---
title: "Jan v0.6.7: OpenAI gpt-oss support and enhanced MCP tutorials"
version: 0.6.7
description: "Full support for OpenAI's open-weight gpt-oss models and new Jupyter MCP integration guide"
date: 2025-08-07
ogImage: "/assets/images/changelog/gpt-oss-serper.png"
---

import ChangelogHeader from "@/components/Changelog/ChangelogHeader"
import { Callout } from 'nextra/components'


<ChangelogHeader title="Jan v0.6.7: OpenAI gpt-oss support and enhanced MCP tutorials" date="2025-08-07" ogImage="/assets/images/changelog/gpt-oss-serper.png"/>

## Highlights 🎉

Jan v0.6.7 brings full support for OpenAI's groundbreaking open-weight models - gpt-oss-120b and gpt-oss-20b - along with enhanced MCP documentation and critical bug fixes for reasoning models.

### 🚀 OpenAI gpt-oss Models Now Supported

Jan now fully supports OpenAI's first open-weight language models since GPT-2:

**gpt-oss-120b:**
- 117B total parameters, 5.1B active per token
- Runs efficiently on a single 80GB GPU
- Near-parity with OpenAI o4-mini on reasoning benchmarks
- Exceptional tool use and function calling capabilities

**gpt-oss-20b:**
- 21B total parameters, 3.6B active per token
- Runs on edge devices with just 16GB memory
- Similar performance to OpenAI o3-mini
- Perfect for local inference and rapid iteration

<Callout type="info">
Both models use Mixture-of-Experts (MoE) architecture and support context lengths up to 128k tokens. They come natively quantized in MXFP4 format for efficient memory usage.
</Callout>

### 🎮 GPU Layer Configuration

Due to the models' size, you may need to adjust GPU layers based on your hardware:

![GPU layers setting adjusted for optimal performance](/assets/images/changelog/jupyter5.png)

Start with default settings and reduce layers if you encounter out-of-memory errors. Each system requires different configurations based on available VRAM.

### 📚 New Jupyter MCP Tutorial

We've added comprehensive documentation for the Jupyter MCP integration:
- Real-time notebook interaction and code execution
- Step-by-step setup with Python environment management
- Example workflows for data analysis and visualization
- Security best practices for code execution
- Performance optimization tips

The tutorial demonstrates how to turn Jan into a capable data science partner that can execute analysis, create visualizations, and iterate based on actual results.

### 🔧 Bug Fixes

Critical fixes for reasoning model support:
- **Fixed reasoning text inclusion**: Reasoning text is no longer incorrectly included in chat completion requests
- **Fixed thinking block display**: gpt-oss thinking blocks now render properly in the UI
- **Fixed React state loop**: Resolved infinite re-render issue with useMediaQuery hook

## Using gpt-oss Models

### Download from Hub

All gpt-oss GGUF variants are available in the Jan Hub. Simply search for "gpt-oss" and choose the quantization that fits your hardware:

### Model Capabilities

Both models excel at:
- **Reasoning tasks**: Competition coding, mathematics, and problem solving
- **Tool use**: Web search, code execution, and function calling
- **CoT reasoning**: Full chain-of-thought visibility for monitoring
- **Structured outputs**: JSON schema enforcement and grammar constraints

### Performance Tips

- **Memory requirements**: gpt-oss-120b needs ~80GB, gpt-oss-20b needs ~16GB
- **GPU layers**: Adjust based on your VRAM (start high, reduce if needed)
- **Context size**: Both models support up to 128k tokens
- **Quantization**: Choose lower quantization for smaller memory footprint

## Coming Next

We're continuing to optimize performance for large models, expand MCP integrations, and improve the overall experience for running cutting-edge open models locally.

Update your Jan or [download the latest](https://jan.ai/).

For the complete list of changes, see the [GitHub release notes](https://github.com/menloresearch/jan/releases/tag/v0.6.7).
