The open-source AI revolution is in full swing. Whether you want to run AI locally for privacy, reduce API costs, or customize models for specific tasks, there are now dozens of powerful free options available. This guide covers the top open-source AI models, their strengths, token limits, and where to download them.
Quick Comparison Table
| Model | Provider | Context Window | Best For | License |
|---|---|---|---|---|
| Qwen2.5-Turbo | Alibaba | 1,000,000 | Long documents, multilingual | Apache 2.0 |
| Mistral Large 3 | Mistral AI | 256,000 | General, enterprise | Apache 2.0 |
| LLaMA 3.2 | Meta | 128,000 | General purpose, research | Meta License |
| Gemma 3 | 128,000 | Multimodal, lightweight | Gemma License | |
| DeepSeek-V3 | DeepSeek | 128,000 | Reasoning, math | MIT |
| DeepSeek-R1 | DeepSeek | 128,000 | Logic, problem-solving | MIT |
| Qwen2.5 | Alibaba | 128,000 | Coding, general | Apache 2.0 |
| Mistral 7B | Mistral AI | 32,000 | Lightweight, local use | Apache 2.0 |
| Gemma 3 1B | 32,000 | Mobile, edge devices | Gemma License |
1. LLaMA (Meta)
Overview
Meta's LLaMA (Large Language Model Meta AI) series has become the foundation for many open-source AI projects. LLaMA 3.1 and 3.2 offer impressive capabilities rivaling commercial models, with LLaMA 4 expected in late 2025 or early 2026.
Key Specifications
- Context Window: 128,000 tokens
- Sizes: 8B, 70B, and 405B parameters
- Strengths: Strong general performance, extensive community support, foundation for many fine-tuned models
- License: Meta Community License (free for most uses)
Download Links
2. Mistral AI
Overview
Mistral AI, founded by former Google DeepMind and Meta researchers, produces some of the most efficient open-source models. Their models are known for punching above their weight class in performance.
Key Specifications
- Mistral Large 3: 256K context, enterprise-grade performance
- Mistral Large 2: 128K context, multimodal support
- Mistral 7B: 32K context, extremely efficient for local use
- Strengths: High efficiency, strong reasoning, excellent code generation
- License: Apache 2.0 (fully open)
Download Links
3. Gemma (Google)
Overview
Gemma models are Google's lightweight open models built on the same technology as Gemini. Gemma 3, released in March 2025, is multimodal and supports over 140 languages.
Key Specifications
- Context Window: 128K (4B, 12B, 27B) or 32K (1B variant)
- Sizes: 1B, 4B, 12B, and 27B parameters
- Strengths: Multimodal (images, text, video), lightweight, runs on consumer hardware, multilingual
- License: Gemma Terms of Use (free for most applications)
Download Links
4. DeepSeek
Overview
DeepSeek is a Chinese AI lab focused on open-source models with exceptional reasoning and mathematical capabilities. Their models have gained significant attention for matching or exceeding proprietary model performance at a fraction of the cost.
Key Specifications
- DeepSeek-V3: General language and reasoning, MIT licensed
- DeepSeek-R1: Advanced logic and mathematical reasoning
- DeepSeek-Coder V2: Optimized for software development
- Context Window: 128K tokens (flagship models)
- Strengths: Exceptional reasoning, math, cost efficiency
- License: MIT (very permissive)
Download Links
5. Qwen (Alibaba)
Overview
Alibaba's Qwen series offers some of the largest context windows among open-source models. Qwen2.5-Turbo supports up to 1 million tokens, making it ideal for processing entire books or large codebases.
Key Specifications
- Qwen2.5-Turbo: 1M token context window
- Qwen2.5: 128K context, multiple sizes (7B to 72B)
- Qwen3-Coder: Specialized for agentic coding tasks
- Strengths: Massive context, strong multilingual (especially CJK), excellent coding
- License: Apache 2.0
Download Links
How to Run These Models Locally
Several tools make running open-source AI models locally straightforward:
| Tool | Best For | Link |
|---|---|---|
| Ollama | Easiest setup, command line | ollama.com |
| LM Studio | GUI interface, beginners | lmstudio.ai |
| llama.cpp | Maximum performance, developers | GitHub |
| Hugging Face | Python integration, ML workflows | huggingface.co |
Choosing the Right Model
For General Use
LLaMA 3.2 (70B) or Mistral Large 3 offer the best balance of capability and accessibility. Both have strong general knowledge and reasoning abilities.
For Coding
DeepSeek-Coder V2 and Qwen3-Coder are specifically optimized for software development, with strong performance on code generation, debugging, and explanation.
For Limited Hardware
Gemma 3 (4B) and Mistral 7B run efficiently on consumer GPUs and even some high-end CPUs. Gemma 1B can run on mobile devices.
For Long Documents
Qwen2.5-Turbo with its 1M token context is ideal for processing entire books, large codebases, or lengthy conversation histories.
For Math and Reasoning
DeepSeek-R1 excels at complex logical problems, mathematical proofs, and step-by-step reasoning tasks.
Important Considerations
- Hardware Requirements: Larger models (70B+) typically require 48GB+ VRAM or significant RAM for CPU inference
- Quantization: Models can be quantized (compressed) to run on less powerful hardware with minimal quality loss
- Commercial Use: Check each model's license—Apache 2.0 and MIT are generally safe for commercial use
- Updates: Open-source models are frequently updated; check for the latest versions
Last updated: February 6, 2026. Model specifications and availability may change as providers release updates.