# Model Optimization

Test different AI models to find the best fit for your use case. Compare performance, cost, and capabilities across multiple providers with side-by-side analysis.

## Available Models

Access through Actions → Model Optimization. Test and compare different AI models to find the optimal performance for your specific use case.

### GPT-4o

OpenAI

- **Context:** 128,000 tokens  
- **Status:** Active  
  
Latest GPT-4 model with multimodal capabilities and improved reasoning.

### GPT-4o Mini

OpenAI

- **Context:** 128,000 tokens  
- **Status:** Active  
  
Smaller, faster version of GPT-4o with excellent cost-performance ratio.

### Claude 3 Opus

Anthropic

- **Context:** 200,000 tokens  
- **Status:** Active  
  
Anthropic's most capable model with superior reasoning and analysis capabilities.

### Claude 3 Sonnet

Anthropic

- **Context:** 180,000 tokens  
- **Status:** Active  
  
Balanced performance and speed with strong reasoning capabilities.

## Test Configuration

Configure test parameters and inputs to compare model performance on your specific use case.

### Model Selection

Choose which models to test and compare:

- GPT-4o (OpenAI)  
- GPT-4o Mini (OpenAI)  
- Claude 3 Opus (Anthropic)  
- Claude 3 Sonnet (Anthropic)

### Temperature Settings

Control randomness in model responses:

- **Temperature:** 0.7  
  
0 (Deterministic) | 1 (Random)  
  
Lower values give more deterministic outputs, higher values more random.

### System Prompt

Optional system prompt to guide model behavior:

System prompts help set context and behavior for the models.

## Model Comparison Features

### 🔄Side-by-Side Comparison

Test the same input across multiple models simultaneously and compare responses in real-time.

- Compare response quality  
- Evaluate response consistency  
- Analyze different approaches

### 📊Performance Testing

Run your specific inputs through different models to find the best performance for your use case.

- Use your manual inputs  
- Test edge cases  
- Measure accuracy scores

### ⚙️Parameter Optimization

Fine-tune temperature and other parameters to optimize model performance for your specific needs.

- Temperature adjustment  
- System prompt testing  
- Response consistency

### 💰Cost vs Performance

Analyze the cost-effectiveness of different models to make informed decisions about deployment.

- Token usage tracking  
- Performance per dollar  
- Scaling cost analysis

## Model Testing Workflow

1. **Select Models to Test**  
   Choose which models you want to compare based on your requirements for performance, cost, and capabilities.

2. **Configure Test Parameters**  
   Set temperature, system prompts, and other parameters to optimize for your specific use case.

3. **Run Test Inputs**  
   Use your manual inputs or create new test cases to evaluate model performance across different scenarios.

4. **Analyze Results**  
   Compare performance scores, response quality, and cost-effectiveness to make informed model selection decisions.

5. **Deploy Best Model**  
   Select the optimal model for your application and continue with prompt optimization using your chosen model.

## Model Selection Guide

### Performance Requirements

- **Complex reasoning:** Claude 3 Opus, GPT-4o  
- **Fast responses:** GPT-4o Mini, Claude 3 Sonnet  
- **Large context:** Claude 3 Opus (200k tokens)  
- **Consistent output:** Lower temperature settings

### Cost Considerations

- **High volume:** Consider GPT-4o Mini for cost efficiency  
- **Premium quality:** Claude 3 Opus or GPT-4o for best results  
- **Balanced approach:** Claude 3 Sonnet for good cost-performance  
- **Token efficiency:** Test with your actual inputs

### Use Case Specifics

- **Customer support:** Consistency and reliability matter most  
- **Content generation:** Creativity and quality balance  
- **Data analysis:** Reasoning and accuracy priority  
- **Real-time responses:** Speed and cost efficiency

## Model Testing Best Practices

### Test with Representative Data

Use your actual manual inputs and real user scenarios to get accurate performance comparisons.

### Consider Context Length

Test with inputs of varying lengths to understand how models handle different context sizes.

### Evaluate Consistency

Run the same input multiple times to test response consistency, especially important for business applications.

### Monitor Cost Over Time

Track token usage and costs during testing to project real-world expenses at scale.

### Test Edge Cases

Include challenging inputs and edge cases to see how different models handle difficult scenarios.