Skip to main content

Model Comparison

Detailed comparison of available Einstein AI models to help you choose the right one for your use case.

Quick Comparison Table​

FeatureGPT-4 OmniGPT-4o MiniGPT-3.5 Turbo
Quality⭐⭐⭐⭐⭐ Excellent⭐⭐⭐⭐ Good⭐⭐⭐ Adequate
Speed🐢 Slower (2-5s)🚗 Medium (1-3s)🚀 Fast (0.5-1.5s)
Cost💰💰💰 Highest💰💰 Moderate💰 Lowest
Context Window128K tokens128K tokens16K tokens
Best ForComplex tasksGeneral purposeSimple tasks

GPT-4 Omni​

Overview​

The most advanced model with superior reasoning, creativity, and accuracy.

When to Use:

  • Complex reasoning tasks
  • Code generation and review
  • Detailed analysis and research
  • Legal or medical content
  • Tasks where quality is critical

Strengths​

✅ Highest Quality: Best reasoning and understanding
✅ Complex Tasks: Handles multi-step problems effectively
✅ Code Generation: Excellent at writing and debugging code
✅ Consistency: Most reliable outputs
✅ Large Context: 128K token context window

Limitations​

❌ Slower: 2-5 seconds average response time
❌ Most Expensive: 15x more expensive than GPT-3.5
❌ Overkill: Wasted for simple tasks

Performance Metrics​

MetricValue
Avg Response Time3.2s
Tokens/Second~100
Avg Input Tokens150
Avg Output Tokens300
Cost per 1K Tokens$0.03 (input), $0.06 (output)

Use Case Examples​

✅ Good Uses:

- "Analyze this legal contract and identify potential risks"
- "Write a Python script to process customer data with error handling"
- "Explain the implications of this financial regulation"
- "Review this code and suggest architectural improvements"

❌ Poor Uses:

- "Classify this email as spam or not spam" (too simple)
- "Extract the date from this text" (waste of capability)
- "Say hello in Spanish" (use GPT-3.5)

GPT-4o Mini​

Overview​

Balanced model offering good quality at moderate cost - the "sweet spot" for most use cases.

When to Use:

  • General customer support
  • Content generation
  • Data summarization
  • Business communications
  • Most day-to-day AI tasks

Strengths​

✅ Good Quality: Near GPT-4 quality for most tasks
✅ Faster: 1-3 seconds average response time
✅ Affordable: 5x cheaper than GPT-4 Omni
✅ Versatile: Handles wide range of tasks well
✅ Large Context: 128K token context window

Limitations​

❌ Not the Best: Quality below GPT-4 Omni
❌ Moderate Cost: 3x more than GPT-3.5
❌ Complex Tasks: May struggle with very complex reasoning

Performance Metrics​

MetricValue
Avg Response Time1.8s
Tokens/Second~150
Avg Input Tokens150
Avg Output Tokens250
Cost per 1K Tokens$0.006 (input), $0.012 (output)

Use Case Examples​

✅ Good Uses:

- "Write a professional email response to this customer inquiry"
- "Summarize this meeting transcript"
- "Generate product descriptions from specifications"
- "Answer customer support questions"

✅ Excellent For:

  • 80% of business use cases
  • Customer-facing chatbots
  • Content drafting
  • General Q&A

GPT-3.5 Turbo​

Overview​

Fast, affordable model perfect for high-volume, straightforward tasks.

When to Use:

  • Simple classification
  • High-volume tasks
  • Quick responses needed
  • Cost is primary concern
  • Speed-critical applications

Strengths​

✅ Fastest: 0.5-1.5 seconds average response time
✅ Cheapest: 15x less expensive than GPT-4 Omni
✅ High Volume: Perfect for scaling
✅ Simple Tasks: Excellent for straightforward requests
✅ Real-Time: Fast enough for live interactions

Limitations​

❌ Lower Quality: Less capable reasoning
❌ Smaller Context: 16K token window
❌ Complex Tasks: Struggles with multi-step problems
❌ Less Consistent: More variation in outputs

Performance Metrics​

MetricValue
Avg Response Time0.9s
Tokens/Second~200
Avg Input Tokens100
Avg Output Tokens150
Cost per 1K Tokens$0.002 (input), $0.004 (output)

Use Case Examples​

✅ Good Uses:

- "Classify this support ticket by category"
- "Extract the order number from this email"
- "Translate this text to Spanish"
- "Generate a simple product title"

✅ Excellent For:

  • Data classification
  • Simple extraction
  • Translation
  • High-volume automation

Decision Matrix​

By Use Case Priority​

Quality is Critical: → GPT-4 Omni

  • Legal documents
  • Medical content
  • Complex analysis
  • Code review

Balanced Quality & Cost: → GPT-4o Mini

  • Customer support
  • Content generation
  • General business tasks
  • Most use cases (80%)

Speed & Volume Matter Most: → GPT-3.5 Turbo

  • Simple classification
  • Data extraction
  • High-volume tasks
  • Real-time responses

Cost Comparison​

10,000 Requests/Month Example​

Scenario: 200 input tokens, 300 output tokens per request

ModelMonthly CostCost per Request
GPT-4 Omni$300$0.030
GPT-4o Mini$60$0.006
GPT-3.5 Turbo$20$0.002

Potential Savings:

  • GPT-4o Mini vs GPT-4 Omni: $240/month (80% savings)
  • GPT-3.5 vs GPT-4 Omni: $280/month (93% savings)

Hybrid Strategy​

Best Practice: Use Multiple Models​

Route requests to the appropriate model based on complexity:

function selectModel(request) {
  if (isComplex(request)) {
    return "GPT-4-Omni";      // 10% of requests
  } else if (isStandard(request)) {
    return "GPT-4o-Mini";     // 60% of requests
  } else {
    return "GPT-3.5-Turbo";   // 30% of requests
  }
}

Example Results:

  • Average cost per request: $0.008 (vs $0.030 with GPT-4 only)
  • Savings: 73% while maintaining quality where needed

Testing Recommendations​

How to Choose​

  1. Start with your actual use case prompts
  2. Test all three models
  3. Evaluate:
    • Quality: Does it meet your standards?
    • Speed: Is it fast enough?
    • Cost: Can you afford it at scale?
  4. Scale test: Try with 100+ variations
  5. Measure: Track success rate
  6. Decide: Choose the right model for each use case type

Success Criteria​

Define what "good enough" means:

  • Customer support: 90% quality acceptable
  • Legal analysis: 99% quality required
  • Data classification: 95% accuracy needed
  • Content generation: 85% quality acceptable

Choose the right model for each task to balance quality, speed, and cost effectively.