Model Selection

Apps can usually rely on automatic model selection, but you can expose model choices or pin a model when a use case needs tighter control.

When to Customize

Use the platform default unless your app has a clear reason to choose a model. The default keeps fallback behavior enabled and picks efficient models when possible.

Customize model behavior when:

• Users should choose between faster, cheaper, or more capable models.
• A workflow needs a specific model's strengths.
• You want to keep different app features on different model tiers.

Let Users Choose

The common pattern is a model selector in your app, such as a dropdown or settings panel. The app can list available models, store the user's selection, and pass the selected model ID to the AI request.

Model selection is bounded by the user's credit balance. If a user selects an expensive model without enough credits, the request returns a usage-limit error before generation runs.

Use the model IDs from the available models table. For implementation details, see the AI text reference and related AI reference pages.

Pinning a Model

Pinning a model gives you precise control, but it changes failure behavior. When a request specifies a model, the platform uses that model exactly. If the model does not support the request or the user lacks credits, the request fails instead of silently falling back.

Model IDs can change as the platform evolves. If a pinned model is deprecated, the platform may use a reasonable replacement so the app keeps working, but you should refresh selectors and labels against the available models table over time.

Reasoning Effort

Text models can support reasoning effort settings. Higher effort can produce more careful answers, but it takes longer and uses more credits. Lower effort is better for simple formatting, classification, or quick generation.

Reasoning effort applies to text AI features, not image, video, or speech generation.