Gemini Compatible

Qevron also supports Google Gemini's native API format. So you can point the google-genai SDK or your Gemini-format requests at Qevron.

POST/v1/models/{model}:generateContent

POST/v1beta/models/{model}:generateContent

Both /v1 and /v1beta prefixes are supported (the Gemini SDK usually uses /v1beta).

Authentication

The Gemini SDK uses the x-goog-api-key header; Qevron accepts it. Authorization: Bearer also works.

Example

curl

curl "https://app.qevron.ai/v1beta/models/verinova:generateContent" \
  -H "x-goog-api-key: $QEVRON_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "contents": [
      { "role": "user", "parts": [ { "text": "Hello, introduce yourself." } ] }
    ]
  }'

Python (google-genai)

from google import genai

client = genai.Client(
    api_key="QEVRON_API_KEY",
    http_options={"base_url": "https://app.qevron.ai"},
)

resp = client.models.generate_content(
    model="verinova",
    contents="Hello, introduce yourself.",
)
print(resp.text)

Response

A response in Gemini format is returned; the generated text is in candidates[].content.parts[].text:

{
  "candidates": [
    {
      "content": {
        "role": "model",
        "parts": [ { "text": "Hello! I'm an assistant running through Qevron." } ]
      },
      "finishReason": "STOP"
    }
  ],
  "usageMetadata": {
    "promptTokenCount": 8,
    "candidatesTokenCount": 14,
    "totalTokenCount": 22
  }
}

TIP

Use systemInstruction for a system prompt and the :streamGenerateContent endpoint for streaming (same as the Gemini API).