Gemini 3.8 Flash is available through RouterBase. This tutorial is from the RouterBase team; the text and code were drafted with AI assistance.
A model page URL and an API identifier are separate values. Before configuring a client, check the identifier your endpoint actually lists. For this model, the current RouterBase catalog uses google/gemini-3.8-flash.
Run a read-only catalog check
The following JavaScript needs a runtime with built-in fetch. It reads public metadata; it does not send a prompt or require an API key.
const response = await fetch("https://routerbase.com/v1/models");
if (!response.ok) throw new Error(`Catalog HTTP ${response.status}`);
const { data } = await response.json();
if (!Array.isArray(data)) throw new Error("Unexpected catalog response");
const model = data.find(item => item.id === "google/gemini-3.8-flash");
if (!model) throw new Error("Gemini 3.8 Flash is absent from the catalog");
console.log(model.id);
This check was run on September 14, 2026 and returned the expected identifier. If the endpoint fails, the response has a different shape, or the identifier is absent, the script stops. It does not silently choose a replacement model.
Configure the application separately
Set your OpenAI-compatible client's base URL to https://routerbase.com/v1. Supply your RouterBase API key through a server-side environment variable, and set the model field to the exact catalog identifier. See the current API documentation for the request format.
Keep configuration errors separate from output errors. A catalog lookup cannot validate a later authenticated request. Likewise, receiving an answer does not prove that the answer meets the application's requirements.
Give the first prompt a testable goal
Try a short function explanation. Include the function and ask for its inputs, outputs, and one edge case. Check every claim against the code. If the function returns a sentinel value or throws an error, the explanation should preserve that behavior.
Save the prompt version, model ID, and result. These records make a later comparison useful: you can change one variable and see which requirement improved. No inference benchmark or latency measurement is claimed here.
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