Open-weight models

Extracted Attributes

• Field

  Artificial Intelligence / Machine Learning

• Core Components Released

  Learned model parameters (weights and biases)

• Common Permissive Licenses

  Apache 2.0, MIT

• Key Technical Capabilities

  Local inference, fine-tuning, quantization, red-teaming/stress-testing, and offline deployment

• Largest Model (as of July 2026)

  Kimi K3 (Moonshot AI) at 2.8 trillion parameters

• Second Largest Model (as of July 2026)

  Qwen3.8 (Alibaba Cloud) at 2.4 trillion parameters

• Unreleased Components (vs. Open Source)

  Training data, training pipeline source code, and intermediate checkpoints

Timeline

• Moonshot AI's Kimi K3 (2.8 trillion parameters) and Alibaba Cloud's Qwen3.8 (2.4 trillion parameters) are recognized as the largest open-weight frontier models. (Source: Wikipedia)

  2026-07-01

Open weights

Open weights are the publicly released learned parameters of a trained artificial intelligence model, principally its weights and biases. In an artificial neural network, weights are numerical values that determine how strongly inputs contribute to the network's calculations. Publishing these values allows others to download and use the model, while permission to modify, fine-tune or redistribute it depends on its license. The term is commonly applied to large language models, diffusion models and other generative artificial intelligence. Open weights is contrasted to open-source artificial intelligence, which considers the public release of not only parameters but the model's source code, training data, evaluation results, intermediate checkpoints and technical documentation. Open weights AI is a major issue in geopolitics and artificial intelligence controversies, sometimes characterized as an AI arms race or AI Cold War between the US and China. Broadly, models released by Chinese companies, such as DeepSeek, Alibaba Cloud, Moonshot AI and Z.ai, use an open weights framework, under more permissive software licenses like Apache or MIT. United States AI companies, including OpenAI, Anthropic, Google DeepMind, SpaceXAI, and Meta Superintelligence Labs, favor a proprietary software framework, especially for larger models. These are in part national government policy decisions, because public access to AI technology has many consequences. Some US politicians have called to restrict US public access to Chinese AI tools. Popular open-source artificial intelligence project categories include large language models (LLM), machine translation tools, and chatbots. Debate over the benefits and risks of open-sourced AI involve a range of factors like security, privacy and technological advancement. As of July 2026, the largest open weight frontier model is Kimi K3, developed by Moonshot AI, at 2.8 trillion parameters, followed by Alibaba Cloud's Qwen3.8 at 2.4 trillion parameters. Open weights releases outside China are led by US lab Thinking Machines Lab, Nvidia's Nemotron family, and French Mistral AI.

Web Search Results

• Open-weights Model | LLM Knowledge Base

  All terms # Open-weights Model An open-weights model is a Large Language Model (LLM)") whose parameters (or "weights") are publicly accessible and can be used and modified without restriction. Unlike closed or Proprietary Models, open-weights models are often shared within the AI community for research, educational purposes, or to foster innovation. They can be fine-tuned or adapted to specific tasks and contribute to the transparency and collaborative advancement of AI technology. Open-weights models can also facilitate reproducibility in AI research, allowing others to validate and build upon existing work. Open weights also allow independent deployment optimizations such as quantization, which can reduce memory requirements for local or lower-cost inference. [...] In contrast to Open-source Models, open-weights models do not provide access to the model architecture or source code for the training pipeline. In an interview with Sequoia Capital, Andrej Karpathy used a nice analogy to software, saying "an open-weights model is a little bit like tossing over a binary for an operating system." Examples of open-weights models are the LLaMA series by Meta AI and Mistral 7B / Mixtral 8x7B by Mistral AI. The LLM Knowledge Base is a collection of bite-sized explanations for commonly used terms and abbreviations related to Large Language Models and Generative AI. It's an educational resource that helps you stay up-to-date with the latest developments in AI research and its applications. Previous Open-source Model One-shot Prompt Neural Network

• How Open-Weight Models Changed the AI Landscape

  An open-weight model is one where the company has published the trained parameters. Anyone can download them, run the model on their own hardware, and adjust it for their own data. The training data and the full training code, however, usually stay private. The term is “open weight” rather than “open source” for this reason. In traditional software, “open source” means the full source code is available to inspect and reproduce. Most AI models marketed as open source are actually open weight, where the trained model is public, while the process that produced it remains closed. This distinction matters because the published weights, paired with detailed technical reports, are what allow other teams to study a design and build on top of it. [...] Share Bruno Gavino - Codedesign.org Jun 17 Open-weight models are the new ‘free internet’ moment — whoever fine-tunes on the right brand signals first wins the GEO race. ReplyShare 2 more comments... No posts ### Ready for more? © 2026 ByteByteGo · Privacy ∙ Terms ∙ Collection notice Start your SubstackGet the app Substack is the home for great culture

• Medium

  ## What Are Open-Weight LLMs? Open-weight large language models (LLMs) are AI systems where the model’s parameters — the mathematical weights that determine how it processes and generates text — are publicly available. These weights represent the knowledge and capabilities the model has learned during training. Think of an LLM as a complex mathematical function with billions of parameters that have been adjusted during training to produce coherent and relevant text. In an open-weight model, these parameters are released to the public, allowing organisations to download and run the model on their own infrastructure. [...] ## Comprehensive risk assessment Open-weight models allow for “red teaming” or “stress testing” — probing the model’s boundaries by testing its safeguards. This gives you a clear picture of potential outputs in edge cases, allowing you to implement appropriate guardrails specific to your use cases. What is red teaming: Similar to how cybersecurity experts test systems for vulnerabilities, red teaming for LLMs involves deliberately challenging the model to produce inappropriate or harmful content to identify potential risks before deployment. ## Advanced observability With open-weight models, you can perform sophisticated monitoring and analysis: ## Data training transparency [...] ## Deployment flexibility Open-weight models can be deployed in various environments — from cloud to on-premises to edge devices — allowing AI capabilities even in environments with limited or no internet connectivity. For making strategic technology decisions, open-weight models offer a compelling alternative to the subscription-based API economy that has dominated the early LLM landscape. ## Guaranteed availability and business continuity

• What is an Open-Weight Model? | Stanford HAI

  # What is an Open-Weight Model? An Open-Weight Model is an AI model whose core components are publicly released, allowing anyone to download it. This lets users run the model on their own computers, study how it works, and even modify it for their own specific needs. ## Open-Weight Models mentioned at Stanford HAI What is an Open-Weight Model? | Stanford HAI Explore Similar Terms: Open Source | Closed Source | Foundation Model See Full List of Terms & Definitions ### Stay Up To Date Get the latest news, advances in research, policy work, and education program updates from HAI in your inbox weekly. Sign Up For Latest News ###### Navigate About Events AI Glossary Careers Search ###### Participate Get Involved Support HAI Contact Us Skip to content

• Best open-weight models for coding

  ### What are open-weight models? Open-weight models are AI models whose final trained parameters are publicly released, so anyone can run and fine-tune them, while the training data and code stay private. Most modern frontier coding models—including GLM-5.2, Kimi K2.6, and DeepSeek-V4—are open weight, typically released under permissive licenses like MIT or Apache 2.0. ### What is the difference between open weight and open source models? Open-source models release their weights, architecture, and training code, so the model can be fully scrutinized and recreated. Open-weight models release only the trained parameters, so you can use and fine-tune the model but cannot reproduce how it was built. Closed models keep both weights and training details private. [...] Open-source models make their weights, architecture, and training code freely available for anyone to use, modify, and distribute. Open-weight models sit a step below: they release only the final trained parameters, without the training data or code, so users can run and fine-tune the model but can’t fully scrutinize or recreate how it was built. Most modern frontier coding models—like GLM-5, Kimi K2.6, and DeepSeek V4—are technically open-weight, released under permissive licenses. [...] The 2026 open-weight model ecosystem has grown significantly and will continue to improve in the coming months. Thus far, DeepSeek leads on efficiency and reasoning; Z.ai on long-running engineering agents; Qwen on local and self-hosted flexibility; Kimi on multimodal and multi-agent workflows; MiniMax on low-cost production throughput; and Meta’s Llama 4 on native multimodality and large context windows. Together they show that open-weight models are now capable enough to handle real engineering work, provided you match the right model to the right task.