Discover State-of-the-Art AI Models
The most comprehensive database to compare 2,226 open-weights and proprietary LLMs. Filter by reasoning capabilities, tool use, and more.
Everything You Need to Know About LLM Models
Understanding Open Weights vs. Proprietary Models
When choosing a Large Language Model (LLM), one of the first decisions is between open-weights and proprietary models.Open-weights models allow you to run the model on your own infrastructure, giving you full control over privacy and customization. Popular examples include the Llama series, Qwen, and DeepSeek.
Proprietary models, like GPT-5.2 or Claude 4.5 Opus, are accessed via API. They often offer state-of-the-art performance but come with usage costs and data privacy considerations.
Reasoning Capabilities & Tool Use
Modern LLMs are evolving beyond text generation. Reasoning models (like GPT-5.2 Thinking or Claude 4.5) uses "chain of thought" to solve complex logic, math, and coding problems with higher accuracy.
Tool Use (or Function Calling) is critical for building automated agents. Models with high "Tool Call" capabilities can reliably interact with external APIs, databases, and software environments, making them the engines of agentic workflows.
The Power of Context Windows
The context window determines how much information a model can process at once. Measured in tokens, it includes both your prompt and the model's previous responses.
Models like Gemini 3 offer massive windows (exceeding 1 million tokens), allowing users to upload entire codebases or massive document libraries for analysis without losing track of subtle details.
Tokenization and Economics
LLMs don't read words; they process tokens (roughly 0.75 words per token). This is how usage is metered and charged by API providers like OpenAI and Anthropic.
Understanding token efficiency is key to managing costs in 2026, especially when deploying multi-agent systems that make recursive calls to models like GPT-5.2 Pro or Claude 4.5 Sonnet.