Vision & Multimodal Guide

Prepare in the dashboard

1. Sign in to dashboard.koboillm.com with your email and password, then select your team at the top.
2. Open API Keys → Create API key. Enter a Key name; the key budget is optional. The defaults are No expiry and All Team Models. Select Create and save the full key, which is shown once.
3. Copy an accessible model name from Model List. Choose a model that supports this guide's endpoint; API Quickstart also provides request examples.
4. After running your application, open Logs in the same team, select a time range, and use View details on a request to check its key, tokens, and recorded spend.

OpenAI-compatible base URL: https://api.koboillm.com/v1. Follow the client-specific URL format below. The dashboard address is for your account; applications use the API key, not your account password.

Show the current key creation form

Actual dashboard interface with fictional demonstration data. Click to enlarge.

Model names and provider prices in examples are reference values. Availability, endpoint support, and final recorded costs depend on your key and request.

In addition to text completions and image generation, KoboiLLM natively supports Vision Models — AI models capable of “seeing” and analyzing input images. Ideal applications include:

• Automated captioning and alt-text generation
• Document processing and simple OCR
• Product photo and screenshot inspection
• Visual Question Answering (Visual QA)

The API strictly adheres to the standard OpenAI Chat Completions specification using multimodal array content, making existing OpenAI and LiteLLM SDKs immediately compatible.

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Table of Contents

1. Endpoint & Authentication
2. Available Vision Models
3. Quick Start — Sending Image URLs
4. Sending Local Images (Base64)

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1. Endpoint & Authentication

Base URL:

https://api.koboillm.com/v1

Append the endpoint path to the API origin, or configure this base URL in your SDK.

Primary Endpoint: POST /v1/chat/completions — identical to standard chat completions, except that the user message content field is structured as an array containing text and image_url objects.

Authentication: Send Authorization: Bearer sk-xxxxxx. Set the key in your environment:

export LITELLM_API_KEY="sk-xxxxxx"

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2. Available Vision Models

The following models natively support image understanding at KoboiLLM:

Provider	Endpoint Name	Best Used For
OpenAI	openai/gpt-4o-mini	Fast & budget-friendly vision — recommended default
OpenAI	openai/gpt-4o	High-reasoning visual analysis and complex layout reading
Google Gemini	gemini/gemini-2.5-flash	High-speed, low-cost multimodal processing
Google Gemini	gemini/gemini-2.5-pro	Comprehensive document analysis and detailed OCR

For the live model catalog, query /v1/models or check your dashboard at dashboard.koboillm.com.

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3. Quick Start — Sending Image URLs

3.1 — cURL

curl -X POST https://api.koboillm.com/v1/chat/completions \
  -H "Authorization: Bearer sk-xxxxxx" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai/gpt-4o-mini",
    "messages": [
      {
        "role": "user",
        "content": [
          { "type": "text", "text": "Describe what you see in this image in detail." },
          {
            "type": "image_url",
            "image_url": {
              "url": "https://docs.koboillm.com/koboi_llm_logo.png"
            }
          }
        ]
      }
    ]
  }'

3.2 — Python (OpenAI SDK)

from openai import OpenAI

client = OpenAI(
    base_url="https://api.koboillm.com/v1",
    api_key="sk-xxxxxx",
)

response = client.chat.completions.create(
    model="openai/gpt-4o-mini",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Describe what you see in this image."},
                {
                    "type": "image_url",
                    "image_url": {"url": "https://docs.koboillm.com/koboi_llm_logo.png"},
                },
            ],
        }
    ],
)

print(response.choices[0].message.content)

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4. Sending Local Images (Base64)

For local images stored on disk, encode the image to base64 and supply it as a data URL:

import base64
from openai import OpenAI

client = OpenAI(
    base_url="https://api.koboillm.com/v1",
    api_key="sk-xxxxxx",
)

def encode_image(image_path):
    with open(image_path, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode('utf-8')

base64_image = encode_image("document.png")

response = client.chat.completions.create(
    model="openai/gpt-4o-mini",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Extract all text from this document image."},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": f"data:image/png;base64,{base64_image}"
                    },
                },
            ],
        }
    ],
)

print(response.choices[0].message.content)