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2.2.5. Model Types and Use Cases
- Concept: Different models are optimized for different tasks
- Purpose: Match model capabilities to requirements
- Benefit: Optimize for cost, latency, and capability
Comparative Table: Model Selection
| Model | Primary Use | Key Capability | Cost |
|---|---|---|---|
| GPT-4 | Complex reasoning | Multi-turn, deep analysis | Highest |
| GPT-4 Turbo | Balanced performance | 128k context window | High |
| GPT-4o | Multimodal | Vision + text combined | High |
| GPT-3.5-Turbo | Cost-effective | High throughput | Low |
| DALL-E 3 | Image generation | Text-to-image | Per-image |
| Whisper | Speech recognition | Audio transcription | Per-minute |
Key Trade-Offs:
- Capability vs. Cost: More capable models cost more per token
- Context Window vs. Latency: Larger contexts enable more information but increase processing time
Reflection Question: Your chatbot uses GPT-4 but costs are too high. Users mostly ask simple FAQs. What's your optimization strategy?