The AI model ecosystem is evolving rapidly. As a prompt engineer, understanding the strengths and trade-offs of different models is critical.
Key Concepts
- Proprietary Models: GPT-4/4o (OpenAI), Gemini 1.5/2.0 (Google), Claude 3.5/4 (Anthropic) — state-of-the-art, API-based, pay-per-token
- Open Source Models: Llama 3 (Meta), Mistral, Qwen, Gemma — self-hostable, customizable, no per-token cost
- Model Families: Each provider offers tiers — flagship (expensive, powerful) vs. lightweight (cheap, fast)
- Multimodal Models: Models that process text, images, audio, and video (GPT-4o, Gemini, Claude 3.5)
Frameworks
- Model Selection Matrix: Task complexity × latency requirements × budget × data sensitivity
- Benchmarking: MMLU, HumanEval, GSM8K, GPQA — standardized tests to compare models
Comparison
| Model | Strengths | Best For |
|---|---|---|
| GPT-4o | Versatile, strong reasoning | General-purpose, complex tasks |
| Claude 3.5 Sonnet | Long context, safety, coding | Document analysis, careful reasoning |
| Gemini 2.0 Flash | Fast, multimodal, cost-effective | High-volume, speed-critical tasks |
| Llama 3 70B | Open source, self-hostable | Privacy-sensitive, custom fine-tuning |
| Mistral Large | European, strong multilingual | EU compliance, multilingual tasks |
Common Mistakes
- Defaulting to the most expensive model for every task
- Ignoring open-source options for cost-sensitive workloads
- Not benchmarking on YOUR specific use case
- Assuming model rankings are static — they change quarterly