One app, six AI agents: how RafayGen picks the right brain for each task

"Which AI model should I use?" is usually answered with a leaderboard, and a leaderboard is close to useless for the question people are actually asking. The best model on a benchmark is frequently the wrong model for your task, because it is slower, costs more, and is optimised for a kind of difficulty your work does not have.

This explains what actually differs between models, why serious AI products route between several rather than picking one, and how to choose per task — including in RafayGen, where the choice is exposed as six named agents.

The three things that actually differ

Latency. Time-to-first-token varies by more than an order of magnitude between a small model on specialised inference hardware and a large reasoning model. For a quick factual question, a mediocre answer in 400 milliseconds beats an excellent one in twenty seconds, because you will ask three follow-ups and the total wait is what you experience.

Reasoning depth. Some models generate an answer directly; others produce an internal chain of reasoning first and then answer from it. The second kind is dramatically better at multi-step problems — mathematics, debugging, anything where an early mistake invalidates everything after it — and dramatically worse value for "what is the capital of Sindh", where you pay the thinking cost for nothing.

Cost per token. This is invisible to you as a user and it is why the choice exists at all. Running every request against the most capable available model is straightforwardly unaffordable at any scale, and any product that claims to do so is either subsidising you temporarily or not doing it.

There are secondary differences that matter for specific work: context window, instruction-following precision, multilingual strength, and how well a model handles structured output like JSON or a strict format.

Why multi-provider routing exists

There is a reliability argument that has nothing to do with quality. A product built on one provider is offline whenever that provider is offline, rate-limited whenever you exceed its quota, and repriced whenever it changes its pricing. Those are not hypothetical events; they happen regularly.

Routing across several providers turns an outage into degradation. The assistant gets slower or slightly less capable instead of returning an error, and the user often does not notice. Building that properly is more work than it sounds — you need per-lane health tracking, sensible cooldowns after failures, and a floor lane that is always available even if it is the least capable one.

The failure mode worth knowing about, because it catches people who build these systems: a fallback chain is only as good as its worst assumption. If the ordering puts a slow or unhealthy lane first, every request pays that cost before failing over, and the system is slower than having no fallback at all.

RafayGen's six agents and what each is for

• Fast — the lowest-latency lane. Quick factual questions, short rewrites, checking something mid-task. Use it when waiting is the cost you care about.
• Everyday — the balanced default. General writing, summaries, ordinary work. If you are not sure, this is the right starting point.
• Deep — long-form generation. Essays, reports, extended explanations, where coherence across length matters more than speed.
• Complex — technical and structured work. Code, data transformation, anything with a rigid shape that has to come out exactly right.
• Thinking — step-by-step reasoning. Mathematics, logic, debugging, and any question where you want to see the working rather than only the conclusion.
• Frontier — agentic, multi-step work that uses tools across several turns rather than answering in one pass.

A decision procedure that works

Default to Everyday. Escalate to Thinking when an answer is wrong in a way that suggests the model did not reason — it skipped a step, or contradicted itself between paragraphs. Drop to Fast when answers are fine but you are waiting more than you would like.

Some rules of thumb that hold up. If the task has a verifiable right answer and multiple steps, use a reasoning model; the extra time is cheap relative to being wrong. If the task is writing and the output is long, use a long-form model and expect to edit. If you are iterating rapidly — twenty small exchanges — use the fast lane, because latency compounds across turns in a way it does not in a single request.

And do the experiment once. Ask the same non-trivial question to Fast and to Thinking and read both answers. The difference is larger than most people expect, and having seen it once you will pick deliberately from then on instead of leaving the default selected forever.

What routing does not fix

No amount of model selection makes a vague prompt produce a good answer. Prompt quality dominates model choice for most everyday tasks, and switching agents to fix a bad prompt is a common and expensive mistake.

Every lane shares the same fundamental limits. All of them will state incorrect facts fluently. All of them will invent citations. A reasoning model reasons more carefully from the same possibly-wrong recollection, which makes it better at logic and no better at facts.

And the routing is not intelligence about your task. The system knows which lane you selected and which lanes are healthy; it does not know that your question is harder than it looks. That judgement is still yours, which is exactly why the picker is exposed rather than hidden behind an automatic choice.