Jan-v1

Overview

Jan-v1 is a 4B parameter model based on Qwen3-4B-thinking, designed for reasoning and problem-solving tasks. The model achieves 91.1% accuracy on SimpleQA through model scaling and fine-tuning approaches.

Performance

SimpleQA Benchmark

Jan-v1 demonstrates strong factual question-answering capabilities:

Jan-v1 SimpleQA Performance

At 91.1% accuracy, Jan-v1 outperforms several larger models on SimpleQA, including Perplexity’s 70B model. This performance represents effective scaling and fine-tuning for a 4B parameter model.

Chat and Creativity Benchmarks

Jan-v1 has been evaluated on conversational and creative tasks:

Jan-v1 Creativity Benchmarks

These benchmarks (EQBench, CreativeWriting, and IFBench) measure the model’s ability to handle conversational nuance, creative expression, and instruction following.

Requirements

Memory:
- Minimum: 8GB RAM (with Q4 quantization)
- Recommended: 16GB RAM (with Q8 quantization)
Hardware: CPU or GPU
API Support: OpenAI-compatible at localhost:1337

Using Jan-v1

Quick Start

Download Jan Desktop
Select Jan-v1 from the model list
Start chatting - no additional configuration needed

Demo

Jan-v1 Demo

Deployment Options

Using vLLM:

vllm serve janhq/Jan-v1-4B \
    --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 \
    --jinja \
    --no-context-shift

Recommended Parameters

temperature: 0.6
top_p: 0.95
top_k: 20
min_p: 0.0
max_tokens: 2048

What Jan-v1 Does Well

Question Answering: 91.1% accuracy on SimpleQA
Reasoning Tasks: Built on thinking-optimized base model
Tool Calling: Supports function calling through hermes parser
Instruction Following: Reliable response to user instructions

Limitations

Model Size: 4B parameters limits complex reasoning compared to larger models
Specialized Tasks: Optimized for Q&A and reasoning, not specialized domains
Context Window: Standard context limitations apply