Best Open-Source AI Models You Can Run Locally in 2026

Why Local Models Matter

Every time you use ChatGPT or Claude in the cloud, your data passes through someone else's servers. With local models, everything stays on your machine — your conversations, documents, and prompts never leave your hard drive. And thanks to breakthroughs in model compression, the quality gap between local and cloud AI has shrunk dramatically.

To understand what's actually inside these model files, see our explainer on what an LLM really is. To learn how to set them up, check our Ollama guide.

🏆 Best All-Rounders

1. Llama 3.1 8B

VRAM needed: ~6 GB | Best for: General chat, writing, Q&A

Meta's Llama 3.1 remains the gold standard for local AI. The 8B parameter version runs comfortably on any modern GPU and handles conversation, writing assistance, and general knowledge tasks with impressive quality. If you're downloading your first model, start here.

2. Qwen 2.5 7B

VRAM needed: ~6 GB | Best for: Multilingual tasks, reasoning

Alibaba's Qwen series has been quietly excellent. The 2.5 generation brings strong reasoning capabilities and outstanding multilingual support — particularly for Chinese, Japanese, Korean, and European languages. A great alternative to Llama if you work across languages.

3. Mistral 7B

VRAM needed: ~6 GB | Best for: Fast, efficient general use

Mistral's flagship small model punches above its weight. It's fast, efficient, and particularly good at following instructions. The company's focus on efficiency means this model often feels snappier than competitors of the same size.

💻 Best for Coding

4. DeepSeek Coder V2

VRAM needed: ~8 GB | Best for: Code generation, debugging

DeepSeek's coding-focused model is remarkably capable. It handles Python, JavaScript, TypeScript, Rust, and dozens of other languages with quality that rivals cloud coding assistants. If you're doing any local AI-assisted development, this is the one to try.

5. CodeLlama 13B

VRAM needed: ~10 GB | Best for: Code completion, documentation

Meta's code-specific Llama variant. Larger than DeepSeek Coder but excellent at code completion, generating documentation, and explaining existing code. Needs a bit more VRAM but rewards you with strong code understanding.

🧠 Best for Reasoning

6. DeepSeek-R1 (Distilled)

VRAM needed: ~8-12 GB | Best for: Complex reasoning, analysis

The distilled versions of DeepSeek's reasoning model bring chain-of-thought capabilities to consumer hardware. It "thinks through" problems step by step, making it excellent for complex questions, data analysis, and structured problem-solving. This is the model that proved open-source can compete with frontier AI.

🎤 Best for Transcription

7. Voxtral (via Vox Bar)

VRAM needed: ~4-6 GB | Best for: Speech-to-text, dictation

Mistral's Voxtral model powers Vox Bar's local transcription engine. It handles multiple languages, understands context and natural speech patterns, and runs entirely on your GPU. For voice-to-text specifically, it's purpose-built and optimised for real-time local use.

Local vs. Cloud: The Real Comparison

Let's be honest about the trade-offs:

• Cloud wins on: Raw capability for the most complex tasks, larger context windows, multimodal features (image generation, video analysis)
• Local wins on: Privacy (absolute), cost (one-time or free), speed (no network latency), reliability (works offline), ownership (no terms changes)

For everyday tasks — writing, coding assistance, Q&A, transcription, translation, summarisation — local models are already good enough. You only need cloud AI for frontier-level reasoning tasks, and even that gap is closing fast.

See our full comparison page for more on how Vox Bar compares to cloud transcription services specifically.