Jan-v1: Advanced Agentic Language Model

Overview

Introducing Jan-v1, the first release in the Jan Family – specifically designed for advanced agentic reasoning and complex problem-solving within the Jan App. Building on the innovative agentic capabilities of our earlier Lucy model, Jan-v1 represents a significant leap forward through strategic model scaling.

Jan-v1 leverages the newly released Qwen3-4B-thinking model to deliver significantly enhanced reasoning capabilities and effective tool utilizatio. This architectural evolution is designed to deliver superior performance on complex agentic tasks, setting a new benchmark for accessible, high-performance AI.

Performance

Question Answering (SimpleQA)

For question-answering, Jan-v1 shows a significant performance gain from model scaling, achieving 91.2% accuracy.

The 91.2% 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.

Report Generation & Factuality

Evaluated on a benchmark testing factual report generation from web sources, using an LLM-as-judge. The benchmark includes our proprietary Jan Exam - Longform and the DeepResearchBench.

Model	Average Overall Score
o4-mini	7.30
Jan-v1-4B (Ours)	7.17
gpt-4.1	6.90
Qwen3-4B-Thinking-2507	6.84
4o-mini	6.60
Jan-nano-128k	5.63

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 Menlo/Jan-v1 \
    --host 0.0.0.0 \
    --port 1234 \
    --enable-auto-tool-choice \
    --tool-call-parser hermes

Using llama.cpp:

llama-server --model jan-v1.gguf \
    --host 0.0.0.0 \
    --port 1234

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

📄 Citation

Updated Soon

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