Skip to content

Models & routing ​

You call the gateway with a logical model name, not a provider's raw model ID. The gateway resolves that name to a configured provider and upstream model. This keeps your client stable even when an administrator re-points a model behind the scenes.

Logical models ​

A logical model is a stable alias your administrator defines per project — for example:

Logical modelTypical use
coding-defaultDay-to-day coding and reasoning
bulkHigh-volume, lower-cost tasks

Send the logical name in the model field; the gateway routes it to the right provider and real model. If the administrator later swaps the underlying provider or model, your code does not change.

Which models can I use? ​

Your group determines the models you're allowed to call. You can see your allowed models on your Profile and on the Connect a client page. If you request a model your group isn't allowed, the gateway rejects the request.

TIP

Use the logical names your team agreed on (e.g. coding-default) rather than provider-specific IDs — it's the stable contract and lets the platform optimize cost and routing without breaking your client.

Automated model routing (auto) ​

If your administrator has enabled it, you can send the special model auto and let the gateway pick the right model from your prompt — a coding question routes to the coding model, a quick chat to a cheaper one — so you don't have to choose:

bash
curl https://api.<your-domain>/v1/chat/completions \
  -H "Authorization: Bearer $KEY" \
  -d '{"model":"auto","messages":[{"role":"user","content":"summarize this article in two sentences"}]}'

If no route is a confident match, the request falls back to a default model. Everything else — budgets, limits, metering — works exactly as with a named model. Ask your admin which routes are configured for your project (they can show you on Projects → Auto Routing → Test a prompt).

Tool-result compression ​

If your administrator has enabled it for your project, the gateway may compact the content of tool messages before sending your request to the model — minifying verbose JSON and collapsing redundant whitespace. This shrinks the token bill on tool-heavy agent workloads.

It's transparent: you don't change anything in your requests. Only tool_result content is affected — your user, system, and assistant messages and the message structure are never altered. In the aggressive mode an unusually large tool output may be truncated to a head and tail with an honest …[N bytes elided by gateway]… marker in place of the middle. If you ever need the full, untouched tool output, ask your admin to set the project's compression mode to off or lossless.

Next steps ​

Enterprise AI governance, on infrastructure you own.