Vision (Image Analysis)

Vision-capable models can analyze images alongside text, enabling use cases like image description, visual reasoning, document extraction, and more. Images are passed as part of the messages array using the OpenAI-compatible multimodal format.

The Morpheus Inference API is fully OpenAI-compatible — vision works exactly like OpenAI’s multimodal API. If you’ve used GPT-4 Vision before, you already know how to use this.

Supported Models

Model	Context Window	Best For
`kimi-k2.6`	256K	Visual reasoning, math from images, complex multimodal tasks
`kimi-k2.5`	256K	Visual reasoning, math from images, complex multimodal tasks
`gemma-4-31b`	256K	Document parsing, math/science from images, high-accuracy visual reasoning
`gemma-4-26b-a4b`	256K	Efficient image analysis, low-latency visual reasoning
`qwen35-35b-a3b`	256K	General-purpose image analysis, long context
`qwen35-9b`	256K	Fast image analysis, lightweight visual tasks
`mistral-31-24b`	128K	Fast image analysis, efficient visual processing

How It Works

Instead of sending a plain text string as the message content, you send an array of content parts — mixing text and images in a single message:

{
  "role": "user",
  "content": [
    {"type": "text", "text": "What do you see in this image?"},
    {"type": "image_url", "image_url": {"url": "https://example.com/image.jpg"}}
  ]
}

Images can be provided as:

URL — A direct link to an image (https://...)
Base64 — Inline image data (data:image/jpeg;base64,...)

Basic Example

Send an image URL for analysis:

curl
Python
JavaScript
TypeScript

curl https://api.mor.org/api/v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "kimi-k2.5",
    "messages": [
      {
        "role": "user",
        "content": [
          {"type": "text", "text": "What do you see in this image? Describe it in detail."},
          {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/3/3a/Cat03.jpg/1200px-Cat03.jpg"}}
        ]
      }
    ]
  }'

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.mor.org/api/v1"
)

response = client.chat.completions.create(
    model="kimi-k2.5",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What do you see in this image? Describe it in detail."},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/3/3a/Cat03.jpg/1200px-Cat03.jpg"
                    }
                }
            ]
        }
    ]
)

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

Install the OpenAI SDK: pip install openai

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: "YOUR_API_KEY",
  baseURL: "https://api.mor.org/api/v1",
});

const response = await client.chat.completions.create({
  model: "kimi-k2.5",
  messages: [
    {
      role: "user",
      content: [
        { type: "text", text: "What do you see in this image? Describe it in detail." },
        {
          type: "image_url",
          image_url: {
            url: "https://upload.wikimedia.org/wikipedia/commons/thumb/3/3a/Cat03.jpg/1200px-Cat03.jpg",
          },
        },
      ],
    },
  ],
});

console.log(response.choices[0].message.content);

Install the OpenAI SDK: npm install openai

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.MORPHEUS_API_KEY!,
  baseURL: "https://api.mor.org/api/v1",
});

const response = await client.chat.completions.create({
  model: "kimi-k2.5",
  messages: [
    {
      role: "user",
      content: [
        { type: "text", text: "What do you see in this image? Describe it in detail." },
        {
          type: "image_url",
          image_url: {
            url: "https://upload.wikimedia.org/wikipedia/commons/thumb/3/3a/Cat03.jpg/1200px-Cat03.jpg",
          },
        },
      ],
    },
  ],
});

console.log(response.choices[0].message.content);

Install the OpenAI SDK: npm install openai

Using Base64 Images

For local images or when you want to avoid external URLs, encode the image as base64:

Python
JavaScript
TypeScript

import base64
from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.mor.org/api/v1"
)

# Read and encode a local image
with open("image.jpg", "rb") as f:
    image_data = base64.b64encode(f.read()).decode("utf-8")

response = client.chat.completions.create(
    model="kimi-k2.5",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Describe this image."},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": f"data:image/jpeg;base64,{image_data}"
                    }
                }
            ]
        }
    ]
)

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

import OpenAI from "openai";
import fs from "fs";

const client = new OpenAI({
  apiKey: "YOUR_API_KEY",
  baseURL: "https://api.mor.org/api/v1",
});

// Read and encode a local image
const imageData = fs.readFileSync("image.jpg").toString("base64");

const response = await client.chat.completions.create({
  model: "kimi-k2.5",
  messages: [
    {
      role: "user",
      content: [
        { type: "text", text: "Describe this image." },
        {
          type: "image_url",
          image_url: {
            url: `data:image/jpeg;base64,${imageData}`,
          },
        },
      ],
    },
  ],
});

console.log(response.choices[0].message.content);

import OpenAI from "openai";
import fs from "fs";

const client = new OpenAI({
  apiKey: process.env.MORPHEUS_API_KEY!,
  baseURL: "https://api.mor.org/api/v1",
});

const imageData: string = fs.readFileSync("image.jpg").toString("base64");

const response = await client.chat.completions.create({
  model: "kimi-k2.5",
  messages: [
    {
      role: "user",
      content: [
        { type: "text", text: "Describe this image." },
        {
          type: "image_url",
          image_url: {
            url: `data:image/jpeg;base64,${imageData}`,
          },
        },
      ],
    },
  ],
});

console.log(response.choices[0].message.content);

Multiple Images

You can send multiple images in a single message for comparison or multi-image analysis:

response = client.chat.completions.create(
    model="kimi-k2.5",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Compare these two images. What are the differences?"},
                {
                    "type": "image_url",
                    "image_url": {"url": "https://example.com/image1.jpg"}
                },
                {
                    "type": "image_url",
                    "image_url": {"url": "https://example.com/image2.jpg"}
                }
            ]
        }
    ]
)

Use Cases

Image Description

Ask the model to describe what it sees in an image — useful for accessibility, content moderation, or cataloging.

response = client.chat.completions.create(
    model="kimi-k2.5",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "Describe this image in one paragraph."},
            {"type": "image_url", "image_url": {"url": "https://example.com/photo.jpg"}}
        ]
    }]
)

Document & Receipt Extraction

Extract structured data from photos of documents, receipts, or invoices.

response = client.chat.completions.create(
    model="kimi-k2.5",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "Extract all line items, totals, and the date from this receipt. Return as JSON."},
            {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,..."}}
        ]
    }]
)

Math & Diagram Reasoning

kimi-k2.5 excels at solving math problems from images and interpreting diagrams.

response = client.chat.completions.create(
    model="kimi-k2.5",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "Solve the math problem shown in this image. Show your work step by step."},
            {"type": "image_url", "image_url": {"url": "data:image/png;base64,..."}}
        ]
    }]
)

Code Screenshot Analysis

Have the model read and explain code from screenshots.

response = client.chat.completions.create(
    model="mistral-31-24b",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "Read the code in this screenshot. Explain what it does and suggest improvements."},
            {"type": "image_url", "image_url": {"url": "data:image/png;base64,..."}}
        ]
    }]
)

Tips

Choosing a model: Use kimi-k2.6, kimi-k2.5, or gemma-4-31b for complex visual reasoning, math, and multi-image analysis. Use gemma-4-26b-a4b or qwen35-9b for fast, efficient image tasks. Use mistral-31-24b when you need quick responses for simpler image tasks.

Supported formats: JPEG, PNG, GIF, and WebP images are supported. For base64, include the appropriate MIME type in the data URI (e.g., data:image/png;base64,...).

Next Steps

Available Models

See all models and their capabilities.

Chat Completions API

Full API reference for chat completions.

Tool Calling Guide

Let models invoke tools and external APIs.

Quickstart

Get started with your first API call.

Getting Started

Models

Guides

SDKs

AI Coding Tools

Agent Frameworks

Workflow Automation

Chat UIs

Vision (Image Analysis)

Vision (Image Analysis)

Supported Models

How It Works

Basic Example

Using Base64 Images

Multiple Images

Use Cases

Tips

Next Steps

Available Models

Chat Completions API

Tool Calling Guide

Quickstart

​Vision (Image Analysis)

​Supported Models

​How It Works

​Basic Example

​Using Base64 Images

​Multiple Images

​Use Cases

​Tips

​Next Steps

Available Models

Chat Completions API

Tool Calling Guide

Quickstart

Vision (Image Analysis)

Supported Models

How It Works

Basic Example

Using Base64 Images

Multiple Images

Use Cases

Tips

Next Steps