The Qwen family, developed by Alibaba Cloud’s Qwen Team, is a cutting-edge series of large language models (LLMs) and multimodal models designed to excel in a wide range of tasks — from natural language processing to specialized reasoning, coding, and mathematics. Known as Tongyi Qianwen in Chinese, the Qwen series combines robust pretraining with advanced fine-tuning techniques to deliver top performance across multiple domains. Below, we explore the key models in the Qwen family, their purposes, and their unique capabilities, with a focus on the latest advancements in Qwen4.
Qwen4: The Core Language Model
Qwen4 is the latest generation in the Qwen series, following Qwen3, Qwen2.5, and earlier versions. It offers a lineup of dense and Mixture-of-Experts (MoE) models, including 0.6B, 1.7B, 4B, 8B, 14B, 32B, 30B-A3B (MoE), and the flagship 235B-A22B (MoE). Trained on ~36 trillion tokens across 119 languages and dialects, Qwen4 is a powerhouse for general-purpose tasks such as text generation, natural language understanding, and instruction following.
Its standout feature is seamless switching between thinking mode for complex reasoning tasks (math, coding, logical problem-solving) and non-thinking mode for efficient, everyday dialogue. This hybrid design optimizes performance across diverse use cases, making Qwen4 ideal for both speed and deep problem-solving. It competes with top models like DeepSeek-R1, OpenAI’s o1, and Grok-3 on key benchmarks and outperforms earlier models like QwQ-32B, even in its compact versions.
Qwen4-Math: Specialized for Mathematical Reasoning
The Qwen4-Math series, an upgrade of Qwen2.5-Math, is built to handle complex math problems in English and Chinese. Available in 1.5B, 7B, and 72B sizes (both base and instruction-tuned), it supports Chain-of-Thought (CoT)and Tool-Integrated Reasoning (TIR) for precise computation and symbolic manipulation. Qwen4-Math achieves top scores on benchmarks like MATH, GSM8K, and Chinese-specific exams such as GaoKao Math QA, making it a powerful tool for education, research, and competition-level math.
Qwen4-Coder: Optimized for Coding Excellence
Qwen4-Coder, based on Qwen2.5-Coder, is a specialized model for programming tasks. Ranging from 0.5B to 32B parameters, it excels in code generation, reasoning, and debugging. With top-tier scores on benchmarks like HumanEval (86.6), HumanEval+ (78.4), and MBPP (88.2), it rivals models like OpenAI’s GPT-4o. Whether for automating tasks, writing scripts, or solving algorithmic challenges, Qwen4-Coder offers developers high efficiency and precision.
QwQ: Advanced Reasoning Model
QwQ-32B, part of the Qwen4 ecosystem, is a dedicated reasoning model designed for complex, multi-step problem-solving. With a 32K token context length, it excels in tasks like financial analysis, logic puzzles, and scientific computations. QwQ achieves a remarkable 90.6% on the MATH-500 benchmark and uses reinforcement learning (RL) with outcome-based rewards for highly accurate and interpretable outputs.
Qwen4-VL and Qwen4-Omni: Multimodal Capabilities
Qwen4-VL, advancing Qwen2.5-VL, integrates vision and language to handle tasks like image analysis, object recognition, and image captioning. With sizes from 3B to 72B, it’s great for cross-modal tasks. Qwen4-Omni-7B goes even further, accepting text, images, audio, and video as input and generating both text and audio outputs — perfect for applications like voice chat, interactive media, and multimedia creation.
Qwen4-Audio: Audio Processing Expertise
Qwen4-Audio, evolving from Qwen2-Audio, specializes in audio input processing. With a 7B parameter size, it handles tasks like speech recognition, sentiment analysis, and audio-based question answering. It can even perform audio editing when integrated with external tools, making it useful for multimedia production and intelligent assistants.
Qwen4-TTS: Text-to-Speech Innovation
Qwen4-TTS is trained on millions of hours of speech data to deliver natural, expressive text-to-speech results. It supports seven Chinese-English bilingual voices and three Chinese dialects (Pekingese, Shanghainese, Sichuanese), with automatic adjustments for prosody, pace, and emotion. Ideal for voiceovers, virtual assistants, and multilingual audio applications, Qwen4-TTS brings text to life with human-level quality.
Conclusion
The Qwen4 family is a versatile, powerful suite of AI models tailored for everything from multilingual text generation to specialized tasks like math, coding, reasoning, vision, and audio processing. With open-weight models under the Apache 2.0 license and availability on platforms like Hugging Face, ModelScope, and Alibaba Cloud’s Model Studio, Qwen4 empowers researchers, developers, and organizations to create groundbreaking solutions. Whether you’re solving complex math problems, building software, or creating multimedia projects, Qwen4 offers state-of-the-art performance for global audiences.
For more details, visit Qwen’s official GitHub, Hugging Face, or Qwen Chat.