Jan-v1-4B is a 4-billion-parameter open-weight model developed by JanHQ.
It is based on the Qwen3-4B-Thinking architecture and optimized for agentic reasoning, planning, and tool use, while remaining efficient for local deployment.
Trained on a curated dataset for reasoning and conversation, Jan-v1-4B balances factual accuracy, dialogue quality, and usability in compact form.
Features
Agentic reasoning: decomposes tasks and plans multi-step workflows.
Tool integration: supports structured tool calling and external function use.
Conversational fluency: strong dialogue and instruction-following.
Efficient deployment: available in quantized GGUF formats for local devices.
High factual accuracy: achieves 91.1% on SimpleQA benchmark.
Use Cases
Automation agents and reasoning workflows
Virtual assistants with tool calling
Research and tutoring support
Local deployment on consumer hardware
Fine-tuned domain-specific applications
Inputs and Outputs
Input:
Text prompts or conversation history
Structured reasoning tasks with tool calls
Output:
Generated text (answers, plans, explanations)
Structured tool-call responses
How to use
⚠️ Hardware requirement: the model currently runs only on Qualcomm NPUs (e.g., Snapdragon-powered AIPC).
Apple NPU support is planned next.
This model is released under the Creative Commons Attribution–NonCommercial 4.0 (CC BY-NC 4.0) license.
Non-commercial use, modification, and redistribution are permitted with attribution.
For commercial licensing, please contact dev@nexa.ai.
