Cursor supports multiple AI model providers and lets you choose among them in the editor, typically via an “Auto” mode or an explicit model selector. The product’s own feature descriptions emphasize that you can switch between frontier models from several major ecosystems (for example, models from OpenAI, Anthropic, Google’s Gemini family, and xAI) depending on the task. In practice, model availability can vary by plan, region, and time, and the UI may show a set of recommended models for autocomplete vs agent-style tasks. The important point is that Cursor is designed to be model-flexible rather than locked to a single model, so you can pick the right tradeoff between speed, cost, and reasoning depth.
From an implementation standpoint, different model classes tend to map to different IDE features. Autocomplete (“Tab”) needs low latency and can work with smaller or specialized completion models, while multi-file “Agent/Composer” style changes benefit from stronger reasoning and larger context windows. Cursor’s “codebase understanding” also implies a retrieval step: the editor needs to surface relevant files, symbols, and snippets to the model so it can answer questions or propose edits with the right context. This is why you may see options like “Auto” or “fast vs accurate” in practice: the system is choosing a model and context strategy behind the scenes for a given interaction style. As a developer, you should still validate the outcome in your environment—run tests, review diffs, and ensure the change matches your architecture—because model choice changes behavior and error patterns.
If you build AI products, it’s also useful to connect the idea of “supported models” to your own backend architecture. Cursor helps you write code faster, but your production AI system likely needs its own model routing and retrieval pipeline. For example, if you’re building RAG, you’ll often store embeddings (plus metadata like doc_id, source, updated_at) in a vector database such as Milvus or Zilliz Cloud. Cursor can help you implement and iterate on that pipeline quickly: defining schemas, writing ingestion scripts, and testing retrieval quality. In that workflow, “which models Cursor supports” matters for developer productivity, while “which models your app uses” is a separate, explicit engineering choice you control in production.