12. LLM Model Awareness - Know Your Models
Not all AI models are equal. GPT-4 excels at certain tasks, Claude at others. Understanding model strengths helps you choose the right tool for the job and write better prompts.
(Plot Twist: Using GPT-4 for everything is like using a Ferrari to get groceries. Works, but expensive. Learn when to use the Honda. 🏎️)
Model Comparison Matrix
| Feature | GPT-4 Turbo | Claude 3.5 | Gemini Pro |
|---|---|---|---|
| Context Window | 128K | 200K | 1M |
| Code Quality | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐ |
| Speed | Fast | Moderate | Very Fast |
| Best For | General, Creative | Code, Analysis | Research, Multimodal |
When to Use Each Model
Use GPT-4 / GPT-4o for:
- • Creative tasks (naming, writing, brainstorming)
- • General-purpose coding
- • When you need function calling / structured output
- • Rapid prototyping with Code Interpreter
Use Claude 3.5 Sonnet for:
- • Production code generation (cleanest output)
- • Large codebase analysis (200K context)
- • Complex refactoring tasks
- • When you want more conservative/careful suggestions
Use Gemini Pro for:
- • Research and latest information (Google Search integration)
- • Massive context needs (1M tokens)
- • Multimodal tasks (video, audio analysis)
- • Fast, real-time applications
Practical Model Selection Guide
- Debugging production issue: Claude (paste large logs)
- Learning new framework: Gemini (latest docs via search)
- Writing tests: Claude (most thorough)
- Architecture brainstorm: GPT-4 (creative)
- Documentation: GPT-4 or Claude (both good)
- Code review: Claude (conservative, catches more)
Model Limitations to Know
- ⚠️ Training Cutoff: Models don't know events after their training date
- ⚠️ Hallucinations: Can confidently provide wrong information
- ⚠️ Context Limits: Even 1M tokens can't fit entire large codebases
- ⚠️ Cost Varies: GPT-4 more expensive than GPT-3.5, Claude Opus > Sonnet