s vast multilingual and e-commerce data, Qwen is exceptionally strong across Chinese, English, and dozens of other languages, plus vision and audio. For non-English and multimodal workloads, it’s often the best open option.
Where Qwen loses — the honest caveats
1. The best models are closed
The headline-grabbing Qwen3.7 Max is proprietary and API-only. If your reason for choosing Chinese AI is openness, the top of the Qwen range doesn’t deliver it — you drop to the (still excellent) open models a tier down.
2. Hosted-API data and moderation caveats
The Model Studio API runs on Alibaba Cloud infrastructure in China, with the same data-residency and content-moderation considerations as other China-hosted services. Self-hosting the open weights avoids this.
3. Fragmentation
The family’s breadth is also a weakness: with so many models and versions, picking the right Qwen for a task takes research. There’s no single “just use this one” answer the way there is with a one-model lab.
Qwen vs the field
| Dimensione | Qwen | DeepSeek V4 | Llama (Meta) | GPT-5.5 |
|---|---|---|---|---|
| Model family breadth | Widest (edge→frontier) | Narrow | Wide | Narrow |
| Open-weight depth | Deepest | Strong (MIT) | Deep | None |
| Top-end intelligence | Top-10 global | Eccellente | Behind frontier | Frontier |
| Multilingual/multimodal | Eccellente | Buono | Buono | Eccellente |
| Best model open? | No (Max closed) | Sì | Sì | No |
Pros and cons
Qwen pros
- Broadest model family in AI — edge to frontier
- Deepest open-weight catalogue (Apache 2.0)
- Flagship cracked the global top 10 on intelligence
- Outstanding multilingual and multimodal coverage
- Backed by Alibaba Cloud’s scale and reliability
Qwen cons
- The best (Max) models are proprietary, API-only
- Hosted API carries China data-residency caveats
- Family fragmentation — hard to pick the right model
- Content moderation on the hosted service
How to access Qwen
- Flagship (Max): Alibaba Cloud Model Studio, OpenRouter, Together AI — API only.
- Open models: download Qwen3, Qwen-Coder, Qwen-VL, etc. from Hugging Face / ModelScope and self-host or fine-tune.
- Chat: the Qwen Chat web app for casual use.
Which Qwen should you actually run on your hardware?
Qwen’s biggest practical advantage is also its biggest source of confusion: there are too many models to choose from. The honest shortcut is to start from the hardware you own and work backwards. Almost every Qwen size ships in the GGUF format that Ollama, LM Studio and llama.cpp expect, and the Q4_K_M quantization is the right default for nearly everyone — it shrinks a model roughly threefold to fourfold versus full precision for only a small quality loss. Match your machine to a tier below and pick the largest model that fits with room to spare for context.
| Your hardware | Best Qwen to run (Q4_K_M) | What to expect |
|---|---|---|
| Phone / Raspberry Pi (4–8 GB RAM) | 0.6B–4B dense (e.g. a ~1.7B small-series model) | Offline chat and summarizing; a ~0.6B model is around 500 MB and runs at roughly 15–25 tokens/sec |
| 8 GB GPU (RTX 3060 / 4060) | 8B dense | Snappy general assistant, roughly 40+ tokens/sec — the entry point for serious use |
| 12 GB GPU (RTX 3060 12GB / 5070) | 14B dense | Noticeably better reasoning; needs about 11 GB total at 8K context |
| 24 GB GPU (RTX 3090 / 4090 / 5090) | 32B dense, or a 30B-class MoE (30B-A3B) | The local sweet spot; near-frontier quality on a single consumer card |
| 48 GB+ or Apple Silicon 64 GB+ | A large MoE such as the 235B-A22B flagship | The most capable models you can self-host without a server |
Two rules keep you out of trouble. First, leave headroom: a model that exactly fills your VRAM will overflow once you load a long prompt, so size down one tier if you work with big contexts. Second, when the open-weight family offers a Mixture-of-Experts (MoE) option at your tier, take it — a 30B-A3B model delivers roughly the quality of a 30B dense model while costing about as much to run as a 3B one, because only about 3 billion parameters activate per token. The dense models are simpler and slightly more predictable; the MoE models are the smarter bet when you want maximum capability per gigabyte.
Domande frequenti
Is Qwen open source?
Partly. Most of the Qwen family — including very capable models — is released under Apache 2.0 open weights. The top-tier Qwen-Max flagship models are proprietary and API-only. So “Qwen is open source” is true for the family but not for the absolute best model.
Is Qwen better than DeepSeek?
Different strengths. DeepSeek wins on price-performance and ships its very best model as open weights. Qwen wins on family breadth, multimodal/multilingual coverage, and top-end benchmark intelligence (Qwen3.7 Max ranks higher globally). For self-hosting flexibility, Qwen’s catalogue is deeper; for one cheap excellent open model, DeepSeek is simpler.
What does “Qwen” mean?
It’s short for Tongyi Qianwen (通义千问), roughly “truth from a thousand questions” — Alibaba’s brand for its LLM family.
Can I use Qwen commercially?
Yes — the open models are mostly Apache 2.0, which permits commercial use. The Max API has standard commercial terms. Always check the specific model’s license card on Hugging Face.
Does Qwen run on a phone?
The smallest Qwen models (sub-billion parameters) are designed for on-device use, including phones and edge hardware. That’s part of what makes the family unusually complete.
Is Qwen safe to use?
For non-sensitive work, yes. The open-weight Qwen models can be self-hosted, keeping your data entirely under your control — the safest option. The hosted Qwen API runs on Alibaba Cloud in China, with the usual data-residency and content-moderation considerations, so for sensitive or regulated data, self-host the open weights or use a Western provider instead.
Is Qwen free to use?
Mostly, yes — the large open-weight Qwen models are free to download and run under permissive (mostly Apache 2.0) licenses; you pay only for your own compute. The top-tier Qwen-Max flagship is a paid API. There’s also a free Qwen Chat web app for casual use.
How much VRAM do I need to run Qwen locally?
At the standard Q4_K_M quantization, plan on roughly 6 GB of VRAM for an 8B model, about 11 GB for a 14B, and around 20–22 GB for a 30–32B model — always with a few extra gigabytes free for your context window. An 8 GB card is the realistic entry point for a fast, useful assistant; 24 GB (an RTX 3090, 4090 or 5090) hits the local sweet spot. Below that, the smallest Qwen models still run on a phone or a laptop’s CPU, just more slowly.
Which Qwen model is best for coding?
For raw capability, the dedicated Qwen3-Coder flagship (a 480B-parameter Mixture-of-Experts model) is the strongest option and trades blows with leading closed coding models — but at that scale you realistically use it through an API, not on your own machine. For local coding, the Qwen3-Coder 30B-A3B model runs well at Q4_K_M on a single 24 GB GPU (around 19 GB on disk) and is the practical default. If you only have an 8–10 GB card or a 16 GB laptop, drop to a small general Qwen3 (4B or 8B) or the older Qwen2.5-Coder-7B, which is still solid for code completion. Pick the largest coder that fits your hardware rather than the headline model.
Should I run a dense Qwen model or a Mixture-of-Experts one?
Choose dense for small sizes and maximum simplicity — they are easy to quantize, predictable, and well supported everywhere. Choose MoE when you want the most capability per gigabyte of memory: a Mixture-of-Experts model activates only a fraction of its parameters per token, so you get the quality of a much larger model at the inference cost of a small one. On a 24 GB card, the 30B-A3B MoE is often the smartest single choice Qwen offers.
Conclusione
Qwen is the most underrated force in Chinese AI. It lacks DeepSeek’s disruptive pricing narrative, but it offers something arguably more valuable: the broadest, deepest, most production-ready model family in existence, anchored by a flagship that now competes with the global frontier and a long tail of open models that power thousands of products worldwide.
If you want one AI vendor that can take you from a phone-sized model to a top-10 frontier flagship — open where it counts, multimodal, multilingual, and backed by Alibaba Cloud’s reliability — Qwen is the most complete answer available in 2026. Just know that the very best Qwen is closed, and the hosted API carries the standard China-jurisdiction caveats. For most of what most teams build, the open Qwen models are more than enough.