The Three Key Dimensions
Choosing an LLM is not just about raw capability. Three practical dimensions drive most decisions:
Context window — how much text the model can process at once. If your use case involves long documents, codebases, or conversation history, you need a large context window (100K+ tokens). For short classification or generation tasks, a smaller window is fine and cheaper.
Pricing — models are billed per input and output token. The cost gap between frontier and mid-tier models is often 10–20x. For high-volume tasks (batch processing, search ranking, classification), that gap matters enormously. For low-volume interactive use, it usually does not.
Knowledge cutoff — the date beyond which the model has no training data. If your application answers questions about recent events, APIs, or library versions, a stale cutoff forces you to add retrieval (RAG) regardless of model quality.
Smaller vs Larger Models
Larger models excel at nuanced reasoning, ambiguous instructions, and creative tasks. Smaller models are faster, cheaper, and often sufficient for structured extraction, classification, and templated generation. A common pattern: use a small model for the first pass and escalate to a larger model only when confidence is low.
Practical Advice
Run your representative prompts through candidate models and compare outputs side by side. Benchmark latency and cost on realistic traffic. The best model for your project is the one that hits your quality bar at the lowest cost per call.