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Gemini Compat API

The Gemini Compat API is a drop-in replacement for Google’s official Gemini API. It’s fully compatible with Google’s official @google/genai SDK and the Gemini REST interface — just change the base URL and API key, and everything works out of the box.
No code changes needed — if you already use @google/genai or call Gemini’s REST API, simply point your SDK to https://api.mountsea.ai/gemini with your Mountsea API key.

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Why Use Compat API?

Official SDK Support

Works with Google’s official @google/genai (TypeScript) and google-genai (Python) libraries

Same API Shape

Uses the same generateContent / streamGenerateContent endpoints and request/response schema

Unified Billing

Single API key, unified usage tracking and billing through Mountsea

Image Generation

Supports image generation via gemini-*-image models (auto-routed to Nano Banana)

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Configuration

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Base URL

https://api.mountsea.ai/gemini

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Authentication

Use your Mountsea API key (Bearer token). It can be passed as either:
  • HTTP header: Authorization: Bearer your-api-key
  • Or via the official SDK’s apiKey parameter

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Image Model Mapping

When calling Compat API with an image model name, it’s automatically routed to the corresponding Nano Banana model:
Gemini Model ID (input)Routed to (Nano Banana)
gemini-2.5-flash-imageNanoBananaFast
gemini-3.1-flash-image-previewNanoBanana2
gemini-3-pro-image-previewNanoBananaPro
You can use either the Gemini model IDs above or the native Nano Banana model IDs (nano-banana-fast, nano-banana-2, nano-banana-pro) — both work.

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Using the Official @google/genai SDK

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Installation

npm install @google/genai

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TypeScript / JavaScript

import { GoogleGenAI } from '@google/genai';

const ai = new GoogleGenAI({
  apiKey: 'your-mountsea-api-key',
  httpOptions: {
    baseUrl: 'https://api.mountsea.ai/gemini',
  },
});

const response = await ai.models.generateContent({
  model: 'gemini-2.5-flash',
  contents: 'Explain quantum computing in one paragraph',
});

console.log(response.text);

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Python

from google import genai
from google.genai import types

client = genai.Client(
    api_key="your-mountsea-api-key",
    http_options=types.HttpOptions(base_url="https://api.mountsea.ai/gemini"),
)

response = client.models.generate_content(
    model="gemini-2.5-flash",
    contents="Explain quantum computing in one paragraph",
)
print(response.text)

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Using the Gemini REST API

If you don’t want to use the SDK, you can call the endpoint directly. It’s identical in shape to Google’s official REST API.

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Endpoint

POST https://api.mountsea.ai/gemini/v1beta/models/{model}:{action}
Where:
  • {model} — model ID (e.g. gemini-2.5-flash, gemini-3-pro-image-preview)
  • {action} — generateContent (JSON response) or streamGenerateContent (SSE stream)

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Text Generation

curl -X POST "https://api.mountsea.ai/gemini/v1beta/models/gemini-2.5-flash:generateContent" \
  -H "Authorization: Bearer your-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "contents": [
      {
        "role": "user",
        "parts": [{ "text": "Explain quantum computing in one paragraph" }]
      }
    ]
  }'

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Image Generation

curl -X POST "https://api.mountsea.ai/gemini/v1beta/models/gemini-3-pro-image-preview:generateContent" \
  -H "Authorization: Bearer your-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "contents": [
      {
        "role": "user",
        "parts": [{ "text": "A futuristic city skyline at night" }]
      }
    ],
    "generationConfig": {
      "responseModalities": ["image", "text"],
      "imageConfig": {
        "aspectRatio": "16:9",
        "imageSize": "2K"
      }
    }
  }'

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Image Editing

Include an image as inline_data in the parts array: 
curl -X POST "https://api.mountsea.ai/gemini/v1beta/models/gemini-2.5-flash-image:generateContent" \
  -H "Authorization: Bearer your-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "contents": [
      {
        "role": "user",
        "parts": [
          { "text": "Add snow falling throughout the scene" },
          {
            "inline_data": {
              "mime_type": "image/jpeg",
              "data": "<base64-encoded-image-bytes>"
            }
          }
        ]
      }
    ],
    "generationConfig": {
      "responseModalities": ["image"],
      "imageConfig": { "aspectRatio": "1:1" }
    }
  }'

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Streaming

Change generateContent to streamGenerateContent to receive a Server-Sent Events (SSE) stream: 
curl -X POST "https://api.mountsea.ai/gemini/v1beta/models/gemini-2.5-flash:streamGenerateContent" \
  -H "Authorization: Bearer your-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "contents": [
      { "role": "user", "parts": [{ "text": "Write a short poem" }] }
    ]
  }'

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Image Generation Config

When requesting image generation (response_modalities includes image), you can configure the output:
FieldLocationDescriptionExample
aspectRatio / aspect_ratiogenerationConfig.imageConfigAspect ratio"1:1", "16:9", "9:16"
imageSize / image_sizegenerationConfig.imageConfigResolution"1K", "2K", "4K"
Both camelCase (aspectRatio, imageSize) and snake_case (aspect_ratio, image_size) are accepted. These fields can also be placed directly on generationConfig as shortcuts.

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Supported Aspect Ratios (by Model)

  • gemini-2.5-flash-image (→ NanoBananaFast): 1:1, 4:3, 3:2, 2:3, 5:4, 4:5, 3:4, 16:9, 9:16, 21:9
  • gemini-3-pro-image-preview (→ NanoBananaPro): all standard ratios
  • gemini-3.1-flash-image-preview (→ NanoBanana2): all standard ratios + 1:4, 4:1, 1:8, 8:1

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Notes & Limitations

Currently the Compat endpoint returns 1 image per request. The candidateCount field is accepted for compatibility but has no effect — it’s treated as 1.
  • For image generation, always include responseModalities: ["image"] (or ["image", "text"]) in generationConfig.
  • The inline_data.data field should be raw base64 (no data:*;base64, prefix).
  • Both inline_data (snake_case) and inlineData (camelCase) are accepted.

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Available Endpoint

EndpointMethodDescription
/gemini/v1beta/models/{modelAction}POSTGemini-compatible generateContent / streamGenerateContent

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Explore the API Documentation