Multi-model AI routing sends each task to the model best suited for it, falls back when a provider fails, and balances cost against quality — instead of pinning your whole workflow to one model's strengths and one provider's uptime.
Model routing is the decision layer that picks which AI model runs a given task. Good routing turns "which model should I use?" from a hardcoded guess into a rule the platform applies consistently, task by task.
A single Prompt Tornado run can use a search-grounded model for research, a reasoning model for analysis, and dedicated models for image or audio — routed across seven providers.
General reasoning & generation
Long-form writing & reasoning
Multimodal & long context
Search-grounded research
Fast, cost-efficient tasks
Image generation
Audio & voice
Automatic failover between them
Each request is classified against a structured catalog of 181 task types across 17 categories, each carrying the signals used to detect it and a recommended model. This keeps routing declarative — the mapping lives in one governed place, not scattered across prompt strings.
If the primary model errors, the task retries on an alternative capable model, and the substitution is written into the run trace. Fallbacks recover the run and stay visible — no silent downgrade.
High-stakes reasoning goes to a top-tier model; cheap, high-volume extraction goes to a fast, inexpensive one. You set the tradeoff at the routing layer instead of re-deciding it in every prompt.
In the internal evaluation, routing was checked task by task — did each task reach the correct model for its type.
Source: AI Workflow Evaluation · quality_v1 baseline (2026-05-11).
Multi-provider fallback and cost-aware routing, with every decision on the record.