LLM Showdown

Model Selection Strategy

Scaling laws show that larger models (more parameters + more data) perform better, but diminishing returns apply. Use benchmarks and leaderboards to compare.

Model Size	Use Case	Cost
1-3B	Edge devices, classification	Free (local)
7-13B	Chat, summarization, RAG	Low
70B+	Complex reasoning, code gen	Medium-High
Frontier (GPT-4o, Claude)	Enterprise, multimodal	High

Commercial Use Cases

Automation — Replace repetitive human tasks
Augmentation — Assist humans with analysis, writing, coding
Agentic AI — Autonomous decision-making systems

Code Generation Showdown

# Test: Convert Python to C++
prompt = "Convert this Python function to C++: def add(a, b): return a + b"

# Compare outputs from GPT-4o, Claude, DeepSeek Coder, Qwen
models = {
    "gpt-4o": "OpenAI",
    "claude-3-5-sonnet": "Anthropic",
    "deepseek-coder": "DeepSeek",
    "qwen2.5-coder": "Qwen (local via Ollama)"
}

Evaluation Metrics vs Outcomes

Traditional metrics (BLEU, ROUGE) don't capture real-world quality. Use LLM-as-Judge, human evaluation, and task-specific tests.

✏️ Exercise: Create a model comparison test: take 5 Python functions and ask 3 different models (GPT-4o, Claude, and a local model via Ollama) to translate them to Rust. Compare the outputs for correctness, efficiency, and readability. Build a Gradio UI to display results side by side.