With dozens of models on the market, "which one?" became a project — but the answer fits in 4 parameters and 2 minutes of reading.
With dozens of models on the market, choosing "the right one" became a mini-project. The good news: you don't need to test everything — you need to understand 4 parameters.
1. Context window — how many tokens a model accepts at once. Matters for chat history, whole codebases, long documents. Irrelevant for short calls.
2. Temperature — 0 = deterministic (extraction, classification, JSON), 0.7-1.0 = creative. If the answer must be consistent, use 0 and describe the format in the prompt.
3. max_tokens — caps response size and cost. Always set a ceiling.
4. Cost/speed per task — start with the smallest model, evaluate quality, scale up only if needed.
Decision flow:
- Simple tasks (classify, extract, summarize) → small model, temperature 0, tight max_tokens;
- Complex tasks (code, reasoning, long context) → big model, temperature 0-0.3;
- Creativity (copy, brainstorm) → big model, temperature 0.7-1.0.
Most common mistake: using the top model for everything "just to be safe". The cost multiplies, latency rises, and most tasks don't need it.
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