gemini

Call the Gemini API (the Gemini 2.5 and 3 series) through RunAPI using the official OpenAI SDK or Gemini contents clients. Use when the user asks for Gemini chat, streaming completions, multimodal vision input, Google Search grounding, structured output, reasoning effort, or to point an existing OpenAI or Gemini client at RunAPI as the base URL.

Gemini on RunAPI

Gemini on RunAPI exposes two request styles:

Request style	Endpoint	Use when
OpenAI-compatible	POST /v1/chat/completions	You already use the OpenAI SDK or any OpenAI client
Gemini contents	POST /v1beta/models/<model>:generateContent or :streamGenerateContent	You use a Gemini SDK/client with contents requests

Both accept the same RunAPI API Key.

Setup

dotenv

RUNAPI_TOKEN=YOUR_RUNAPI_TOKEN

Get a RunAPI API Key at https://runapi.ai/api_keys.

OpenAI-compatible setup

python

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_RUNAPI_TOKEN",
    base_url="https://runapi.ai/v1",
)

typescript

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: "YOUR_RUNAPI_TOKEN",
  baseURL: "https://runapi.ai/v1",
});

Gemini contents setup

bash

export GOOGLE_API_KEY=YOUR_RUNAPI_TOKEN
export GOOGLE_GENAI_BASE_URL=https://runapi.ai

Core recipe — OpenAI-compatible

python

response = client.chat.completions.create(
    model="gemini-2.5-flash",
    messages=[{"role": "user", "content": "Explain quantum computing simply."}],
    reasoning_effort="high",
)
print(response.choices[0].message.content)
print(response.usage)

typescript

const response = await client.chat.completions.create({
  model: "gemini-2.5-flash",
  messages: [{ role: "user", content: "Explain quantum computing simply." }],
});

bash

curl -X POST "https://runapi.ai/v1/chat/completions" \
  -H "x-api-key: YOUR_RUNAPI_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gemini-2.5-flash",
    "messages": [{"role": "user", "content": "Explain quantum computing simply."}]
  }'

Core recipe — Gemini contents

bash

curl -X POST \
  "https://runapi.ai/v1beta/models/gemini-3-flash-preview:streamGenerateContent" \
  -H "x-goog-api-key: YOUR_RUNAPI_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "contents": [
      { "role": "user", "parts": [{ "text": "Hello!" }] }
    ]
  }'

For gemini-3-flash-preview and gemini-3.5-flash, RunAPI uses the native Gemini streamGenerateContent route. For other callable Gemini models, RunAPI accepts Gemini contents requests and bridges them to the OpenAI-compatible chat request format. Use the official Gemini SDKs when an existing application already sends contents requests; for new app code, prefer the OpenAI-compatible setup.

Streaming (OpenAI-compatible)

python

stream = client.chat.completions.create(
    model="gemini-2.5-flash",
    messages=[{"role": "user", "content": "Write a haiku about coding."}],
    stream=True,
)
for chunk in stream:
    delta = chunk.choices[0].delta.content
    if delta:
        print(delta, end="", flush=True)

Streaming runs through a regional edge proxy so the request does not hold a Rails/Puma thread. Long generations should always stream.

Vision / multimodal

json

{
  "model": "gemini-2.5-flash",
  "messages": [
    {
      "role": "user",
      "content": [
        { "type": "text", "text": "What is in this image?" },
        { "type": "image_url", "image_url": { "url": "https://runapi.ai/img.jpg" } }
      ]
    }
  ]
}

Standard OpenAI multimodal block for the OpenAI-compatible endpoint. For the contents streaming endpoint, embed image data as parts[].inlineData or parts[].fileData.

Google Search grounding

json

{
  "model": "gemini-2.5-pro",
  "messages": [
    { "role": "user", "content": "Latest news on Gemini 3." }
  ],
  "tools": [
    { "type": "function", "function": { "name": "googleSearch" } }
  ]
}

Available on gemini-2.5-flash, gemini-2.5-pro, gemini-3.1-pro-preview, and gemini-3-pro-preview.

Structured output

json

{
  "model": "gemini-2.5-flash",
  "messages": [{ "role": "user", "content": "Give me one person object." }],
  "response_format": {
    "type": "json_schema",
    "json_schema": {
      "name": "person",
      "schema": {
        "type": "object",
        "properties": { "name": { "type": "string" }, "age": { "type": "integer" } },
        "required": ["name", "age"]
      }
    }
  }
}

Reasoning effort

Supported on gemini-2.5-pro, gemini-3.1-pro-preview, gemini-3-pro-preview, and gemini-3-flash-preview — pass reasoning_effort: "low" | "medium" | "high".

List models

bash

curl https://runapi.ai/v1beta/models -H "x-api-key: YOUR_RUNAPI_TOKEN"

Or via the OpenAI-style path:

bash

curl https://runapi.ai/v1/models \
  -H "Authorization: Bearer YOUR_RUNAPI_TOKEN"

Supported models

Model ID	Capabilities
gemini-3.5-flash	Streaming contents requests, multimodal, function calling, thoughts
gemini-3.1-pro-preview	Chat, multimodal, structured output, reasoning effort
gemini-3-pro-preview	Chat, multimodal, structured output, reasoning effort
gemini-3-flash-preview	Chat, multimodal, function calling, structured output, reasoning effort
gemini-2.5-pro	Chat, multimodal, Google Search, structured output, reasoning effort
gemini-2.5-flash	Chat, multimodal, Google Search, structured output, thoughts

gemini-flash-latest resolves to gemini-3-flash-preview.

Connect Gemini CLI itself

bash

export GOOGLE_API_KEY=YOUR_RUNAPI_TOKEN
export GOOGLE_GENAI_BASE_URL=https://runapi.ai
gemini

Agent rules

• Use the OpenAI-compatible endpoint for new app code. Use Gemini contents paths when an existing client already sends contents requests.
• Native Gemini streaming is available for gemini-3-flash-preview and gemini-3.5-flash; other callable Gemini models accept contents requests through a RunAPI protocol bridge.
• Use streaming for any response longer than a few hundred tokens. Do not hold the agent on a long blocking request.
• Google Search grounding uses a googleSearch function tool.
• Pricing, rate limits, quotas — link to https://runapi.ai/models/gemini.md, not this skill file.

Routing

• Model page: https://runapi.ai/models/gemini.md
• Provider page: https://runapi.ai/providers/google.md
• Catalog: https://runapi.ai/models.md