Dishaya / How AI Model Routing Works
How AI Model Routing Works
A plain-English guide to the technique behind cheaper, more reliable AI, and how to tell good routing from marketing.
AI model routing sends each prompt to the language model best suited to answer it , chosen by how hard the request is and what each model costs, instead of sending everything to one fixed model. A router reads the request, picks the cheapest model that still clears the quality bar, and automatically fails over to another model if the first is unavailable. Done well, it keeps answer quality flat while cutting cost by a large margin.
Why routing exists
The same question can cost ten times more depending on which model answers it, and the most expensive model is rarely ten times better at everyday work. Summarising an email, reformatting a list, or answering a factual question does not need a frontier reasoning model. Sending all of that to one premium endpoint is the single biggest source of wasted AI spend. Routing exists to spend a frontier model's price only when the task earns it.
The four steps of a route
1. Classify the request
Before spending a token, the router estimates how hard the prompt is: is this a quick transformation, a reasoning problem, code, or a long research task? This classification happens in milliseconds and decides which tier of model is a candidate.
2. Rank candidates by value
Among the models that can clear the quality bar for that class of work, the router ranks by price and recent reliability. The cheapest capable model wins. A good router also weighs live signals, is a provider currently rate-limiting or slow?, so it does not send your prompt somewhere that is about to time out.
3. Escalate when the answer is weak
If the chosen model returns something thin or low-confidence, a quality-aware router can quietly retry on a stronger model rather than handing you a poor answer. This is the difference between routing for cost and routing for value.
4. Fail over automatically
Providers go down, hit rate limits, and run out of credits. A router keeps an ordered chain of models, so when one call fails the request continues on the next model instead of erroring out. For anything running in production, this failover is often worth more than the cost savings.
Cost-based routing vs. value-based routing
The cheapest possible routing simply always picks the lowest-priced model. That saves money but silently lowers quality on hard prompts. Value-based routing instead asks the harder question, what is the least I can spend and still clear the bar for this specific request?, and escalates when needed. When you evaluate a router, this is the distinction that matters.
| Approach | Optimises for | Risk |
|---|---|---|
| Single premium model | Simplicity | Pays top price for trivial work |
| Always-cheapest routing | Cost only | Quality drops on hard prompts |
| Value-based routing | Cost and quality | Needs an honest quality signal |
How Dishaya does it
Dishaya reads every prompt, routes it to the best-value model, escalates when an answer looks weak, and fails over across providers automatically, and it shows you what each prompt saved versus a single premium model. You can bring your own provider keys with no markup, or use Dishaya credits. In private mode, a model runs entirely on your own machine, so nothing leaves your device. Its research mode goes a step further and checks each claim against its source before writing it, see verified AI research.
What is AI model routing?
Sending each prompt to the model best suited to answer it, by difficulty and cost, instead of one fixed model, with automatic failover when a provider is unavailable.
Does routing reduce answer quality?
Good routing keeps quality flat and lowers cost, because easy requests do not need a frontier model and hard ones are escalated to one.
What happens if a model runs out of credits mid-answer?
A router with failover retries on the next model in its chain, so the answer continues instead of failing.