Recently, the open-source AI field has seen a major breakthrough. A deep research model named Jan-v1 was officially released, meticulously fine-tuned based on Alibaba Cloud's Qwen3-4B-Thinking model, directly competing with the commercial product Perplexity Pro. This 400 million parameter model has quickly become a focal point in the AI developer community, thanks to its 91% SimpleQA accuracy rate, full local operation capability, and specialized optimization for reasoning and tool usage.
Performance Exceeds Expectations, Accuracy Competes with Commercial Products
Jan-v1 achieved a 91% accuracy rate on the SimpleQA benchmark test, slightly surpassing the performance of Perplexity Pro, which has drawn significant attention from the industry. Through specialized fine-tuning of the Qwen3-4B-Thinking model, Jan-v1 demonstrates exceptional performance in logical reasoning, tool usage, and multi-step task handling.
The model supports a context length of 256K tokens and can be expanded to 1 million tokens using YaRN technology, making it particularly suitable for long document analysis, academic research, and complex conversation scenarios. Compared to many large models that rely on cloud computing power, this feature of Jan-v1 provides users with greater flexibility.
Local Operation Advantages Stand Out, Privacy Protection Becomes a Core Selling Point
Different from Perplexity Pro, which relies on cloud services, the biggest advantage of Jan-v1 is its ability to run entirely in a local environment. The model only needs about 4GB VRAM to operate efficiently, greatly reducing hardware requirements and usage costs. This feature not only ensures the privacy and security of user data but also avoids the risks of network latency and service outages.
Jan-v1 integrates deeply with Jan App, allowing users to quickly access model functions through a simple interface. The model supports multiple deployment methods such as vLLM and llama.cpp, providing developers with a wide range of integration options. The recommended parameter settings include a temperature of 0.6 and top_p of 0.95 to ensure high-quality and stable inference output.
Technological Innovation, Dual-Mode Reasoning Mechanism Attracts Attention
The technical foundation of Jan-v1 comes from Alibaba Cloud's Qwen3-4B-Thinking model, released in April 2025. This base model has 400 million parameters and a unique dual-mode reasoning mechanism, including a thinking mode and a non-thinking mode. Through innovative multi-stage reinforcement learning fine-tuning technology, Jan-v1 has moved away from reliance on traditional supervised fine-tuning, further enhancing its capabilities in mathematics, science, coding, and logical reasoning.
The "thinking mode" of this model can automatically generate structured reasoning processes, with outputs presented in specific formats, making it easier for users to verify and understand the AI's reasoning logic. This transparent reasoning process holds significant value for academic research and complex problem analysis.
Wide Application Scenarios, Covering from Research to Intelligent Agents
Jan-v1 is positioned as a multifunctional AI assistant, especially suitable for deep research, web search, and tool invocation scenarios. Whether it is answering complex academic questions, generating high-quality code, or executing external tool tasks through various frameworks, Jan-v1 can provide reliable support.
The model is released under the Apache 2.0 open-source license, allowing developers to freely customize and perform secondary development. Combined with the ecosystem support from platforms such as Hugging Face and Ollama, the application scenarios of Jan-v1 are expected to expand further.
Developer Community Responds Enthusiastically, Open-Source Ecosystem Is Gradually Improving
The release of Jan-v1 has sparked intense discussions in the AI developer community. Many developers have given high praise for its high performance in low-resource environments, believing that this model redefines the potential of small models. With a 91% SimpleQA accuracy rate and support for 256K context, it has become an ideal choice for academic researchers and individual developers.
However, some members of the community have pointed out that a 400 million parameter model may need to be optimized by combining with external tools when dealing with extremely complex tasks. Despite this, the open-source nature of Jan-v1 and its comprehensive technical documentation provide a solid foundation for community contributions and continuous improvement.
The release of Jan-v1 marks an important advancement in the open-source AI field. Its design philosophy that balances performance and privacy, along with relatively low resource requirements, offers new possibilities for the popularization and application of AI technology. As the community continues to contribute and the tool ecosystem continues to improve, this model is expected to play a greater role in AI research and practical applications.
Project Address: https://huggingface.co/janhq/Jan-v1-4B