Choosing the Right Model

The model you pick shapes every trade-off that follows: accuracy, latency, cost, and privacy. There is no universally best choice — the right model is the cheapest one that meets your quality bar for the task.

The capability-cost spectrum

Frontier models (e.g., GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro) deliver the highest reasoning and instruction-following, but cost more per token and have higher median latency. Mid-tier models offer a strong balance for structured tasks. Small, fast models are effective for classification, routing, and short-form extraction where speed matters more than nuance.

• Frontier: complex reasoning, long context synthesis, ambiguous instructions
• Mid-tier: document Q&A, summarization, well-specified extraction
• Small/fast: intent classification, moderation, single-label tagging, high-volume pipelines

Latency and throughput considerations

For real-time chat, aim for first-token latency under 300ms — which typically means streaming with a capable mid-tier model, not a frontier model generating a long response before sending anything. For batch jobs, latency matters less than throughput and cost per million tokens.

A practical selection process

1. Define the minimum quality bar with an eval (human or automated)
2. Start with a mid-tier model and measure against the bar
3. Step up to frontier only if quality falls short
4. Step down to a small model once quality is established — you may be surprised
5. Re-evaluate quarterly as model capabilities shift

Privacy and data residency

If your data is sensitive, check each provider's data handling terms. Some enterprises require on-premises deployment or a private cloud region, which may narrow the model menu to open-weight options like Llama or Mistral served on your own infrastructure.