Key takeaways
- Public benchmarks are a starting point; evaluate models on your own tasks and data.
- Different tasks suit different models: use capable models for complex reasoning and smaller, faster ones for simple, high-volume steps.
- Total cost depends on tokens per request, volume, caching and retries, not just the price list.
- Review data handling terms and design an abstraction layer so you can switch models later.
The number of capable language models has grown rapidly, and prices and rankings change every few months. Choosing the “best” model in the abstract is less useful than choosing the right model for each job in your product, and building so you can change your mind.
Start with the tasks
| Task type | What matters most |
|---|---|
| Complex reasoning, planning, analysis | Model capability and reliability |
| Classification, extraction, routing | Consistency, speed and cost |
| Customer-facing conversation | Tone, latency and safety |
| Long documents | Context window size and accurate retrieval |
| Code generation | Accuracy on your languages and frameworks |
Build an evaluation set
- Collect 50 to 200 real examples for each task, including difficult and unusual ones.
- Define what a good output looks like: correct fields, accurate facts, right tone, valid format.
- Score candidate models automatically where possible and by human review where needed.
- Re-run the evaluation whenever you change models, prompts or retrieval.
Cost: look beyond the price list
- Estimate tokens per request, including system prompts, retrieved context and outputs.
- Multiply by realistic volumes, including peaks and retries.
- Use prompt caching where providers offer it for repeated context.
- Route simple steps to smaller models and reserve larger models for hard cases.
- Set budgets and alerts per environment.
Latency
For interactive features, users notice delays quickly. Smaller models, streaming responses, shorter prompts and parallel tool calls help. Measure time to first token and total response time on realistic requests, not single test prompts.
Privacy and data terms
- Check whether the provider uses API data for training, and how long it is retained.
- Confirm regional processing options if data residency matters.
- For regulated data, confirm agreements such as a BAA for health information.
- Consider self-hosted open-weight models for the most sensitive workloads.
Avoiding lock-in
Put a thin layer between your application and model providers: a common interface for prompts, tools and responses, with configuration deciding which model serves each task. Keep prompts and evaluations in version control. Switching then becomes a configuration change backed by evaluation results, not a rewrite.
A practical selection process
- Shortlist two or three models per task based on capability, price and terms.
- Run your evaluation set and compare quality, cost and latency.
- Pick the cheapest model that meets the quality bar, with a fallback.
- Monitor production quality and revisit choices every few months.
Our AI solutions team runs evaluations like this before recommending models for client products.
A worked example
A support platform uses an LLM for three tasks: classifying incoming tickets, drafting replies and summarizing long threads. Evaluation shows that a small, fast model classifies tickets as accurately as larger models at a fraction of the cost, while drafting replies benefits from a more capable model. Summaries work well on a mid-sized model with prompt caching for repeated instructions. Routing each task to the right model cuts costs substantially without reducing quality.
Frequently asked questions
Should we use one model for everything?
Often not. Routing different tasks to different models balances quality, speed and cost.
How often should we re-evaluate models?
Every few months, or when a new model or price change could improve quality or cost.
Are open-weight models good enough for production?
For many focused tasks, yes. Evaluate them on your own data against commercial options.
