#Model Overrides

A specialist can have preferred models, but your workspace or personal settings may need a different model for cost, latency, availability, privacy, or quality reasons.

Model overrides let you choose a different model without rebuilding the specialist.

#When to Use Overrides

Use model overrides when you want to:

• standardize a workspace on a preferred provider
• reduce cost for routine tasks
• use a stronger model for complex work
• test a specialist with different models
• avoid a provider that is unavailable in your workspace

#Selection Order

When a specialist responds, The AI Platform chooses a model from the available configuration. The exact routing can depend on provider setup and workspace policy, but the general precedence is:

1. personal override
2. workspace or organization override
3. specialist preference
4. workspace default or system fallback

If a selected provider is not connected or allowed, The AI Platform may require you to connect it or choose another model.

#Where Overrides Appear

Model configuration can appear on specialist detail pages, review screens, or response metadata depending on the specialist type and your permissions.

Open a specialist detail page to review its setup, capabilities, and available model-related information.

#Good Override Practices

• Prefer workspace defaults for broad policy.
• Use personal overrides for experimentation.
• Clear overrides when testing is complete.
• Choose cheaper models only after checking quality on representative tasks.
• Use stronger models for complex reasoning, code review, architecture, or high-risk work.

#Next Steps

• Specialists Overview
• Working with Specialists
• Connections for adding model providers