Model Selection Guide

The Solution: Choose the Right Tool for the Job

Model selection is the engineering decision of matching your requirements — speed, cost, accuracy, context length, and modality — to the right model. There is no single "best" LLM: a model that tops a leaderboard can be wasteful for routing support tickets and still too weak for proving a math theorem. The job is to find the cheapest, fastest model that clears the quality bar your task actually requires. Think of it like choosing a vehicle — a sports car, a truck, and a bicycle are each "best" only for a specific trip.

How to evaluate a model

Start from the task, not the hype. Public benchmarks like MMLU or HumanEval are a coarse filter for shortlisting candidates, but they rarely predict performance on your data — so build a small eval set of 50–100 real examples and score each candidate on it. Then weigh three operational axes: latency (time to first token and full response), cost (price per million input and output tokens, which differ), and the context window you need. Capabilities matter too: only some models do native tool use, vision, or long-context retrieval well. For privacy or compliance, open-weight models you self-host (Llama, Mistral) trade convenience for control; quantization can shrink them to fit your hardware at a small quality cost.

Tradeoffs, pitfalls, and a worked example

The biggest pitfall is over-provisioning: paying flagship prices for tasks a small model handles fine. The opposite trap is under-provisioning a genuinely hard reasoning task and shipping wrong answers. A powerful pattern is model routing — a cheap classifier sends easy requests to a small model and only escalates the hard ones. Worked example: a support bot gets 10,000 tickets/day. Sending all of them to a flagship model at, say, $5 per million output tokens might cost hundreds of dollars daily. Instead, route the ~80% of simple FAQ-style tickets to a small model (Haiku / GPT-4o Mini) at roughly a tenth of the price, and escalate only the ~20% of ambiguous or multi-step cases to the flagship. On a representative eval this typically cuts total spend by 60–70% while keeping answer quality within a couple of percent — because the small model was already good enough for the easy majority.

Key Selection Criteria

• Quality: Benchmark scores (MMLU, HumanEval) matter less than eval on YOUR data — always test with real examples from your domain
• Model Routing: Use a lightweight classifier to route easy tasks (FAQ, extraction) to cheap models and hard tasks (reasoning, coding) to flagship models — saves 60-80% with minimal quality loss
• Cost vs Latency: Flagship models are 10-30x more expensive and 2-5x slower — justify the upgrade with measurable quality difference on your evals
• Context Window: Need 100K+ tokens? Only Claude (200K) and Gemini (1M+) support it natively — others require chunking strategies

Try It Yourself!

Explore different models and their trade-offs for various use cases.