Large models demonstrate broad capability, but many business tasks are narrower: classify an incoming request, extract fields from a document, translate a short message or retrieve a policy. These tasks benefit from predictable latency, modest memory use and a clear evaluation target.
Specialists are easier to measure
A small model can be evaluated against a fixed task set and replaced when it stops meeting the requirement. This is harder when one large model is expected to handle every step of a workflow. Specialisation also makes prompts, data boundaries and tool permissions simpler.
The system matters more than the parameter count
Useful performance comes from the whole design: clean source data, retrieval where knowledge must be current, tools for exact operations, guardrails for uncertain cases and a runtime suited to the target hardware. A larger model cannot compensate for an unclear process.
Conclusion
The right model is the smallest one that meets a documented quality target. This creates systems that are easier to deploy locally, cheaper to run and more straightforward to improve.