Local LLM Tool Comparison 2026: Ollama, LM Studio, Jan and 7 More (Pricing & Free Plans)

When choosing a local LLM tool, there are many factors to check, such as pricing structures, the availability of a free plan, the level of Japanese support, and supported OS environments. It is also common to struggle with finding a tool that fits your specific use case without comparing the strengths and weaknesses of each option.

In this article, we provide a complete comparison of 10 major tools, including Ollama and LM Studio, based on AIpedia data (as of November 2026). We organize pricing details, strengths and things to note regarding features, and environment requirements to help you resolve any uncertainty during implementation.

We also summarize recommendations by goal for specific use cases and key points on how to choose without common pitfalls. Compare and evaluate the optimal tool efficiently according to your company's security policies and PC specifications.

What Are Local LLM Tools? Capabilities and Key Considerations Before Choosing

Local LLM tools are software that allows you to run large language models on your own PC or server without an internet connection. When using local models, the prompts you enter and the generated responses are not sent to external cloud servers. This enables you to use AI while protecting privacy, even when handling confidential business documents or personal data.

• Document summarization and Q&A: You can upload files such as PDFs or Word documents and answer questions based on their content using RAG (Retrieval-Augmented Generation) features.
• Code generation and completion: You can create code in programming languages, review existing code, and fix bugs within a local environment.
• Diverse task processing: Some tools, like LocalAI, can execute not only text generation but also image generation, speech recognition, and text-to-speech (TTS) locally.
• Integration with existing apps: If a tool has a built-in OpenAI-compatible API server feature, you can connect your existing applications directly to the local model for use.
• Model training and tuning: Some tools, like text-generation-webui, allow you to perform fine-tuning such as LoRA via a GUI to adjust the model to your specific needs.

When choosing a tool, first check the "scope of free usage." Many tools are completely free as open source, but some may offer paid plans. The level of Japanese support is also important. Some tools support conversational features but have an English-only UI (screen display), while others may have varying accuracy for Japanese.

Additionally, you need to consider the environment requirements based on your intended use. Desktop apps for individuals are easy to operate via a GUI, but high-load processing for businesses may require specific GPU performance or knowledge of setting up a Linux environment. It is best to select tools like Ollama or LM Studio according to your PC specifications and technical strengths and weaknesses.

Comparison of 10 Key Tools (Pricing, Japanese Support, Platforms)

Tool	AIpedia rating	Main plans	Japanese support	Platforms
Ollama	4.3	Completely free (open source)	Supported (Japanese dialogue possible)	Mac, Windows, Linux
LM Studio	4.4	Free (free for both personal and business use. The app itself is not open source)	Partially supported	Windows, Mac, Linux
Jan	4.1	Completely free (open source, AGPLv3) / API keys require separate fees from each provider	Partially supported	Windows, macOS, Linux
Open WebUI	4.4	Free (open source)	Partially supported (Japanese chat possible, UI is primarily English)	Web, Self-hosting (Docker)
AnythingLLM	4.2	Completely free (open source) / AnythingLLM Cloud (contact sales) / API keys require separate fees from each provider	Partially supported	Windows, macOS, Linux, Docker
Msty	4.2	Free $0 / Aurum $149/user/year / Aurum Lifetime $349 (one-time purchase)	Not supported	macOS, Windows
Llamafile	4.3	Completely free (open source, Apache 2.0 license)	Not supported	Windows, Mac, Linux
LocalAI	4.2	Completely free (open source, MIT license)	Not supported	Windows, Mac, Linux, Docker
vLLM	4.5	Completely free (open source, Apache 2.0 license)	Not supported	Linux, Docker
text-generation-webui	4.3	Completely free (open source, AGPL-3.0 license)	Not supported	Windows, Mac, Linux

Ratings are based on a 5-point scale by the AIpedia editorial team. Pricing and plans may change. Please check the official website of each tool for the latest information before signing up.

Features, Pricing, and Best Use Cases for 10 Tools

1. Ollama

Ollama is an open-source tool that lets you run LLMs on your local PC. You can download and run many models, such as Llama and Mistral, with a single command. Because data is not sent externally, it is well-suited for privacy protection.

• Strengths: Completely free and provided as open source.
• Strengths: Data processing is completed locally, so there is no concern about external transmission.
• Strengths: Supports many open-source models, including Llama and Gemma.
• Things to note: A high-performance GPU is recommended for fast performance, and answer accuracy does not match that of cloud AI.
• Things to note: According to AIpedia data, it has "no GUI (terminal operation)." Although an official desktop app (macOS, Windows) was released in 2025, allowing you to use a chat interface, detailed settings and model management are often still done via commands.

Pricing: Completely free (open source)

Best for: Those who want to set up development environments or use local AI with a focus on privacy. Available on Mac, Windows, and Linux.

View detailed features, pricing, and FAQ for Ollama

2. LM Studio

LM Studio is a desktop app for running large language models on your local PC. You can download and run open-source models like Llama and Mistral using only GUI operations. Its feature is that you can manage models intuitively without programming knowledge.

• Strengths: Completely free for personal and business use.
• Strengths: Works without an internet connection, and since data is not sent externally, privacy is maintained.
• Strengths: Includes a built-in OpenAI-compatible local API server function, allowing integration with existing apps.
• Things to note: A PC with a GPU is recommended to use high-performance models.
• Things to note: Model files are large, ranging from several GB to tens of GB, and consume storage space.

Pricing: Free (free for both personal and business use. The app itself is not open source)

Best for: Those who prioritize privacy and want to use AI in offline environments. Suitable for those who want to easily try models via GUI, or developers who want to call local LLMs from existing applications. However, if you expect answer quality on par with cloud AI, it may fall short of expectations.

3. Jan

Jan is an open-source local AI chat application. You can run local LLMs like Llama and Mistral with one click via GUI, and it also connects to cloud APIs from OpenAI and Anthropic. Its design prioritizes privacy.

• Strengths: Download models from the built-in model hub and start chatting immediately.
• Strengths: Based on llama.cpp, it works on CPU-only systems without a GPU, and all conversation data is saved locally.
• Strengths: Connects to cloud APIs from OpenAI and Anthropic, and also has an OpenAI-compatible local API server function.
• Things to note: The UI is English-only and does not support Japanese. Also, running high-performance models requires a high-spec PC.
• Things to note: The answer quality of local LLMs is inferior to cutting-edge cloud models, and the extension ecosystem is still developing.

Pricing: Completely free (open source, AGPLv3) / API keys require separate fees from each provider

Best for: Those who want to try local LLMs via GUI without using the command line, and those who prioritize local data storage.

4. Open WebUI

Open WebUI is a self-hosted AI chat UI that supports Ollama and OpenAI-compatible APIs. You can build an easy-to-use chat interface on your own PC or server. When combined with local models, it runs completely offline, ensuring no data is sent externally.

• Strengths: It comes standard with advanced features such as RAG capabilities, voice and video calls, and document processing. It supports 9 types of vector databases, including ChromaDB and PostgreSQL, and allows for prompt injection protection via LLM-Guard.
• Strengths: It supports multiple users, making it suitable for use by teams or organizations. When combined with Ollama, you can easily operate various local LLMs like Llama and Mistral via a GUI.
• Things to note: Setup requires technical knowledge, such as Docker. Additionally, Japanese localization of the UI is incomplete, resulting in an English-centric interface.

Pricing: Free (open source)

Best for: Individuals who prioritize privacy and have their own server or GPU environment. It is suitable for business professionals who can handle technical setup and want to establish a local AI chat environment on a team basis.

View detailed features, pricing, and FAQ for Open WebUI

5. AnythingLLM

AnythingLLM is an all-in-one AI desktop application that runs in a local environment. It supports multiple LLM providers and allows you to complete everything from document upload to question answering via RAG (Retrieval-Augmented Generation) within a single app. It also integrates built-in vector databases and agent features, enabling AI utilization while maintaining privacy.

• Strengths: It integrates LLM selection, document management, vector databases, and agent features, allowing for easy construction of RAG systems.
• Strengths: Since data is stored locally, it can be used to protect privacy even with confidential corporate documents.
• Things to note: The UI is English-only with insufficient Japanese support, which may take time to get used to operating.
• Things to note: Vectorizing large amounts of documents takes time, and advanced customization requires knowledge of Docker or CLI.

Pricing: Completely free (open source) / AnythingLLM Cloud (contact sales) / API keys require separate fees from each provider

Best for: Individuals who want to complete document management and AI question answering locally, or those who want to switch between cloud LLMs and local LLMs. However, this assumes no resistance to an English UI. A PC with a GPU is recommended to run local LLMs comfortably.

6. Msty

Msty is a desktop AI chat app that can run LLMs in a local environment. It integrates with Ollama, allowing you to use numerous open-source models while protecting privacy. It also integrates cloud APIs from OpenAI and Anthropic, featuring the ability to manage both local and cloud models on a single screen.

• Strengths: Available from a completely free Free plan, reducing initial costs.
• Strengths: Features a refined UI for intuitive operation, and includes RAG capabilities and prompt template saving.
• Things to note: Running local models requires decent PC specifications, such as 8GB or more of RAM.
• Things to note: It does not support a Japanese UI, and no mobile version is provided.

Pricing: Free $0 / Aurum $149/user/year / Aurum Lifetime $349 (one-time purchase) / Enterprise, Teams (contact sales)

Best for: Ideal for those who prioritize privacy and want to flexibly switch between local and cloud models. Recommended for business professionals and individual users who want to easily start using local AI with a refined UI, even without technical knowledge.

7. Llamafile

Llamafile is a tool developed by Mozilla that runs LLMs from a single executable file. It integrates model weights and the inference engine into one file, eliminating the need to install dependencies like Python or CUDA. You can launch it on Windows, Mac, and Linux simply by executing the downloaded file (on Mac, run it from the terminal).

• Strengths: Zero dependencies and no installation required, saving time on environment setup.
• Strengths: Can be stored on a USB drive and run directly on other PCs.
• Strengths: Includes a built-in Web UI and OpenAI-compatible API, making integration with external apps easy.
• Things to note: Because it includes model weights, a single file may be several GB in size.
• Things to note: Supported model formats are limited to GGUF.

Pricing: Completely free (open source, Apache 2.0 license)

Best for: Suitable for those who want to minimize environment setup effort or use AI portably via USB drive. However, if you prioritize GUI customization for model management, compare it with other tools. Check the official website for the latest pricing and features.

8. LocalAI

LocalAI is an open-source local AI runtime environment compatible with the OpenAI API. It can run LLMs as well as image generation, speech recognition, and text-to-speech functions locally in an integrated manner. You can migrate to a local environment simply by replacing the endpoint of existing OpenAI-compatible applications.

• Strengths: Functions as a drop-in replacement for the OpenAI API, allowing you to use existing apps as-is.
• Strengths: Works without a GPU in CPU-only environments and can be started easily with a single Docker command.
• Things to note: Inference speed for large models may be slow on CPU-only setups.
• Things to note: The GUI is minimal, and editing configuration files may be required in some cases.

Pricing: Completely free (open source, MIT license)

Best for: Ideal for developers who want to migrate existing OpenAI API apps to local environments, or businesses looking to build an on-premises AI infrastructure. However, it is better suited for those who prioritize API integration and server-side setup over intuitive GUI operations.

9. vLLM

vLLM is a high-throughput LLM inference and serving engine developed at UC Berkeley. Using its proprietary PagedAttention technology, it manages GPU memory efficiently and is reported to achieve up to 24 times the throughput (amount processed per unit time) compared to HuggingFace Transformers. It is a production-oriented tool optimized for API delivery in live environments.

• Strengths: It has a built-in OpenAI-compatible API server, making it easy to use with existing apps.
• Strengths: It supports high parallel processing via continuous batching, helping reduce operational costs.
• Things to note: An NVIDIA GPU (with 16GB or more VRAM) is nearly mandatory. It is not practical in CPU-only environments.
• Things to note: It does not provide a chat UI for individuals. The design is specialized for API servers.

Pricing: Completely free (open source, Apache 2.0 license)

Best for: Companies and engineers who want to run an LLM API service in production that handles simultaneous access from multiple users. It is suitable for building internal AI infrastructure or customer-facing APIs, rather than personal chat use.

10. text-generation-webui

text-generation-webui is an open-source tool that runs local LLMs through a Gradio-based web interface. It supports various quantization formats such as GPTQ and GGUF, allowing you to download and use a wide range of models. In addition to chat and story generation, you can perform fine-tuning with LoRA/QLoRA directly via the GUI.

• Strengths: It includes a built-in OpenAI-compatible API server and an Extension system, offering high flexibility for feature expansion.
• Strengths: You can configure detailed settings such as GPU memory usage and layer splitting.
• Things to note: Because it offers a wide variety of features, beginners may find the operation complex.
• Things to note: A high-performance GPU with 8GB or more VRAM is recommended for stable performance.

Pricing: Completely free (open source, AGPL-3.0 license)

Best for: Power users who prioritize customizability and are comfortable setting up Python environments and using the command line. Please check the official website for the latest pricing and features.

Recommendations by Goal

Goal	Recommended tool	Reason
Try it out easily for free first	Ollama	Fully free open source. Download and run many models with a single command.
Operate intuitively via GUI	LM Studio	Download and manage models via GUI. Free for personal and business use.
Run in an environment without a GPU	Jan	Runs on CPU only. Execute local LLMs with one click.
Leverage document search (RAG)	AnythingLLM	Integrates RAG features. Intuitively manage documents like PDFs and Word files.
Use existing app APIs as-is	LocalAI	Has OpenAI API-compatible endpoints. Use it by simply switching the connection target of existing compatible apps.
Build a high-throughput API server	vLLM	High-throughput inference engine. Provides an API server for production environments.
Use diverse quantization formats or fine-tune	text-generation-webui	Supports a wide range of quantization formats, such as GPTQ and GGUF. LoRA fine-tuning is possible via the GUI.
Self-host a chat interface for team use	Open WebUI	Host a chat interface connected to Ollama or OpenAI-compatible APIs on your own server. Supports multi-user.
Switch between local and cloud models in one screen	Msty	Switch between Ollama-connected local models and cloud APIs from OpenAI, Anthropic, Google, etc., in the same screen.
Use it portably without installation	Llamafile	Model and execution engine are bundled into a single file. Can be launched from a USB drive.

The above reflects general trends based on data. Actual performance speed and quality vary depending on the hardware environment used. When choosing the optimal tool, check details on the official website according to your PC specifications and business workflows.

How to Choose Without Regrets

Purpose and UI usability

First, clarify whether you want to "chat via a conversation interface" or "integrate it into development." LM Studio and Jan have intuitive GUIs, allowing you to complete everything from downloading a model to running it with one click. On the other hand, Ollama and vLLM are designed primarily around command lines and APIs. The latter offers high flexibility, but setting them up takes time if you lack technical knowledge.

Pricing structure and cost composition

Many tools are free, but the "form" of that free access varies. Ollama and LM Studio are completely free to use with no additional costs. In contrast, Msty offers a free plan in addition to paid options like the Aurum plan ($149/user/year), featuring diverse pricing models. Also, if you use cloud APIs alongside these tools, note that provider usage fees apply separately from the tool itself.

Japanese support and UI language

Beyond features, the availability of a Japanese interface is an important selection criterion. Ollama and LM Studio allow conversations in Japanese, but Jan and AnythingLLM have English-centric UIs. Msty, Llamafile, and vLLM are noted as not supporting a Japanese UI. If you are comfortable with English, this is not an issue. However, if you prefer not to get lost while operating the UI, check the status of Japanese support. Note that the quality of conversations in Japanese depends more on the model used than on the tool itself.

Platforms and PC specifications

Recommended hardware environments vary by tool. Jan can run on CPU only, but vLLM almost requires an NVIDIA GPU (16GB or more VRAM). LocalAI runs without a GPU in CPU environments, but tends to be slower with large-scale models. Check in advance whether your PC has a GPU and if you have sufficient storage space, then choose a tool that fits your setup without strain.

Extensions and integration breadth

Determine whether you need document processing or API integration beyond simple chat. AnythingLLM has built-in RAG (Retrieval-Augmented Generation) functionality and is strong in document management. On the other hand, LocalAI can locally execute diverse AI functions such as image generation and speech recognition. If you want to use existing OpenAI-compatible apps as-is, tools with high API compatibility like LocalAI or vLLM are suitable.

FAQ

Q. Are all local LLM tools free to use?

Most tools are free, and many are open source. Many tools, such as Ollama, LM Studio, and Jan, incur no costs for both personal and business use. However, Msty has paid plans: Aurum is $149/user/year, and Aurum Lifetime is $349 (one-time purchase). If you use cloud APIs with Jan or AnythingLLM, separate fees from each provider apply. Please check the official websites for the latest pricing structures.

Q. Is a high-performance GPU always required?

Recommended environments vary by tool. Ollama and LM Studio recommend a PC with a GPU to run high-performance models, while Jan and LocalAI can operate on CPU only. vLLM is considered to almost require an NVIDIA GPU (16GB or more VRAM). You need to set up your environment based on the model size and tool characteristics.

Q. How well does Japanese conversation work?

Ollama supports Japanese conversations, and LM Studio and Jan have partial support. Open WebUI and AnythingLLM allow Japanese chat, but their UI localization is primarily in English. On the other hand, Msty and Llamafile do not support a Japanese UI, and vLLM is an API server without a chat interface. For any tool, the quality of Japanese conversation varies significantly depending on the model used.

Q. Can beginners set it up easily?

LM Studio and Jan are suitable for beginners because they allow intuitive model downloading and execution via GUI. Llamafile is also a single file with few dependencies and can be launched by double-clicking. In contrast, Open WebUI requires Docker, and vLLM requires knowledge of Linux and Python. text-generation-webui has a one-click installer, but knowledge of Python or the command line is helpful for GPU configuration and installing extensions.

Q. Can it integrate with existing apps?

Many tools have built-in OpenAI-compatible API server functionality. Ollama, LM Studio, Jan, LocalAI, vLLM, and others can be used simply by changing the connection destination of existing OpenAI-compatible apps. This allows you to run models in a local environment while maintaining your traditional application flow. Please check each tool's documentation for specific integration methods.

Summary

This article compared 10 local LLM tools, including Ollama and LM Studio. Many of these tools are completely free, with privacy protection as a key strength since data does not leave your device. However, running high-performance models often requires a GPU-equipped PC, and response quality is generally lower than that of cloud AI.

The presence of a GUI and the difficulty of setup vary significantly by tool. Ollama relies mainly on command-line operations, while LM Studio and Jan allow intuitive model management through visual interfaces. vLLM and text-generation-webui require technical knowledge but offer options suited to specific needs, such as high throughput and support for diverse quantization formats.

• If you want to get started easily: LM Studio (allows intuitive downloading and running of models via GUI)
• If you want to leverage documents: AnythingLLM (integrates RAG functionality and document management for easy setup)
• If you are operating in a server environment: vLLM (enables high-throughput inference processing and is optimized for production environments)