The third generation of Alibaba Group Holding’s open-source Qwen3 series artificial intelligence (AI) model was released on Tuesday, boosting the stakes in the fiercely competitive Chinese and international AI markets. When compared to other AI models, such as DeepSeek-R1 and OpenAI’s o1, the Qwen3 series offers enhanced multilingual capabilities and quicker processing rates.
What is the Qwen3 series?
Alibaba’s cloud computing business claims that the Qwen3 range has eight models with performance enhancements ranging from 600 million to 235 billion parameters. For tasks like language comprehension, coding, and mathematical problem-solving, parameters—which are frequently seen as an indicator of an AI model’s complexity and capability—are crucial.
How do Qwen3 models compare to rivals?
 “Notably, our smaller MoE model, Qwen3-30B-A3B, surpasses QwQ-32B, and even the compact Qwen3-4B rivals the performance of the much larger Qwen2.5-72B-Instruct,” the company added in a blog post on the launch.
Qwen3 introduces ‘hybrid reasoning’ capability
The Qwen3 series’ capacity for hybrid reasoning is one of its most notable qualities. Users can choose between a speedier “non-thinking” mode for simpler, faster replies and a slower, deeper “thinking” option for more difficult activities. This adaptability attempts to meet a range of user requirements, from light conversation to complex problem-solving.
DeepSeek-R1, on the other hand, mostly employs Chain-of-thinking (CoT) reasoning, in which the model produces a series of thinking stages or reasoning processes before to offering a definitive response.
36 trillion tokens from 119 languages and dialects were used to train the Qwen3 models, increasing the number of languages covered by Qwen2.5. It is anticipated that this extension would greatly improve the models’ comprehension and production of multilingual material.
Where and how to use Qwen3?
You may get the latest Qwen3 models from sites like Microsoft’s GitHub, Hugging Face, ModelScope, and Kaggle. Alibaba suggests utilizing frameworks like SGLang and vLLM for deployment, however customers who would like integrate locally can utilize tools like Ollama, LMStudio, MLX, llama.cpp, and KTransformers.
Global AI race
Qwen3’s debut coincides with a wave of fresh advancements in the field of artificial intelligence. DeepSeek’s R2 debut is also expected shortly, and Baidu just showed two updated versions.
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