Overview

Jan-v1-edge is a lightweight, agentic language model from janhq, designed for efficient on-device execution. Distilled from the larger Jan-v1 model, it retains strong reasoning and problem-solving capabilities within a smaller 2 billion parameter footprint, making it ideal for resource-constrained environments.

Key Capabilities & Features

• Distilled Architecture: Developed through a two-phase post-training process involving Supervised Fine-Tuning (SFT) from Jan-v1 and Reinforcement Learning with Verifiable Rewards (RLVR).
• High Efficiency: Achieves 83% accuracy on SimpleQA, demonstrating robust performance despite its small size.
• Agentic Design: Built for interactive workloads and web search applications.
• Optimized for Edge: Specifically engineered for fast, reliable execution on local devices.
• Context Length: Supports a substantial 40960 token context window.

Performance Highlights

While showing a slight degradation in instruction-following and creative writing compared to Qwen 3 1.7B Thinking, Jan-v1-edge remains comparable or superior in EQBench and recency-based Question Answering.

Integration

Jan-v1-edge is optimized for direct integration with the Jan App and supports local deployment via vLLM and llama.cpp.