Lesson 1 of 5
Why Multiple AI Models?
📖 6 min✨ 35 XP
No single AI model is best at everything. Different models have different strengths, costs, and capabilities. Multi-model orchestration means using the right tool for each job.
The Model Landscape (2024-2025)
GPT-4 (OpenAI)
- **Strengths:** Creative writing, complex reasoning, broad general knowledge
- **Best for:** Content creation, brainstorming, conversational AI
- **Cost:** High ($0.03 per 1K tokens)
- **Context window:** 128K tokens
Claude (Anthropic)
- **Strengths:** Long document analysis, instruction-following, safety
- **Best for:** Document processing, analysis, coding
- **Cost:** Medium ($0.015 per 1K tokens)
- **Context window:** 200K tokens
Gemini (Google)
- **Strengths:** Multimodal (text + images), fast, Google integration
- **Best for:** Image understanding, real-time applications, research
- **Cost:** Low to Medium
- **Context window:** 1M tokens
DeepSeek/Open-Source Models
- **Strengths:** Cost-effective, customizable, self-hosted option
- **Best for:** Bulk processing, specialized tasks, privacy-sensitive work
- **Cost:** Very low (often free)
Key Insight
Each model was trained differently, on different data, with different objectives. This creates unique capabilities—and unique blind spots.
When to Use Multiple Models
- **Validation:** Run critical tasks through 2-3 models and compare results
- **Triangulation:** Combine insights from different models for comprehensive analysis
- **Cost optimization:** Use cheaper models for simple tasks, expensive for complex
- **Specialized tasks:** Route each task to the model best suited for it
- **Redundancy:** Fallback to another model if primary fails
Lesson 1 / 5