{
  "id": 4160669,
  "title": "What’s the difference between proprietary, open weight, and open source AI?",
  "url": "https://urgent.news/2026/08/29/whats-the-difference-between-proprietary-open-weight-and-open-source",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-29T09:00:00.000Z",
  "source": {
    "name": "Fast Company",
    "slug": "fast-company",
    "url": "https://www.fastcompany.com/91594272/what-is-the-difference-between-proprietary-open-weight-open-source-ai-llm-openai-anthropic-llama-deepseek"
  },
  "original_language": "en",
  "account": "Proprietary LLMs are owned by a single entity, typically a large corporation. These models are famously closed-off and opaque, with their code and training data heavily protected. Examples include OpenAI's GPT series, Anthropic's Claude, and Google's Gemini. These models are considered \"frontier\" or the most intelligent, but their high cost often limits access to major companies only. Proprietary LLMs offer simple integration through APIs, making them easy for businesses to adopt.\n\nOpen weight LLMs, on the other hand, allow users to download the model's memory, providing a more affordable and potentially less opaque alternative. These models can be run locally, allowing businesses to keep sensitive information private. However, they often require significant upfront hardware costs. Notable open weight models include Meta's Llama and Chinese LLMs like DeepSeek, Kimi, and Qwen.\n\nOpen source LLMs are the most transparent and malleable among the three categories. Defined as software that can be freely downloaded, modified, and distributed, open source models offer complete transparency into their training code and datasets. This transparency, along with the ability to customize models, makes them appealing for businesses operating in highly regulated industries. Examples of open source models include AI2's OLMo, Eleuther AI's Pythia, Google's T5, and LLM360's Amber and CrystalCoder. Ultimately, the \"best\" type of LLM depends on a company's specific needs and priorities.",
  "summary": "Most people know that when it comes to large language models (LLMs), not all are created equal. Some models are clearly more “intelligent,” while others offer benefits like being significantly cheaper to use. Yet all LLMs fall into one of three categories: proprietary, open weight, or open source. Understanding these distinctions is the key to understanding the benefits and drawbacks of any…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}