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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:

Temperature Settings

Control randomness in model responses:

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.