Choose AI models with production data, not benchmark tables
A practical framework for selecting models by quality, latency, cost, and reliability on the work your users actually send.
The question sounds simple: which model should power this agent?
The honest answer is less satisfying: it depends on the job, the traffic, and the failure you can least afford.
A leaderboard can identify capable models. It cannot tell you which one will perform best inside your prompt, tools, retrieval system, latency budget, and user population.
That decision needs production evidence.
Model quality is a system property
An agent combines more than an LLM. A voice agent may also depend on speech recognition, turn detection, synthesis, and telephony. A RAG agent depends on retrieval and context construction. A tool-using agent depends on schemas, timeouts, and recovery behavior.
Changing one model can move several outcomes at once:
| Dimension | What to measure |
|---|---|
| Quality | Task completion, groundedness, policy adherence |
| Latency | Median and tail latency across complete tasks |
| Cost | Cost per successful outcome, not cost per token alone |
| Reliability | Provider errors, malformed outputs, retries, fallbacks |
| User experience | Rephrasing, abandonment, escalation, human repair |
The cheapest model is expensive if people redo its work. The fastest model is slow if it selects the wrong tool twice.
Begin with a workload, not a provider
Define the job before comparing models. Appointment scheduling may reward fast, predictable tool use. Complex claims review may place more weight on reasoning and evidence. High-volume classification may prioritize cost and stable structured output.
Build a representative set from real production sessions. Include common requests, valuable workflows, edge cases, and failures the team never wants to repeat. Then run candidates through the same environment and rubric.
Do not flatten the results into one universal score. Segment them by intent. A model that wins on research may lose on extraction. Routing can be more useful than declaring one provider the permanent champion.
Compare complete outcomes
Track the whole run from user request to final result. A useful comparison asks:
- Did the agent understand the intent?
- Did it retrieve or call the right resource?
- Did it finish the task?
- Did it remain inside policy?
- How long did the user wait?
- What did a successful task cost?
Use medians for the typical experience and tail percentiles for the painful one. A model can look fast at P50 while producing unacceptable delays for a meaningful share of users.
Treat model selection as a living decision
Models change. Providers update infrastructure. Prices move. Prompts and tools evolve. Your users also discover new ways to use the product.
Keep the comparison reproducible. Version prompts, record model configuration, retain representative traces, and rerun evals before a migration. Release model changes gradually and compare production outcomes by version.
That creates model intelligence grounded in your application rather than a static spreadsheet.
Build the evidence loop with Currai
Currai connects model calls, tool activity, latency, cost, sessions, users, and evaluation results. Teams can compare model and prompt versions using the actual work their agents perform, then open the traces behind every regression.
Instrument through native HTTP or an existing OpenTelemetry pipeline. Use the Currai integration skill, or start with Currai free.