The model leaderboard changes every few weeks. The business requirement does not. A team that chooses a model from a benchmark table will be re-evaluating it before the integration is finished.
A better process starts with the task, the data and the constraints that cannot move.
Start with the task
Write one sentence describing the job: classify support tickets, extract fields from invoices, answer policy questions, draft product descriptions or summarize calls. The task determines whether you need reasoning, speed, multilingual output, vision or tool calling.
A model that wins a coding benchmark may be irrelevant for Hindi customer support. A cheap model may be perfect for classification and wrong for a legal summary.
Language and latency
Test the actual language mix. Many Indian workflows switch between English, Hindi and a regional language inside the same conversation. A model that handles English well can still fail on transliterated Hindi or mixed-script input.
Latency matters more than benchmark scores in interactive products. Measure time to first token and time to complete a realistic response on the network your users actually use. A model that feels slow will be abandoned regardless of accuracy.
Cost per successful task
Token price is only the starting point. Include retries, retrieval calls, tool calls, reasoning tokens and human review time. A model that costs twice as much but needs half the corrections can be cheaper per finished task.
Run the same 50 real tasks through two or three shortlisted models. Record the pass rate, the number of edits and the total cost. That table is more useful than a public leaderboard.
Data residency and vendor risk
Check where the data is processed, whether the provider retains it, and what happens when a request is used for improvement. For personal data, the answer belongs in your DPDP documentation, not only in the vendor’s marketing page.
Consider the exit path. Can you switch models without rewriting the product? A thin internal interface that accepts a model name and returns a structured result makes a later switch much cheaper.
A small evaluation harness
Build a test set before the integration. Include clean examples, ambiguous requests, missing fields, long documents and one adversarial input. Store the expected output and a short reason.
Run every candidate model against the same set. Compare correctness, format compliance, latency and cost. Keep the harness after launch; it becomes the regression test for every model or prompt change.
When to switch
Switch when a measured failure matters: a language gap, a latency ceiling, a cost per task that breaks the business case, or a vendor change in retention or availability. Do not switch because a new model tops a leaderboard for one week.
The best model for an Indian business is the one that passes your test set at a cost and latency your users accept. Everything else is a proxy.
A shortlisting scorecard
Turn the requirements into a scorecard before you test. Give each criterion a weight: correctness, language coverage, latency, cost per task, data handling and integration effort. A weighted score prevents one impressive demo from overriding a weak fit.
Use pass/fail gates for the non-negotiables. If the model cannot process Hindi input or cannot run in the required region, the score does not matter. Gates first, weights second.
Keep the scorecard short. Six criteria are enough for a first decision. A long scorecard becomes a spreadsheet exercise that nobody revisits after the pilot.
Record the model version and the date. A scorecard without that context is not reproducible when the provider updates the model.
FAQ
Should we use the largest model available?
Only if the task needs it. Classification, extraction and routing often work better with a smaller, faster model and a good evaluation set.
How many models should we test?
Two or three is enough for a first decision. More candidates add cost without changing the shortlist unless the task has a very specific language or vision requirement.
Can we change models later?
Yes, if the product calls a model through an internal interface instead of hard-coding one provider. That layer is worth building early.
For the current model landscape, read the AI model comparison. For build scope, see AI app development cost and custom software development.
