Stop reaching for the biggest model by default

The instinct when starting an AI project is to grab the most capable model available and move on. Sometimes that's right. Often it's expensive, slow, and overkill. Choosing a model well is a practical optimisation problem, and a little structure saves a lot of money and latency. Here's a framework you can apply to any task.

1. Characterise the task honestly

Before comparing models, understand what you're actually asking. Three questions:

How hard is the reasoning? Classifying a support ticket into one of five buckets is easy. Drafting a nuanced legal summary is hard. The harder the reasoning, the more capability you need — but plenty of real tasks are genuinely easy and don't justify a frontier model.

How much does a mistake cost? A wrong tag on an internal note is cheap. A wrong answer to a customer about their account is expensive. High-stakes tasks justify more capable models and more guardrails; low-stakes tasks can tolerate cheaper, faster options.

What's the volume and speed requirement? A task you run a million times a day has a very different cost profile from one you run twice. A user waiting on a live response needs low latency; a nightly batch job doesn't care.

2. Understand the trade-off triangle

Model selection is governed by three forces in tension: capability, cost, and latency. As a rule, the most capable models cost more per call and respond more slowly; smaller models are cheaper and faster but less capable. You rarely get to maximise all three.

The skill is matching the point on that triangle to your task. A high-volume, low-stakes, simple task wants cheap and fast. A low-volume, high-stakes, hard task wants maximum capability and can absorb the cost. Most teams overspend by putting easy, high-volume tasks on frontier models out of habit.

3. Consider the deployment dimension

Beyond the big three, a few practical factors often decide it:

• Hosted API vs self-hosted. A managed API is simplest to start with. Running an open model yourself gives control, privacy and potentially lower cost at scale — at the price of operational effort.
• Data and privacy constraints. If your data can't leave your environment, that may rule out certain hosted options and point toward a model you run yourself.
• Context window. If your task involves feeding in long documents, you need a model that can hold enough context.
• Specialised vs general. Some tasks (code, specific languages, domains) are served well by models tuned for them.

4. Test on your own work — not benchmarks

This is the step most people skip, and it's the most important. Public benchmarks and leaderboards tell you how models perform on someone else's tasks. They are a weak proxy for how a model handles yours.

Do this instead:

1. Assemble 20–50 real examples from your actual use case, with known good answers.
2. Run your shortlist of candidate models against all of them, using the same prompt.
3. Score the outputs on what you actually care about — accuracy, format, tone, whatever matters.
4. Compare not just quality but cost and latency per call.

A cheaper, faster model that scores 95% as well as the frontier model on your tasks is usually the right choice — and you'd never learn that from a leaderboard.

5. Consider a tiered approach

You don't have to pick one model for everything. A common, effective pattern is to route by difficulty: handle the easy, common cases with a small fast model, and escalate only the hard or uncertain ones to a more capable model. This captures most of the cost savings of the small model while preserving quality on the cases that need it. You can even have the cheap model flag its own low-confidence cases for escalation.

The short version

Don't ask "what's the best model?" — it's the wrong question. Ask "what's the right model for this specific task, given how hard it is, what mistakes cost, and how often I'll run it?" Then prove your answer on your own examples. That discipline routinely cuts costs by a large margin with no loss of quality the user would ever notice — which is exactly the kind of unglamorous win that separates teams who ship sustainable AI from teams who get a surprise bill.