Ensure AI model configurations accurately reflect official specifications and avoid hardcoded assumptions. Model parameters, token limits, capabilities, and naming should match vendor documentation rather than using generic defaults. Key practices: - Verify token counts include both input and output limits (e.g., `tokens: maxInput + maxOutput`) - Avoid hardcoded parameter defaults that don't apply universally (like `default: 0.8` for strength) - Use consistent naming conventions (e.g., "Imagen 4" with spaces, remove "Instruct" for base models) - Add "Preview" or "Beta" labels for experimental models - Remove deprecated models only when official end-of-life dates are announced - Cross-reference specifications with official API documentation Example of proper model configuration: ```typescript { description: 'Gemini 2.5 Flash 是 Google 最先进的主力模型', displayName: 'Gemini 2.5 Flash', // Clear, consistent naming id: 'google/gemini-2.5-flash', contextWindowTokens: 1_048_576, // Verified against official docs maxOutput: 65_535, // Separate input/output limits // No hardcoded parameter defaults } ``` This prevents user confusion, billing errors, and ensures reliable model behavior across different AI providers.