Multimodal AI Explained: How Models Process Text, Images, Audio, and Video
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Multimodal AI explained — how models like GPT-4o and Gemini process text, images, audio, and video together, with practical examples and real-world applications.
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Multimodal AI Explained: How Models Process Text, Images, Audio, and Video
Multimodal AI describes models that process text, images, audio, and video in one unified system, instead of separate tools stitched together. Feed GPT-4o a photo of a whiteboard full of technical diagrams, and it doesn't just describe the boxes — it can spot a logical inconsistency in the flow arrows that a person staring at the same board for an hour missed.
That's the practical shift: not just seeing an image, but reasoning across it jointly with text. By 2026, the strongest models handle text, images, audio, and video together well enough to unlock genuinely new use cases, not just demos.
Here's how the architecture works and how to use it.
The Architecture: How Models See
From Pixels to Tokens
A large language model (LLM) only understands tokens, so an image first has to become something token-shaped before the model can reason about it.
Image Processing Pipeline:
1. Input image (e.g., 224×224 pixels, RGB)
↓
2. Split into patches (e.g., 16×16 pixel patches)
→ For 224×224 image: 196 patches
↓
3. Linear projection: each patch → embedding vector
(similar to word embedding but for image patches)
↓
4. Add positional embeddings (tells model where each patch is)
↓
5. These "image tokens" are concatenated with text tokens
↓
6. Transformer processes all tokens together (joint attention)
→ Image tokens can attend to text tokens and vice versaThis Vision Transformer (ViT) approach, popularized by Google in 2020, is the foundation of most modern vision-language models.
Using GPT-4 Vision
import base64
from openai import OpenAI
client = OpenAI()
# Method 1: Image from URL
def analyze_image_url(image_url: str, question: str) -> str:
response = client.chat.completions.create(
model="gpt-4o",
messages=[
{
"role": "user",
"content": [
{
"type": "image_url",
"image_url": {
"url": image_url,
"detail": "high" # "low" = faster/cheaper, "high" = better OCR
}
},
{
"type": "text",
"text": question
}
]
}
],
max_tokens=1000
)
return response.choices[0].message.content
# Method 2: Local image as base64
def analyze_local_image(image_path: str, question: str) -> str:
with open(image_path, "rb") as f:
image_data = base64.b64encode(f.read()).decode("utf-8")
# Get image media type
if image_path.endswith(".png"):
media_type = "image/png"
elif image_path.endswith((".jpg", ".jpeg")):
media_type = "image/jpeg"
else:
media_type = "image/webp"
response = client.chat.completions.create(
model="gpt-4o",
messages=[
{
"role": "user",
"content": [
{
"type": "image_url",
"image_url": {
"url": f"data:{media_type};base64,{image_data}"
}
},
{"type": "text", "text": question}
]
}
]
)
return response.choices[0].message.content
# Multi-image analysis
def compare_images(image_paths: list[str], comparison_question: str) -> str:
content = []
for path in image_paths:
with open(path, "rb") as f:
data = base64.b64encode(f.read()).decode("utf-8")
content.append({
"type": "image_url",
"image_url": {"url": f"data:image/jpeg;base64,{data}"}
})
content.append({"type": "text", "text": comparison_question})
response = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": content}]
)
return response.choices[0].message.content
# Example: Document data extraction
invoice_text = analyze_local_image(
"invoice.pdf",
"Extract: invoice number, date, line items with prices, and total. Return as JSON."
)Using Claude Vision
import anthropic
import base64
import httpx
client = anthropic.Anthropic()
def claude_vision(image_path: str, question: str) -> str:
with open(image_path, "rb") as f:
image_data = base64.b64encode(f.read()).decode("utf-8")
response = client.messages.create(
model="claude-3-5-sonnet-20241022",
max_tokens=1024,
messages=[
{
"role": "user",
"content": [
{
"type": "image",
"source": {
"type": "base64",
"media_type": "image/jpeg",
"data": image_data,
},
},
{
"type": "text",
"text": question
}
],
}
],
)
return response.content[0].text
# Claude with image from URL
def claude_vision_url(image_url: str, question: str) -> str:
image_data = base64.b64encode(httpx.get(image_url).content).decode("utf-8")
response = client.messages.create(
model="claude-3-5-sonnet-20241022",
max_tokens=1024,
messages=[{
"role": "user",
"content": [
{
"type": "image",
"source": {"type": "base64", "media_type": "image/jpeg", "data": image_data}
},
{"type": "text", "text": question}
]
}]
)
return response.content[0].textOpen-Source Multimodal with LLaVA
from transformers import AutoProcessor, LlavaForConditionalGeneration
from PIL import Image
import torch
import requests
# LLaVA-1.6 (free, runs locally)
model_id = "llava-hf/llava-v1.6-mistral-7b-hf"
processor = AutoProcessor.from_pretrained(model_id)
model = LlavaForConditionalGeneration.from_pretrained(
model_id,
torch_dtype=torch.float16,
device_map="auto"
)
def llava_analyze(image_path: str, question: str) -> str:
image = Image.open(image_path).convert("RGB")
# LLaVA conversation format
conversation = [
{
"role": "user",
"content": [
{"type": "image"},
{"type": "text", "text": question}
]
}
]
prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
inputs = processor(image, prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=512,
temperature=0.3,
do_sample=True
)
# Decode only the generated tokens (not the prompt)
generated = outputs[0][inputs["input_ids"].shape[-1]:]
return processor.decode(generated, skip_special_tokens=True)
# Or use Ollama (simpler setup)
import ollama
def ollama_vision(image_path: str, question: str) -> str:
# ollama pull llava first
response = ollama.chat(
model="llava",
messages=[{
"role": "user",
"content": question,
"images": [image_path]
}]
)
return response["message"]["content"]Audio and Video with Gemini
Gemini 1.5 Pro uniquely handles audio and video natively:
import google.generativeai as genai
genai.configure(api_key="your-gemini-api-key")
model = genai.GenerativeModel("gemini-1.5-pro")
# Audio analysis
def analyze_audio(audio_path: str, question: str) -> str:
audio_file = genai.upload_file(path=audio_path, mime_type="audio/mp3")
response = model.generate_content([
question,
audio_file
])
return response.text
# Video analysis (up to ~1 hour)
def analyze_video(video_path: str, question: str) -> str:
video_file = genai.upload_file(path=video_path, mime_type="video/mp4")
# Wait for processing
import time
while video_file.state.name == "PROCESSING":
time.sleep(2)
video_file = genai.get_file(video_file.name)
response = model.generate_content([
video_file,
question
])
return response.text
# Example: Video timestamp extraction
timestamps = analyze_video(
"lecture.mp4",
"List all topics covered with their timestamps in format MM:SS - Topic Name"
)
# Example: Multi-modal (image + audio)
scene_description = model.generate_content([
genai.upload_file("photo.jpg"),
genai.upload_file("ambient_sound.mp3"),
"Describe what's happening in this scene, incorporating both the visual and audio context."
])Model Comparison: Multimodal Capabilities
| Model | Images | Audio | Video | OCR | Reasoning | Cost |
|---|---|---|---|---|---|---|
| GPT-4o | ✓ | ✓ | Limited | Excellent | Excellent | $5/$15/M |
| Claude 3.5 Sonnet | ✓ | ✗ | ✗ | Excellent | Excellent | $3/$15/M |
| Gemini 1.5 Pro | ✓ | ✓ | ✓ (1hr) | Good | Good | $3.5/$10.5/M |
| LLaVA-1.6 7B | ✓ | ✗ | ✗ | Good | Good | Free (local) |
| Phi-3-Vision | ✓ | ✗ | ✗ | Good | Good | Free (local) |
Practical Applications
Document Extraction Pipeline
def extract_invoice_data(invoice_path: str) -> dict:
"""Extract structured data from invoice image."""
result = analyze_local_image(
invoice_path,
"""Extract the following from this invoice and return as JSON:
{
"invoice_number": "...",
"date": "YYYY-MM-DD",
"vendor": "...",
"line_items": [{"description": "...", "quantity": N, "unit_price": N, "total": N}],
"subtotal": N,
"tax": N,
"total": N
}
Return ONLY valid JSON, no other text."""
)
import json
return json.loads(result)
# Chart analysis
def analyze_chart(chart_path: str) -> dict:
description = analyze_local_image(
chart_path,
"Analyze this chart: describe the type, axes, key trends, "
"data values (approximate), and main insights. Return as JSON."
)
return descriptionMultimodal AI Is Infrastructure Now, Not a Demo
Multimodal AI has moved from novelty to infrastructure. Document processing that once needed a specialized OCR pipeline now works with a single API call. Video review that needed a human now runs unattended. UI code can be generated straight from a screenshot.
Practical guidance: for image-plus-text tasks, GPT-4o, Claude 3.5 Sonnet, or Gemini all deliver excellent results — pick on cost and existing stack. For video and audio, Gemini 1.5 Pro is the only frontier model with native support. For privacy-sensitive workloads, LLaVA via Ollama runs locally with no data leaving the machine.
For building complete AI applications with multimodal capabilities, see our AI chatbot guide. For the transformer architecture underlying these vision models, see our transformer guide.
Further Reading
- Ollama Tutorial: Run LLMs Locally on Your Computer (Complete Setup Guide)
- Fine-Tuning LLMs: When to Do It and How to Do It Right
- Transformer Architecture Explained: The Architecture Behind All Modern AI
- LLM Context Window Explained: Why It Matters and How to Use It
- Embeddings Explained: How AI Converts Words to Numbers That Mean Something
- How to Fine-Tune ChatGPT for Your Brand Voice
- How to Write ChatGPT System Prompts for Consistent Output
- Hugging Face Transformers Tutorial: Complete Guide to Using Pretrained Models
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Ensures every release is bug-free through rigorous testing, and crafts high-precision prompts that power our AI-driven workflows. Abdullah Al Arman Emon leads QA and prompt engineering across AiTechWorlds.
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