Open source AI models are machine learning models with publicly available weights, architectures, and often training code, allowing users to freely use, modify, and distribute them. Unlike proprietary models accessible only through APIs, open source models can be downloaded, fine-tuned, and deployed on local infrastructure, offering greater cost control, data sovereignty, and customization. They eliminate vendor lock-in, enabling users to switch providers or self-host.
These models are available for various applications, including large language models (LLMs) for text generation, speech models for transcription and voice cloning, vision models for image analysis, and multimodal models that combine text, image, and audio understanding. Recent advancements have led to open source AI models that rival and sometimes surpass proprietary models like GPT in coding, reasoning, and multimodal tasks. Examples include Llama 4, DeepSeek-V3, and Qwen3, which demonstrate strong performance across reasoning, coding, and multilingual understanding. The "open" aspect of these models exists on a spectrum, ranging from truly open-source with permissive licenses like Apache 2.0 and MIT to "open weights" models with more restrictive licenses.
