Two model endpoints can accept the same request format and still produce different operational behavior.
One model may return reliable structured JSON while another adds explanatory text. Tool names can match while arguments differ. Refusal patterns, token usage, and tail latency may also change.
Before switching models, replay a small set of real application tasks. Validate the response schema, inspect tool arguments, compare refusal cases, and measure latency instead of testing only whether the request returns HTTP 200.
VectorNode is our product. Its public page describes access to multiple AI models through one API key, unified credits, transparent pricing, and usage logs: https://www.vectronode.com
Disclosure: VectorNode is our product and is included as one implementation option.
Keep the normalized response for application code, but preserve the raw provider response for debugging. Compatibility should make integrations easier to change, not make important model differences invisible.
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