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Custom Models

Adding and configuring custom AI models for video analysis.

VPA comes with pre-configured support for popular AI models, but you can also add custom models for specialized use cases or to access the latest releases before we add official support.

Pre-configured Models

VPA includes optimized configurations for:

Vision/Analysis Models

  • GPT-4o (OpenAI)
  • GPT-4 Vision (OpenAI)
  • Claude 3.5 Sonnet (Anthropic)
  • Claude 3 Opus (Anthropic)
  • Gemini Pro Vision (Google)

Text Generation Models

  • GPT-4 Turbo (OpenAI)
  • Claude 3 Sonnet (Anthropic)
  • Gemini Pro (Google)
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Model selection

Different models have different strengths. GPT-4o excels at detailed visual analysis, while Claude 3.5 Sonnet offers excellent prompt generation capabilities.

Adding a Custom Model

  1. Navigate to Settings > Models
  2. Click "Add Custom Model"
  3. Enter the model configuration (see below)
  4. Test the model with a sample analysis
  5. Save if the test succeeds

Model Configuration

Each custom model requires the following configuration:

Required Fields

  • Model ID: The exact model identifier used by the provider (e.g., "gpt-4o-2024-05-13")
  • Provider: Which API provider to use (OpenAI, Anthropic, Google)
  • Display Name: A friendly name shown in the UI
  • Type: Vision (for analysis) or Text (for generation)

Optional Fields

  • Max Tokens: Maximum output tokens (default varies by provider)
  • Temperature: Creativity level 0-1 (default: 0.7)
  • System Prompt Override: Custom instructions for this model
  • Cost per Token: For usage estimation

Custom OpenAI-Compatible Endpoints

VPA supports OpenAI-compatible API endpoints, allowing you to use:

  • Self-hosted models (LLaMA, Mistral via vLLM)
  • Azure OpenAI Service
  • OpenRouter and other aggregators
  • Any OpenAI-compatible endpoint

Configuration for Custom Endpoints

  • Base URL: Your endpoint URL (e.g., https://your-deployment.azure.com)
  • API Version: Required for Azure (e.g., "2024-02-15-preview")
  • Deployment Name: Your Azure deployment name (if applicable)
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Vision capability required

For video analysis, your custom model must support image inputs. Text-only models cannot be used for frame analysis.

Model Presets

Save your custom model configurations as presets for quick switching between different setups:

  • Quick Analysis: Faster, lower-cost model
  • High Quality: Best model for important projects
  • Budget: Most cost-effective option

Troubleshooting Custom Models

  • Model not found: Verify the exact model ID with your provider
  • Unauthorized: Check API key permissions for the model
  • No image support: Ensure the model has vision capabilities
  • Rate limits: Some models have lower limits; reduce frame count