Jan-v1: Advanced Agentic Language Model
Overview
Update: Jan-v1-2509
We have released a small weight update, jan-v1-2509, which refines the original v1.
- No architectural changes.
- Slightly lower performance on SimpleQA compared to jan-v1.
- Slightly mproved results on other chat benchmarks and overall more reliable
Jan-v1 is the first release in the Jan Family, designed for agentic reasoning and problem-solving within the Jan App. Based on our Lucy model, Jan-v1 achieves improved performance through model scaling.
Jan-v1 uses the Qwen3-4B-thinking model to provide enhanced reasoning capabilities and tool utilization. This architecture delivers better performance on complex agentic tasks.
Performance
Question Answering (SimpleQA)
For question-answering, Jan-v1 shows a significant performance gain from model scaling, achieving 91.1% accuracy.
The 91.1% SimpleQA accuracy represents a significant milestone in factual question answering for models of this scale, demonstrating the effectiveness of our scaling and fine-tuning approach.
Chat Benchmarks
These benchmarks evaluate the model's conversational and instructional capabilities.
Quick Start
Integration with Jan App
Jan-v1 is optimized for direct integration with the Jan App. Simply select the model from the Jan App interface for immediate access to its full capabilities.
Local Deployment
Using vLLM:
vllm serve janhq/Jan-v1-2509 \
--host 0.0.0.0 \
--port 1234 \
--enable-auto-tool-choice \
--tool-call-parser hermes
Using llama.cpp:
llama-server --model Jan-v1-2509-Q4_K_M.gguf \
--host 0.0.0.0 \
--port 1234 \
--jinja \
--no-context-shift
Recommended Parameters
temperature: 0.6
top_p: 0.95
top_k: 20
min_p: 0.0
max_tokens: 2048
🤝 Community & Support
- Discussions: HuggingFace Community
- Jan App: Learn more about the Jan App at jan.ai
(*) Note
By default we have system prompt in chat template, this is to make sure the model having the same performance with the benchmark result. You can also use the vanilla chat template without system prompt in the file chat_template_raw.jinja.
📄 Citation
Updated Soon