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AI companies now ship heavyweight and lightweight versions of the same model family. Here is how to decide which tasks deserve which model — and cut costs without cutting quality.
In September 2026, OpenAI released GPT-6 Sol for complex work such as coding and GPT-6 Luna for high-volume tasks such as summarisation and extraction. Other AI providers follow a similar pattern, offering premium reasoning models alongside faster, cheaper ones.
The message for businesses is clear: there is no single "best" model — only the best model for a particular task. Choosing well can reduce AI costs dramatically while improving speed and reliability. This practice is called model routing.
Model routing means sending each AI request to the model best suited for it, based on:
| Factor | Large reasoning models | Small fast models |
|---|---|---|
| Best for | Complex analysis, coding, planning, ambiguous questions | Classification, extraction, summarisation, short answers |
| Speed | Slower | Faster |
| Cost per request | Higher | Much lower |
| Consistency on simple tasks | High | Often high enough |
| Typical volume | Low to medium | High |
Ask four questions for each use case:
| Use case | Suggested model tier |
|---|---|
| Tagging support tickets | Small |
| Extracting fields from invoices | Small |
| Summarising meeting transcripts | Small or medium |
| Drafting marketing copy | Medium |
| Writing and reviewing code | Large |
| Research reports and strategy | Large |
| Customer chatbot FAQs | Small, escalating to large for complex queries |
You assign models by task type in your application code. Simple, predictable and easy to audit.
Start with a small model. If its confidence is low or validation fails, escalate to a larger model. This keeps most traffic cheap while protecting quality.
A lightweight model reads each request and predicts which model should handle it. Useful for varied, unpredictable user input.
For startups and SMEs, AI costs are often quoted in dollars while revenue is in rupees. Smart routing can make AI features affordable at Indian price points — for example, handling regional-language customer queries with small models and reserving large models for complex cases.
Not for every task. On simple, well-defined tasks, smaller models can perform comparably at a fraction of the cost.
Basic rule-based routing can be implemented with modest engineering effort. Advanced cascade or classifier systems need more development and testing.
Yes. Many businesses use multiple providers, although this adds complexity in monitoring, security and compliance.
Whenever providers release new models or change pricing — in practice, at least every quarter.
The AI industry's move toward model families with different sizes and prices rewards businesses that think like architects, not just users. Match the model to the task, measure results and keep the design flexible — and AI becomes both more powerful and more affordable.
