Qwen 4 LLM chat

Qwen 4

Qwen 4 LLM – the latest milestone in their Qwen series of large language models (LLMs). This release has sparked excitement across the AI community, offering a powerful, versatile, and open-weight model family that pushes the boundaries of what LLMs can achieve. With groundbreaking advancements in reasoning, multilingual support, and a unique dual-mode architecture, Qwen 4 is poised to redefine how developers, researchers, and businesses approach AI-driven solutions.

What is Qwen 4 LLM?

Qwen 4, developed by Alibaba Cloud’s Qwen team, is a comprehensive suite of large language models designed to cater to a wide range of use cases, from casual dialogue to complex reasoning tasks. The model family includes both dense and Mixture-of-Experts (MoE) variants, ranging from lightweight models with 0.6 billion parameters to the flagship Qwen 4-235B-A22B, which boasts 235 billion total parameters with 22 billion activated per token. Released under the Apache 2.0 license, Qwen 4 is open-weight, making it accessible for both research and commercial use.

What sets Qwen 4 apart is its hybrid architecture, which seamlessly integrates two distinct operational modes: 

  • Thinking Mode for deep, step-by-step reasoning and 
  • Non-Thinking Mode for fast, efficient responses.

This dual-mode approach, combined with extensive training on 36 trillion tokens across 119 languages and dialects, positions Qwen 4 as a highly flexible and powerful tool for global AI applications.

Key Features of Qwen 4 LLM

  1. Dual-Mode Operation: Thinking and Non-Thinking Modes
    Qwen 4 introduces a novel “thinking budget” feature, allowing users to toggle between Thinking Mode for complex tasks like coding, math, and logical reasoning, and Non-Thinking Mode for quick, conversational responses. This eliminates the need to juggle multiple models for different tasks, streamlining workflows and optimizing performance. For example, when solving a math problem, Qwen 4 can produce a detailed chain-of-thought (CoT) explanation wrapped in <think> tags, ensuring clarity and accuracy. For casual queries, it switches to Non-Thinking Mode for rapid, concise replies. This flexibility is controlled via /think and /no_think tokens, with Thinking Mode enabled by default.
  2. Impressive Model Range and Efficiency
    Qwen 4 offers a diverse lineup of models, including dense models (0.6B, 1.7B, 4B, 8B, 14B, and 32B parameters) and MoE models (30B-A3B and 235B-A22B). The MoE architecture is particularly noteworthy, as it activates only a fraction of parameters per token, delivering high performance with lower computational costs. For instance, the Qwen 4-30B-A3B model, with just 3 billion activated parameters, outperforms larger dense models like QwQ-32B, while the Qwen 4-4B rivals the performance of Qwen 2.5-72B-Instruct. This efficiency makes Qwen 4 accessible for deployment on consumer hardware, from laptops to high-end GPUs.
  3. Multilingual Mastery
    Trained on a massive dataset spanning 119 languages and dialects, Qwen 4 excels in multilingual tasks such as translation, question-answering, and instruction-following. Whether you’re working in Chinese, English, or less common dialects, Qwen 4 delivers robust performance, making it ideal for global applications. This multilingual capability is enhanced by its ability to handle long-context inputs, with larger models supporting up to 128K tokens and smaller ones up to 32K tokens.
  4. Advanced Reasoning and Tool Use
    Qwen 4’s reasoning capabilities are a standout feature, competing with top-tier models like DeepSeek-R1, o1, and Gemini-2.5-Pro in benchmarks for coding, mathematics, and general reasoning. Its four-stage post-training pipeline—Long Chain-of-Thought Cold Start, Reasoning-Based Reinforcement Learning, Thinking Mode Fusion, and General RL—ensures robust problem-solving skills. Additionally, Qwen 4 supports the Model Context Protocol (MCP) for seamless integration with external tools, APIs, and databases via the Qwen-Agent framework. This makes it a powerful choice for agentic workflows, such as automating code execution or querying real-time data.
  5. Open-Source Accessibility
    Qwen 4’s open-weight models are available on platforms like Hugging Face, ModelScope, and Ollama, enabling developers to fine-tune and deploy them locally with tools like vLLM and llama.cpp. The Apache 2.0 license allows for commercial use, modification, and distribution, democratizing access to state-of-the-art AI. Smaller models like Qwen 4-0.6B and Qwen 4-1.7B are particularly suited for resource-constrained environments, while larger models like Qwen 4-32B cater to enterprise-grade applications.

How Qwen 4 Was Built

Qwen 4’s capabilities stem from a sophisticated training process. The models underwent a three-stage pretraining phase with 36 trillion tokens, starting with 30 trillion tokens at a 4K context length to build foundational language skills. The second stage refined the dataset with knowledge-intensive content like STEM and coding, using an additional 5 trillion tokens. The final stage extended the context window to 32K tokens with high-quality, long-context data. Synthetic data generated by Qwen 2.5-Math and Qwen 2.5-Coder further enriched the dataset with math and code examples.The post-training pipeline is equally impressive, featuring four stages:

  • Long Chain-of-Thought Cold Start: Fine-tuning on 300,000 CoT examples for math, coding, and logic.
  • Reasoning-Based Reinforcement Learning: Optimizing problem-solving strategies.
  • Thinking Mode Fusion: Balancing deep reasoning with fast responses.
  • General RL: Enhancing instruction-following and agentic capabilities.

This pipeline ensures Qwen 4 can handle both complex reasoning and rapid dialogue within a single model, a significant departure from the one-size-fits-all approach of earlier LLMs.

Performance and Benchmarks

Qwen 4’s flagship model, Qwen 4-235B-A22B, achieves competitive results against leading models like DeepSeek-R1, o1, o3-mini, Grok-3, and Gemini-2.5-Pro in coding, math, and general capabilities. The smaller Qwen 4-30B-A3B outperforms QwQ-32B despite using only 10% of the activated parameters, while the compact Qwen 4-4B matches the performance of the much larger Qwen 2.5-72B-Instruct. These results highlight Qwen 4’s efficiency and power, particularly in STEM and reasoning tasks.

Applications of Qwen 4 LLM

Qwen 4’s versatility makes it suitable for a wide range of applications:

  • Development: The Qwen 4-32B model excels at code generation and debugging, rivaling GPT-4o in accuracy. Developers can use it to automate workflows or build AI-driven applications.
  • Research: With its strong reasoning and multilingual capabilities, Qwen 4 is ideal for academic research, from solving complex math problems to analyzing multilingual datasets.
  • Business: Enterprises can leverage Qwen 4 for tasks like content creation, translation, and customer service automation, benefiting from its cost-effective MoE models and long-context support.
  • Agentic Workflows: Integration with Qwen-Agent and MCP enables Qwen 4 to power intelligent agents that interact with tools, APIs, and real-time data.

How to Get Started with Qwen 4

Running Qwen 4 locally is straightforward, thanks to its compatibility with frameworks like Ollama and vLLM. For example:

  • Ollama: Use ollama run Qwen/Qwen4-8B to download and interact with the model via a local server at http://localhost:11434.
  • vLLM: Serve larger models like Qwen 4-235B-A22B with commands like vllm serve Qwen/Qwen4-235B-A22B-FP8 –tensor-parallel-size 4 for high-throughput inference.
  • Hugging Face Transformers: Load Qwen 4-32B with Python for custom applications, ensuring you use transformers version 4.51.0 or higher.

For those who prefer cloud-based access, Qwen 4 is available via Alibaba Cloud’s Model Studio or the Qwen Chat web platform (chat.qwen.ai).

Limitations and Future Potential

While Qwen 4 is a significant advancement, it has some limitations. It lacks native multimodal capabilities like vision or audio processing, though integration with Qwen2.5-VL can address this. Additionally, its factuality and hallucination performance in real-world scenarios requires further testing.Looking ahead, Qwen 4’s dual-mode architecture and open-source nature could set a new standard for LLM design. Its ability to balance reasoning and efficiency may inspire future models to adopt similar approaches, potentially transforming the AI landscape.

Conclusion

Qwen 4 LLM is a game-changer in the world of AI, offering unmatched flexibility, efficiency, and performance. Its dual-mode operation, extensive model range, and robust reasoning capabilities make it a top choice for developers, researchers, and businesses. Whether you’re building AI agents, tackling complex research problems, or creating multilingual applications, Qwen 4 has the tools to bring your ideas to life. With its open-source accessibility and compatibility with local deployment frameworks, there’s never been a better time to explore Qwen 4. Dive in today and discover the future of AI!