Jan-v3-4B

Jan-v3-4B-base-instruct is a compact 4B parameter model created via post-training distillation from a larger teacher model. It is designed as an ownable, general-purpose base that is straightforward to fine-tune, with strong instruction following out of the box.

Open in Jan

Overview

Property	Value
Parameters	4B (3.6B non-embedding)
Base Model	Qwen3-4B-Instruct-2507
Context Length	262,144 tokens (native)
Architecture	36 layers, 32 Q heads / 8 KV heads (GQA)
License	Apache 2.0

Capabilities

Instruction following: Strong compliance with user instructions out of the box
Fine-tuning base: Designed as a starting point for downstream fine-tuning
Coding assistance: Effective lightweight coding help (a dedicated code variant, Jan-Code-4B, is also available)
General purpose: Distillation preserves broad capabilities across standard benchmarks

Jan-v3-4B-base-instruct is the base model for Jan-Code-4B, the code-tuned variant.

Performance

Aider Benchmark (Polyglot Code Editing)

Jan-v3-4B leads its weight class on the Aider polyglot benchmark with a strict pass rate of 18, nearly 50% higher than the next best model (Qwen3-4B-Thinking at 12.1):

Aider Benchmark — Jan-v3 leads with high efficiency

General Performance Comparison

Jan-v3-4B-base-instruct outperforms Qwen3-4B-Instruct-2507 (its base) across coding, math, and instruction-following benchmarks while remaining competitive on reasoning:

Performance Comparison vs. Qwen3-4B-Instruct-2507

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-v3-4B

Quick Start

Download Jan Desktop
Select Jan-v3-4B-base-instruct from the model list
Start chatting — no additional configuration needed

Demo

Try it live at chat.jan.ai (opens in a new tab)

Deployment Options

Using vLLM:


vllm serve janhq/Jan-v3-4B-base-instruct \
    --host 0.0.0.0 \
    --port 1234 \
    --enable-auto-tool-choice \
    --tool-call-parser hermes

Using llama.cpp:


llama-server --model Jan-v3-4B-base-instruct-Q8_0.gguf \
    --host 0.0.0.0 \
    --port 1234 \
    --jinja \
    --no-context-shift

Recommended Parameters


temperature: 0.7
top_p: 0.8
top_k: 20

What Jan-v3-4B Does Well

Long context: Native 262k token context window for handling large documents
Instruction following: Reliable and consistent response to user instructions
Fine-tuning: Designed to be a strong, ownable starting point for specialization
General tasks: Broad capabilities preserved through distillation from a larger model

Limitations

Model size: 4B parameters limit complex multi-step reasoning compared to larger models
Specialized domains: For coding specifically, consider Jan-Code-4B

Available Formats

GGUF Quantizations

Q4_K_M: Good balance of size and quality
Q5_K_M: Better quality, slightly larger
Q8_0: Highest quality quantization (recommended)

Models Available

Jan-v3-4B-base-instruct on Hugging Face (opens in a new tab)

Community

Discussions: HuggingFace Community (opens in a new tab)
Support: Available through Jan App at jan.ai (opens in a new tab)

Jan-Code-4B Jan-v2-VL