# InternVL3 vs Jan

InternVL3 records 0 monthly visits with 0% month-over-month growth and Freemium pricing. Jan records 291,894 monthly visits with -9.6% growth and Free pricing.

| Metric | [InternVL3](https://openaitools.app/tool/internvl3) | [Jan](https://openaitools.app/tool/jan) |
| :--- | :--- | :--- |
| Website URL | https://internvl.opengvlab.com | https://jan.ai |
| Categories | AI Chatbot, AI Image Recognition, AI Code Generator | AI Chatbot, AI Assistant, Open Source AI Models, Large Language Models (LLMs) |
| Pricing Model | Freemium | Free |
| Monthly Visits | 0 | 291,894 |
| MoM Growth Rate | 0% | -9.6% |
| Primary Market | Global | United States |
| Direct Visits | 0 | 105,615 |
| Review Score | 4.7 | 4.6 |
| Saved Count | 0 | 6 |
| Supported Platforms | Website | Website |

## InternVL3

InternVL3 is part of the InternVL series of open multimodal large language models developed by OpenGVLab, a research group known for releasing strong open vision-language models. Where many proprietary systems hide their weights, InternVL3 ships as downloadable, self-hostable models, so teams can run, study and adapt them directly. The series emphasizes working with images and text together, and later releases push further into reasoning, long-context handling and agent-style tasks. In practice, InternVL3 can read and reason about photos, screenshots, diagrams and documents, and it also handles everyday language work such as drafting, summarizing and code. Because it is open, it is a common choice for researchers benchmarking multimodal capabilities and for companies that need an MLLM they can host inside their own infrastructure. The trade-off is that it is closer to a research and developer tool than a polished consumer app: the main paths are an online chat demo and the open weights themselves, and running the larger variants locally requires serious GPU resources.

**Pricing & Plans:** Free and open. The model weights are released openly for download and self-hosting, and an official online chat demo is available at no cost. There is no standard commercial subscription; total cost depends on the hardware you run it on.

- **Native multimodal understanding:** Understands and reasons over images and text in the same request, from describing scenes to answering questions about visual content.
- **Strong reasoning and long context:** Built for multi-step reasoning and long-context inputs, so it can work through complex documents, diagrams and extended conversations.
- **Open model weights:** Released as open weights by OpenGVLab, allowing download, inspection and self-hosted deployment.
- **Broad text and code tasks:** Supports general language tasks alongside vision work, including code generation and translation-oriented prompts.
- **Fine-tuning and agents:** Can be fine-tuned and adapted to domain-specific needs, and integrated into agent-style workflows that chain tools and steps.
- **Online chat demo:** Try the models through the official chat UI before deciding whether to self-host.

**Strengths:** Open weights allow self-hosting, auditing and fine-tuning; Strong multimodal understanding across images and text; Handles long-context inputs and multi-step reasoning; Usable both as an online demo and as an on-premise model

**Limitations:** No polished commercial SaaS product; the primary access is a research demo and open weights; Direct access to the demo subdomain can be blocked or hit certificate/DNS quirks depending on network; Running the larger InternVL models locally needs substantial GPU memory; Model cards and tooling assume some technical familiarity

[Profile (Markdown)](https://openaitools.app/tool/internvl3.md)

## Jan

Jan is a desktop application that positions itself as an open replacement for ChatGPT, with the key difference that inference happens on hardware you control. After installing the app you pick a model, and Jan downloads and runs it locally, so prompts, chat history, and files stay on your machine rather than on someone else's server. For heavier workloads you can connect an OpenAI, Anthropic, Gemini, or Mistral key and route requests to the cloud while keeping the same chat interface and local history.

The project is developed in the open by Menlo Research and has accumulated tens of thousands of GitHub stars along with millions of downloads. It exposes OpenAI-compatible local endpoints, which means existing tooling and SDKs can point at Jan instead of a hosted service, and it supports custom assistants with their own instructions and model choices. Recent releases have added an agent surface alongside the classic desktop chat, and a memory feature is announced as coming to carry context and preferences between sessions.

Because it is open source, Jan can be audited, self-built, and extended, and there is no paid tier gating the desktop client. The practical tradeoff is that local model quality and speed depend entirely on your own GPU or CPU, so lighter machines will favor smaller quantized models while cloud keys remain the escape hatch for harder tasks.

**Pricing & Plans:** Jan is free and open source, with no paid tier for the desktop app. Costs only appear if you optionally connect your own paid cloud API keys for providers such as OpenAI or Anthropic.

- **Local-first inference:** Download and run open models such as Llama, Mistral, Qwen, Gemma, and DeepSeek directly on your own machine, with chat data kept on device.
- **Bring your own cloud models:** Optionally connect API keys for OpenAI, Anthropic, Gemini, Mistral, and other providers to route requests to frontier models from the same interface.
- **OpenAI-compatible local API:** Jan exposes a local server endpoint that follows the OpenAI API shape, so existing SDKs and tools can talk to your local models without code changes.
- **Custom assistants:** Create assistants with their own system instructions, model selection, and parameters, then reuse them across conversations.
- **Open source and extensible:** The full desktop client and agent code are public on GitHub, so you can audit behavior, build your own version, and contribute improvements.

**Strengths:** Completely free and open source, with no account required to run local models; Conversation data stays on your device by default; Works across macOS, Windows, and Linux with an OpenAI-compatible local endpoint; Optional cloud key support lets you mix local and frontier models in one app

**Limitations:** Local model quality and speed are capped by your own hardware; Very large models may need substantial RAM or VRAM to be practical; Some cloud-connected and memory features are newer or still rolling out

[Profile (Markdown)](https://openaitools.app/tool/jan.md)

## Frequently Asked Questions

### How do InternVL3 and Jan compare in monthly traffic?

InternVL3 has 0 monthly visits, while Jan has 291,894. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

InternVL3 has a month-over-month growth rate of 0%; Jan has -9.6%.

### How does pricing compare?

InternVL3: Freemium. Free and open. The model weights are released openly for download and self-hosting, and an official online chat demo is available at no cost. There is no standard commercial subscription; total cost depends on the hardware you run it on. Jan: Free. Jan is free and open source, with no paid tier for the desktop app. Costs only appear if you optionally connect your own paid cloud API keys for providers such as OpenAI or Anthropic.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [ChatGPT](https://openaitools.app/tool/chatgpt-4) | 6,048,022,723 | +2.2% | Freemium | OpenAI's flagship multimodal assistant powered by GPT-6 and GPT-5.6 for reasoning, coding, and deep research. | [.md](https://openaitools.app/tool/chatgpt-4.md) |
| 2 | [Google Gemini](https://openaitools.app/tool/gemini-gemini-advanced) | 2,637,398,242 | -0.1% | Freemium | Google's multimodal AI assistant powered by Gemini 4 Carbon (Gemini Carbon 4), Gemini 4 Argon, and Gemini 3.8 Flash with a 1M-token context window and Workspace integration. | [.md](https://openaitools.app/tool/gemini-gemini-advanced.md) |
| 3 | [DeepSeek](https://openaitools.app/tool/deepseek) | 372,761,591 | +4.3% | Freemium | High-efficiency open-weight and hosted reasoning AI powered by DeepSeek-V4 and R1 at ultra-low token costs. | [.md](https://openaitools.app/tool/deepseek.md) |
| 4 | [Kimi AI](https://openaitools.app/tool/kimi-chat) | 19,632,261 | -23.4% | Free | Moonshot AI's long-context assistant for research, coding, and agentic work | [.md](https://openaitools.app/tool/kimi-chat.md) |
| 5 | [Ollama](https://openaitools.app/tool/ollama) | 9,421,973 | +4.3% | Free, Freemium, Paid | Provides a lightweight command-line runtime for local LLMs, though it lacks Jan's built-in graphical user interface. | [.md](https://openaitools.app/tool/ollama.md) |
| 6 | [Chatbot App](https://openaitools.app/tool/chatbot-app) | 2,170,883 | -9.7% | Freemium | Chat with 30+ leading AI models in one clean, fast workspace | [.md](https://openaitools.app/tool/chatbot-app.md) |
| 7 | [Chat & Ask AI](https://openaitools.app/tool/askaichat-app) | 881,428 | +14.9% | Freemium | One subscription, many AI models — chat, search, analyze files and links | [.md](https://openaitools.app/tool/askaichat-app.md) |
| 8 | [Open WebUI](https://openaitools.app/tool/open-webui) | 804,534 | -2.8% | Freemium | Offers a powerful self-hosted web interface for local models, but requires manual Docker setup rather than desktop installation. | [.md](https://openaitools.app/tool/open-webui.md) |
| 9 | [Sakana Chat](https://openaitools.app/tool/sakana-chat) | 704,604 | -20.9% | Freemium | Chat with Japan's frontier models, one interface | [.md](https://openaitools.app/tool/sakana-chat.md) |
| 10 | [AnythingLLM](https://openaitools.app/tool/anythingllm) | 512,269 | +12.9% | Freemium | Specializes in document chat and local RAG workflows, though less focused on general-purpose model tinkering than Jan. | [.md](https://openaitools.app/tool/anythingllm.md) |

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Source: https://openaitools.app/compare/internvl3-vs-jan

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