Lesson 6: Images In and Out

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Beyond Text

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Core Concepts

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Images In: Vision

• {"type": "input_text", "text": "..."} for the words
• {"type": "input_image", "image_url": "..."} for the picture

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Images Out: Generation

• Call an image model directly, such as gloo-google-gemini-3-pro-image. You can add optional image_generation settings (quality, size), which are passed to providers that support them.
• Give a GPT-5 or GPT-6 model that supports it the image_generation tool (see Models that can use the tool). OpenAI’s image generation runs as a tool, so the model decides when to draw and can combine the image with text in the same response. Include an input_image in the conversation and the tool edits that image instead of starting fresh.

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What It Costs

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Get Started

pip install openai

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Read an Image

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["GLOO_API_KEY"],
    base_url="https://platform.ai.gloo.com/ai/v2/direct",
)

response = client.responses.create(
    model="gloo-google-gemini-3.1-pro",
    input=[{
        "role": "user",
        "content": [
            {"type": "input_text", "text": "What event is this flyer for, and when is it?"},
            {"type": "input_image", "image_url": "https://example.com/flyer.jpg"},  # replace with your image's URL
        ],
    }],
)

print(response.output_text)

import base64

with open("flyer.png", "rb") as f:
    encoded = base64.b64encode(f.read()).decode()

image_part = {"type": "input_image", "image_url": f"data:image/png;base64,{encoded}"}

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Generate an Image

import base64

response = client.responses.create(
    model="gloo-google-gemini-3-pro-image",
    input=[{
        "role": "user",
        "content": [
            {"type": "input_text", "text": "A watercolor painting of a lighthouse at sunset."}
        ],
    }],
)

for item in response.output:
    if item.type != "image_generation_call":
        continue
    if item.status == "completed" and item.result:
        with open("lighthouse.png", "wb") as f:
            f.write(base64.b64decode(item.result))
        print("Saved lighthouse.png")
    else:
        print("The image wasn't generated. Status:", item.status)

curl -X POST 'https://platform.ai.gloo.com/ai/v2/direct/responses' \
  -H "Authorization: Bearer $GLOO_API_KEY" \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "gloo-google-gemini-3-pro-image",
    "input": [
      { "role": "user", "content": [
        { "type": "input_text", "text": "A watercolor painting of a lighthouse at sunset." }
      ]}
    ]
  }'

{
  "output": [
    {
      "type": "image_generation_call",
      "id": "ig_...",
      "status": "completed",
      "result": "iVBORw0KGgoAAAANSUhEUgAA...<truncated base64 PNG>..."
    }
  ]
}

response = client.responses.create(
    model="gloo-openai-gpt-5-mini",
    input="A watercolor painting of a lighthouse at sunset.",
    tools=[{"type": "image_generation", "model": "gpt-image-2", "quality": "low"}],
)

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Good to Know

• Use the Responses shape for images. Vision and image generation are first-class on the Responses endpoints. Direct Responses and the guarded Responses endpoint take the same request.
• Look through every output item. A response can hold text, reasoning and image items together. Pick out what you need by type.
• gpt-image-2 isn’t a top-level model. On Gloo, as on OpenAI’s own API, it’s set inside the image_generation tool. The request’s model is a GPT-5 or GPT-6 model.
• Save what you’ll need again. The image comes back in the response as base64. Decode it and store the file if your app will show it later.
• Describe what you want to see. Image prompts work best with concrete details: subject, style, setting, colors and mood.

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Key Takeaway

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Learn more

• Direct Responses: Multimodal: image input, image generation and image editing
• Using the Guarded Responses API: a step-by-step vision example
• Supported Models: which models read and generate images

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What’s next