Glossary · Definition
Open weights
Open weights means a model's trained parameters are publicly downloadable — you can run, fine-tune, and host the model yourself. Different from full 'open source' (which would also include training code + dataset).
Definition
Open weights means a model's trained parameters are publicly downloadable — you can run, fine-tune, and host the model yourself. Different from full 'open source' (which would also include training code + dataset).
What it means
By 2026 the open-weight ecosystem is competitive with closed-weights frontier: Llama 3.3 / 4, Qwen 3.5, DeepSeek V3.2 / R1, Kimi K2, Mistral Large 3, Gemma 3, Phi-4. Licenses vary: Llama has acceptable-use clauses; Qwen + Phi are Apache 2.0; DeepSeek + Kimi have custom licenses. Always read the license before commercial deployment.
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Why it matters
Open weights are the difference between 'rent your AI from a vendor' and 'own your AI infrastructure.' Privacy-sensitive workloads, regulated industries, cost optimization at scale — all push towards open weights. The 2025-2026 era saw frontier-class quality become available open-weight, changing the build vs buy calculation for serious AI products.
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Frequently asked questions
Open weights vs open source?
Open weights = downloadable parameters. Open source = also training code + recipe. Most 'open' models are weights-only.
Best in 2026?
DeepSeek V3.2 (frontier coding + agentic). Qwen 3.5 72B (general). Kimi K2 (1M context). Llama 4 Maverick (broadest ecosystem).
Related terms
- DefinitionLLM (Large Language Model)An LLM (Large Language Model) is a transformer-based neural network trained on huge text datasets to predict the next token. ChatGPT, Claude, Gemini, DeepSeek — all are LLMs.
- DefinitionMoE (Mixture of Experts)MoE (Mixture of Experts) is an AI architecture where the model has many specialized sub-networks ('experts') and only activates a few per token. Lets the model be huge in total parameters but cheap to run.
- DefinitionFine-tuningFine-tuning is the process of further training a pretrained model on your specific data, baking in style, format, or domain knowledge that's hard to achieve with prompting alone.