Qwen 2.5 arrived in late 2024 and spent 2025–2026 quietly becoming the benchmark model for open-weight AI — matching or beating GPT-4o on coding, math, and multilingual tasks while being fully open-weight and runnable on your own hardware. If you have not tried it seriously, here is how to get started.
What changed in 2026
- Qwen 2.5 72B became a baseline. In mid-2026 benchmarks, 72B-Instruct rivals GPT-4o on MMLU, HumanEval, and multilingual NLU — the first open-weight model family to do so consistently.
- Qwen-Coder 32B hit a new ceiling for code. Outperforms most closed models on SWE-bench for code editing and debugging at 32B parameters — runnable on a single A100 or H100 80GB.
- Alibaba Cloud expanded API regions. European and US-West endpoints are now generally available, reducing latency concerns for non-APAC users.
- Quantized versions improved. GGUF Q4_K_M and Q5_K_M quantizations of the 7B and 14B models run well on 8–16 GB VRAM laptops with acceptable quality loss.
The Qwen model lineup
| Model |
Parameters |
Best for |
| Qwen 2.5 0.5B / 1.5B |
Tiny |
Edge, on-device, classification |
| Qwen 2.5 7B |
Small |
Fast local inference, basic tasks |
| Qwen 2.5 14B |
Medium |
Balanced quality + speed on 1 GPU |
| Qwen 2.5 32B |
Large |
Strong general use, 1–2 GPUs |
| Qwen 2.5 72B |
XL |
Near-frontier quality, multi-GPU |
| Qwen-Coder 7B / 32B |
Code-specialized |
Code generation, review, debugging |
| Qwen-VL |
Multimodal |
Image + text understanding |
How to access Qwen
Option 1 — Qwen Chat (web, free)
Visit chat.qwenlm.ai. No setup. Free access to Qwen 2.5 72B Instruct with rate limits. Best for trying the model before committing.
Option 2 — Alibaba Cloud API (DashScope)
- Create an Alibaba Cloud account at
aliyun.com.
- Enable the DashScope service and generate an API key.
- Use the OpenAI-compatible endpoint:
POST https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions
Authorization: Bearer YOUR_API_KEY
Model: qwen-plus (or qwen-turbo / qwen-max)
Pricing is consumption-based — roughly $0.002–$0.006 per 1K tokens depending on the variant, significantly cheaper than GPT-4o at comparable capability.
Option 3 — Local via Ollama
ollama pull qwen2.5:7b
ollama run qwen2.5:7b
For the 32B model: ollama pull qwen2.5:32b — requires ~20 GB RAM/VRAM.
For Qwen-Coder: ollama pull qwen2.5-coder:32b.
Option 4 — Hugging Face
All models are on Qwen/Qwen2.5-72B-Instruct etc. Use with transformers or vllm for hosted inference.
How to start
- Clarify your task. Qwen 2.5 follows instructions closely — be specific in your system prompt about format, length, and language.
- Set the language explicitly. For multilingual tasks, specify: "Respond in Arabic" or include the language in the system role.
- Use Qwen-Coder for code tasks. The base model is strong but the coder variant is measurably better on code generation and review.
- Benchmark against your current tool. Run 10–20 real prompts from your workflow and compare output quality — do not rely on published benchmarks alone.
Common mistakes
Running 72B locally on inadequate hardware. You need 48 GB+ VRAM for full precision, or 40 GB+ for Q4 quantization. Underspecced hardware produces slow, degraded output. Use the API instead.
Ignoring the system prompt. Qwen models are instruction-tuned; they respond significantly better with a clear system prompt than as raw completers.
Treating it like a Western model. Qwen's training data is heavily Chinese-language — it may handle Chinese idioms and cultural references in ways that surprise English-only users. Verify output in multilingual deployments.
Not using the coder variant for coding. The base model codes well, but Qwen-Coder is a separate fine-tune with measurably higher benchmark scores on code tasks.
What to skip
- Tiny models (0.5B, 1.5B) for complex tasks — they are for classification and edge use; use 7B+ for anything requiring reasoning.
- DashScope if you have latency requirements in the US/EU — check ping to the endpoint and test with your use case before committing to production.
- Qwen-VL for pure text tasks — it is larger and slower than the text models; only use the multimodal variant when you actually have images to process.
FAQ
Is Qwen really as good as GPT-4o?
On coding and multilingual benchmarks, Qwen 2.5 72B is within a few points. On creative writing and nuanced English instruction-following, GPT-4o and Claude still lead. Use your own eval.
Is Qwen open-source?
The weights are publicly available under a permissive license for most sizes. Commercial use is allowed; check the license for each model variant at HuggingFace.
What languages does Qwen support?
Chinese, English, French, Spanish, Arabic, Japanese, Korean, German, and 22+ others. Chinese and English are strongest.
Can I fine-tune Qwen?
Yes. Use standard LoRA / QLoRA fine-tuning with the Hugging Face trl library. The instruction-tuned base is a solid starting point.
Where to go next
See How to use Mistral in 2026, How to use Llama in 2026, and ChatGPT vs Claude vs Gemini vs Grok in 2026.