Best Open Source ChatGPT Alternatives That Won't Disappoint in 2026

ChatGPT has changed a lot, but it is no longer the only player. Users worried about data privacy are looking for open-source ChatGPT alternatives. On top of that, rising subscription costs and a lack of control add to the misery. That’s why the demand for open source alternatives to ChatGPT is exploding right now.

For those unversed, the code and model weight of an open-source model are public. This means you can run them locally or deploy on your own infrastructure.

To help you decide, we have tested and reviewed the best open source ChatGPT alternatives for 2026.

Okara

Best For: Teams and businesses that want access to several open-source AI models without worrying about technical setup or maintenance.

Simply put, Okara is a multi-model AI workspace featuring the best open-source AI models. It brings together the best of text, image, and video generation models on a privacy-focused platform. Most tools on this list ask you to pick an AI and build around it. Okara is not like that. It allows you to switch models based on the task at hand.

• Multi-model Access: You can access 20+ leading open-source models through a single, clean UI. Deepseek, Llama, Qwen, to name a few.
• Privacy-First Design: Encrypted storage and user-controlled decryption mean Okara itself can not read chats in secure mode. Users prefer it for its secure, privacy-respecting design.
• Integrated Research Tools - Okara’s built-in research tools cover web, X, Reddit, and YouTube. This way, AI responses use current information rather than old training data.

Key Features

• Chat privately with 20+ open-source models
• End-to-end encryption
• Self-host options available
• Switch models without losing chat history
• Real-time web search
• Dual image generators

Pros

• Strong privacy
• Multiple models in one place
• Best for confidential work
• File analysis
• Team collaboration features

Cons

• Advanced for casual users
• Relatively new compared to rivals

Pricing

• Pro ($20/month)
• Max ($50/month)
• Founding User ($500 one-time payment) lifetime access (limited spots)
• Enterprise (Custom pricing)

Jan.AI

Best For: Privacy-focused individuals looking for a “ChatGPT replacement” that runs 100% offline on their own computer.

Jan is exactly what it says on the package. It is an open-source, offline-first desktop app with an interface similar to ChatGPT. You can download and run this private assistant without cloud, tracking, or telemetry.

• Wide Compatibility: Jan supports several open-source models and integrates with other AI tools. Supported tools are ChatGPT, Llama, Claude, Gemini, Deepseek, and more.
• 100% Offline: It works without an internet connection once the model is downloaded. Besides that, all your AI interactions never leave your device.
• Local API Server: Jan runs a server on localhost that mimics OpenAI’s API, so other apps and plugins can connect to it.
• MCP Integration: This secure AI now supports Model Context Protocol. So, it can browse the web, use tools, and connect with external services like Linear and Todoist.

Key Features

• Real-time web search
• Runs locally and fully offline
• API support
• Multi-platform compatibility
• Document-based conversations

Pros

• Open source and free
• Ideal for privacy-sensitive tasks
• User-friendly UI
• No data collection

Cons

• Resource-intensive
• Limited to models supported in the library

Pricing

Jan is open source (AGPLv3), so it does not require subscription fees. However, you will pay for the hardware and setup cost.

AnythingLLM

Best For: Teams and businesses that want to turn their documents into a private, queryable chatbot.

AnythingLLM is one of the most feature-rich open source AI applications available today. Many even call it the king of RAG (Retrieval Augmented Generation). It is essentially a secure documents hub where businesses can upload their documents and talk to them using any LLM. AnythingLLM is more like a self-hosted, private ChatGPT with your company’s information.

• Built-in RAG: Upload PDFs, Word docs, CSVs, and URLs into this searchable, vector database. It pulls the relevant info from your documents to give an accurate, sourced answer.
• Model Agnostic: You can connect it to local models via Ollama or use cloud APIs from providers like OpenAI or Anthropic.
• Agent Tools: The built-in agents can perform tasks on your behalf, such as searching the web and summarizing content. Plus, a user can create custom AI agent skills.
• Deployment Options: Run it locally as a Desktop app, self-host it via Docker, or choose a cloud service.

Key Features

• Self-hosted and private
• Multi-user support
• Dual chat modes (conversation, query)
• Multiple document formats supported
• Multi-model LLM provider
• AI agents

Pros

• Developer-friendly interface
• Flexible deployment
• Multi-user support with RBAC
• Team workspaces

Cons

• Desktop-only

Pricing

The desktop and self-hosted versions are free under MIT license. Managed cloud hosting with up to three team members starts at $50/month. The pro package for startups is priced at $99/month.

Ollama

Best For: Developers and technical users who want a lightweight way to run open source LLMs via the command line.

Ollama makes running large language models locally dramatically simpler. This command tool downloads and starts the model with one command line, ollama run llama3. After that, it takes care of the GPU acceleration and hardware optimization in the background.

• Enormous Model Library: It has an already huge and growing list of popular models like Llama 3, Mistral, Gemma, and Phi.
• Privacy-Focused Design: Data is not stolen or shared unless the user explicitly sends it somewhere. It allows offline use and runs directly on your computer for complete privacy.
• Modelfiles: Create and share custom models by changing parameters or system prompts in a simple file.
• Developer-friendly: Developers prefer it for its simplicity and simple CLI. In addition, support for local Rest API allows them to connect with external apps

Key Features

• Easy setup through CLI or Docker container
• Supports Rest API to connect with other apps
• Complete control over downloading, updating, and deleting models
• Cross-platform compatibility

Pros

• Extremely fast to set up
• Works without an internet connection
• Highly customizable
• Compatibility with several open source models

Cons

• Pre-set quantization options

LM Studio

Best For: Beginners and enthusiasts seeking a user-friendly way to discover and run local models.

LM Studio is the perfect entry point for people moving away from ChatGPT. Beginners can experiment with local models without touching the command line. Its GUI application allows you to browse, download, and run GGUF-format models from Hugging Face with a few clicks. Like Jan, it has a ChatGPT-like window.

• GUI-Based Model Discovery: The desktop application includes a Discover page to browse popular models directly. Additionally, you can search and download Hugging Face models directly inside the app.
• OpenAI-Compatible: LM Studio can spin up a local API server that mimics OpenAI’s endpoint. As a result, any tool that works with OpenAI’s AI can be connected to this model.
• Chat with Documents: It supports RAG and allows you to attach documents to your conversation.
• Multi-model Conversations: It runs multiple AI models at the same time and compares their responses side-by-side.

Key Features

• Supports popular open-weight LLM (Qwen, Ministral, Olmo, Deepseek)
• Includes developer tools (OpenAI’s API, SDK, and CLI)
• Automatically configures compatible hardware
• Find AI models within the app

Pros

• Offline operations
• Advanced parameter tuning
• Built-in inference server
• Beginner-friendly
• Ideal for “model hopping”

Cons

• Needs significant computing power
• Requires 500MB of storage space

Pricing

LM Studio is completely free; however, you will bear the setup cost.

Open WebUI

Best For: Teams and individuals needing a self-hosted, feature-rich web interface to run AI models on Ollama servers.

Open WebUI (formerly Ollama WebUI) looks almost identical to ChatGPT. In simple terms, it can be called a browser-based frontend for Ollama. It is packed with features, including RAG support, web search integration, multi-model chats, prompt templates, and a model management panel. Typically, you run it in a Docker container, and it connects to an Ollama server (or any OpenAI-Compatible AI).

• Native Ollama Integration: It manages and runs Ollama models directly from the browser UI. Further, it connects with OpenAI-compatible endpoints such as GroqCloud, Mistral, Open Router, and LM Studio.
• Web Browsing: Open WebUI connects search providers like Brave, Google PSE, Bing, and DuckDuckGo to give your models current information.
• Video and Video Chat: It natively supports hands-free voice and video calls. The model is compatible with multiple speech-to-text providers (Whisper, Deepgram, OpenAI) and text-to-speech engines.
• Image Generation: It connects to AUTOMATIC1111 or ComfyUI locally, or uses OpenAI's DALL-E, to make and edit images within your chats.

Key Features

• Works on mobile through its Progressive Web App (PWA)
• Detailed roles and access control
• Markdown and LaTeX capabilities
• Creates and customizes Ollama functions
• Offers a modular plugin framework to integrate Python libraries

Pros

• Good feature set for a self-hosted app
• Responsive design
• Role-based access control (RBAC)
• Web search for RAG
• Multi-Modal support
• Active community and frequent updates

Cons

• Overkill for anyone wanting a simple chat
• Performance depends on hardware and LLM

Pricing

Open WebUI is free and open source, which you can self-host on your server or locally. Cloud compute cost is your only expense.

LibreChat

Best For: Users and businesses that need a single interface to manage and use multiple local and cloud-based AI models without losing the ChatGPT feel.

LibreChat is the closest thing to a true open-source ChatGPT alternative. This self-hostable web app unifies the entire AI world. It adds OpenAI, Anthropic, Azure, and Google models in addition to local ones from Ollama. Users can switch between models and providers mid-conversation.

• Multi-service Aggregation: As stated above, LibreChat connects to almost every major provider and your local models in one enterprise-grade interface.
• Agent Capabilities: It now supports a sequential mixture-of-agents setup. This means multiple specialized agents work together on advanced tasks. Also, you can create simple agents just by writing text prompts as "presets.”
• Conversation Forking: Users can fork conversations to explore different responses and switch models in the middle of the chat.
• Document Q&A: Upload files, and LibreChat pulls answers from these uploaded documents based on the question. It uses Mistral OCR to do so and transcribe audio with GPT-4o-transcribe.

Key Features

• Connects to 15+ AI providers
• Searchable conversation history stored locally
• Saves custom presets for different tasks
• Share conversations on social media
• Supports image generation and analysis
• Allows users to run Python code in a code interpreter

Pros

• A near-perfect replica of the ChatGPT model
• One interface to manage all major AI models
• Best for comparing model outputs

Cons

• A little complex to configure initially
• RAG is not as advanced as other alternatives

Pricing

Since LibreChat is open-source, it is free, and you can self-host via Docker or your own infrastructure.

LangChain

Best For: Software engineers building complex, autonomous AI agents and applications

Do not mistake LangChain for a simple “chat app.” Instead, it is a toolkit for developers to build their own AI applications. It is a powerful Python/JavaScript framework to build LLM applications from scratch. Unlike some other models, it is not a ready-to-use ChatGPT replacement out of the box.

As the name suggests, developers can "chain" together different AI components, such as a model, a database, and a web search tool, to create an agent.

• Model-agnostic LLM Integration: It connects users to multiple providers such as OpenAI, Anthropic, Ollama, Hugging Face, and more.
• Modular Components: It includes pre-built components for prompt templates, memory, and output parsers.
• Agent Framework: Developers can build smart agents that can reason and decide which tools to use to finish a task.
• LCEL (LangChain Expression Language): A declarative way to chain components together for easy prototyping and production.

Key Features

• Features a unified interface for multiple LLMs
• Refines prompts using templates to get accurate responses
• Integrates with hundreds of external tools
• Debug and test your LLM application in production via LangSmith

Pros

• Massive integration ecosystem
• Production-ready architecture
• RAG capabilities
• Prompt management

Cons

• Performance bottlenecks or latency
• Not for non-developers

Pricing

The core Langchain library is open source and completely free.

Why Choose an Open-source Alternative to ChatGPT?

People prefer open source AI chatbot alternatives to ChatGPT due to three reasons: data control, cost, and customization.

• Data Control: In a closed system, your prompts are often used to "train" the next model. Your prompt passes through OpenAPI’s servers when using ChatGPT. This is fine for personal use cases. It becomes a real problem for businesses handling client data or anything regulated by GDPR and HIPAA. Open source tools, especially Okara, keep your private chats and company secrets on your own infrastructure.
• Cost Savings: Those using open source ChatGPT alternatives do not have to worry about premium subscriptions or token limits. Open source models, at their core, are free. You only pay the fixed infrastructure cost for these self-hosted models. Platforms like Okara help you save massively as compared to per-token pricing models.
• Customization: Developers enjoy the freedom that comes with open source alternatives. They can fine-tune the model on their own data or combine it with other tools in their workflow. In-depth customization is not possible with a generic ChatGPT account.

What to Look For In an Open Source ChatGPT Alternative?

It is worth working through a few questions before committing to an open source model.

• Technical Skills Level: Some tools (Ollama, LangChain) on this list require advanced technical skills. In contrast, Okara, LM Studio, and Jan work for people with no developer background at all. Before jumping in, evaluate the skill level of your team and pick a model that they can comfortably use.
• Hardware Availability: Larger models require powerful computers with expensive GPUs and a lot of VRAM. Standard laptop users should stick to smaller, efficient models. Managed open-source platforms like Okara are better for average users. It is more practical because the heavy lifting happens on their private servers.
• Privacy Requirements: Privacy-focused teams need tools that work on their own infrastructure. Jan (fully offline), Ollama/Open WebUI (self-hosted), and AnythingLLM offer a secure environment. Notably, cloud-hosted open source services still involve a third-party server.

Want Top Open Source ChatGPT Alternatives in One Place?

Testing all these open source AI models is no less than a full-time job. Juggling different installations is a massive headache for busy professionals.

Thankfully, Okara solves this by bringing together the world’s best open source models in an encrypted workspace. Users can switch between them based on the task without managing installations or GPU configs. This economical, productive AI keeps your chats, ideas, and business secrets secure.

FAQs

What is an open-source ChatGPT alternative?
It is a chatbot or AI tool whose source code and often the trained model itself are publicly available. You can inspect, tweak, download, and run the software yourself. The code for ChatGPT is not available as it is a closed, proprietary service.

Are open-source AI models free?
Yes, open source models are almost always free to download and use. However, you need to invest in your own hardware (a computer with a good GPU) to run them. Plus, users will cover electricity costs to run these models.

Can I run open-source AI locally?
Yes, you can run them entirely offline. Open source AI models such as Jan, Ollama, and LM Studio are built to be used locally. Users can download and run the models on their computer without an internet connection.

Are open-source models as powerful as ChatGPT?
It largely depends on the model and the assigned tasks. For instance, larger Llama 3 versions can match or even outperform GPT-3.5 in many areas. They are catching up fast to GPT-4, especially in specific, focused tasks.

Can I use multiple open-source models through one UI so that I don’t have to keep switching?
Yes, several tools allow you to switch between these models without constantly changing platforms. Okara is designed exactly for this purpose. Plus, WebUI and LibreChat also give access to both local and cloud models through a single interface.