GPT-4o Released: OpenAI's Fastest and Most Capable Multimodal Model

Today OpenAI announced GPT-4o (the “o” stands for “omni”) - their new flagship model that can reason across audio, vision, and text in real time. This is one of the most impressive AI demonstrations I’ve ever seen.

What is GPT-4o?

GPT-4o is a natively multimodal model trained end-to-end across text, vision, and audio. Unlike previous models that used separate systems for different modalities, GPT-4o processes all inputs and generates all outputs with a single neural network.

Key Specifications

Feature	GPT-4o	GPT-4 Turbo
Context Window	128K tokens	128K tokens
Knowledge Cutoff	Oct 2023	Dec 2023
Audio Response Time	232ms avg	N/A
Text Performance	GPT-4 Turbo level	Baseline
Vision Performance	Better	Good
Cost	50% cheaper	Baseline
Rate Limits	5x higher	Baseline

The Live Demo Was Mind-Blowing

OpenAI demonstrated GPT-4o’s capabilities in real-time interactions:

1. Real-time conversation - Response latency as low as 232ms (human conversation speed)
2. Emotion detection - Recognized emotions from facial expressions and voice tone
3. Singing - Generated melodic responses on request
4. Code interpretation - Analyzed code from a phone camera in real-time
5. Multi-language translation - Real-time voice translation between languages

Getting Started with GPT-4o

Text Generation

from openai import OpenAI

client = OpenAI()

# GPT-4o for text - same interface, better performance
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {
            "role": "system",
            "content": "You are a helpful assistant."
        },
        {
            "role": "user",
            "content": "Explain quantum computing in simple terms."
        }
    ]
)

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

Vision Capabilities

import base64
from openai import OpenAI

client = OpenAI()

def analyze_image(image_path: str, question: str) -> str:
    """Analyze an image with GPT-4o"""

    with open(image_path, "rb") as image_file:
        base64_image = base64.standard_b64encode(image_file.read()).decode("utf-8")

    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[
            {
                "role": "user",
                "content": [
                    {
                        "type": "text",
                        "text": question
                    },
                    {
                        "type": "image_url",
                        "image_url": {
                            "url": f"data:image/jpeg;base64,{base64_image}"
                        }
                    }
                ]
            }
        ],
        max_tokens=1000
    )

    return response.choices[0].message.content

# Example usage
result = analyze_image(
    "architecture_diagram.png",
    "Analyze this system architecture. What are potential scalability issues?"
)
print(result)

Multiple Images

def compare_images(images: list[str], comparison_prompt: str) -> str:
    """Compare multiple images with GPT-4o"""

    content = [{"type": "text", "text": comparison_prompt}]

    for image_path in images:
        with open(image_path, "rb") as f:
            base64_image = base64.standard_b64encode(f.read()).decode("utf-8")
            content.append({
                "type": "image_url",
                "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}
            })

    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": content}],
        max_tokens=2000
    )

    return response.choices[0].message.content

# Compare UI mockups
result = compare_images(
    ["mockup_v1.png", "mockup_v2.png"],
    "Compare these two UI designs. Which is better for user experience and why?"
)

Video Analysis (Frame-by-Frame)

import cv2
import base64
from typing import Generator

def extract_frames(video_path: str, fps: int = 1) -> Generator[str, None, None]:
    """Extract frames from video at specified FPS"""
    video = cv2.VideoCapture(video_path)
    original_fps = video.get(cv2.CAP_PROP_FPS)
    frame_interval = int(original_fps / fps)

    frame_count = 0
    while video.isOpened():
        success, frame = video.read()
        if not success:
            break

        if frame_count % frame_interval == 0:
            _, buffer = cv2.imencode(".jpg", frame)
            yield base64.b64encode(buffer).decode("utf-8")

        frame_count += 1

    video.release()

def analyze_video(video_path: str, question: str, max_frames: int = 10) -> str:
    """Analyze video content with GPT-4o"""

    frames = list(extract_frames(video_path, fps=1))[:max_frames]

    content = [{"type": "text", "text": f"Analyze these video frames: {question}"}]

    for frame in frames:
        content.append({
            "type": "image_url",
            "image_url": {"url": f"data:image/jpeg;base64,{frame}"}
        })

    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": content}],
        max_tokens=2000
    )

    return response.choices[0].message.content

Audio Capabilities (Coming Soon)

The audio API isn’t publicly available yet, but here’s what we saw:

# COMING SOON - Audio API
# This is based on the demo, not actual available API

# Real-time conversation with voice
response = client.audio.speech.create(
    model="gpt-4o-realtime",  # Hypothetical
    voice="alloy",
    input="Hello! How can I help you today?"
)

# Voice-to-voice conversation
# Latency: 232ms average (human conversation speed)
# Supports: Emotion, tone, singing, multiple languages

Pricing Comparison

GPT-4o is significantly cheaper than GPT-4 Turbo:

Model	Input (per 1M tokens)	Output (per 1M tokens)
GPT-4o	$5.00	$15.00
GPT-4 Turbo	$10.00	$30.00
GPT-4	$30.00	$60.00

50% cost reduction with better performance!

Practical Applications

Document Understanding

def process_document(document_images: list[str]) -> dict:
    """Extract structured data from document images"""

    content = [{
        "type": "text",
        "text": """Analyze these document pages and extract:
1. Document type
2. Key dates
3. Parties involved
4. Key terms and amounts
5. Action items or deadlines

Return as structured JSON."""
    }]

    for img in document_images:
        with open(img, "rb") as f:
            content.append({
                "type": "image_url",
                "image_url": {
                    "url": f"data:image/jpeg;base64,{base64.standard_b64encode(f.read()).decode()}"
                }
            })

    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": content}],
        response_format={"type": "json_object"},
        max_tokens=2000
    )

    import json
    return json.loads(response.choices[0].message.content)

Real-Time Code Review

def review_code_screenshot(screenshot_path: str) -> str:
    """Review code from a screenshot - great for mobile development"""

    with open(screenshot_path, "rb") as f:
        image_data = base64.standard_b64encode(f.read()).decode()

    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": """Review this code screenshot:
1. Identify any bugs or issues
2. Suggest improvements
3. Note any security concerns
4. Rate code quality (1-10)"""
                },
                {
                    "type": "image_url",
                    "image_url": {"url": f"data:image/png;base64,{image_data}"}
                }
            ]
        }],
        max_tokens=1500
    )

    return response.choices[0].message.content

Accessibility Helper

def describe_scene(image_path: str) -> str:
    """Generate detailed scene descriptions for accessibility"""

    with open(image_path, "rb") as f:
        image_data = base64.standard_b64encode(f.read()).decode()

    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": """Describe this image in detail for someone who cannot see it.
Include:
- Main subjects and their positions
- Colors and lighting
- Emotions or atmosphere
- Any text visible
- Important details that provide context"""
                },
                {
                    "type": "image_url",
                    "image_url": {"url": f"data:image/jpeg;base64,{image_data}"}
                }
            ]
        }],
        max_tokens=1000
    )

    return response.choices[0].message.content

Migration from GPT-4

Simple Drop-In Replacement

# Before: GPT-4 Turbo
response = client.chat.completions.create(
    model="gpt-4-turbo",
    messages=[...]
)

# After: GPT-4o (just change the model name)
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[...]
)

# Same API, better performance, lower cost

Handling Vision

# GPT-4o has improved vision - same API as GPT-4 Vision
response = client.chat.completions.create(
    model="gpt-4o",  # Was: gpt-4-vision-preview
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "What's in this image?"},
            {"type": "image_url", "image_url": {"url": "..."}}
        ]
    }],
    max_tokens=500
)

Free Tier for ChatGPT Users

OpenAI announced that GPT-4o will be available to free ChatGPT users with usage limits:

• Access to GPT-4o intelligence (was GPT-3.5 only)
• Vision capabilities
• Data analysis
• Memory feature
• Browse the web
• Use GPTs

This democratizes access to frontier AI capabilities.

What Makes GPT-4o Special

Native Multimodality

Previous approach:

Text -> GPT-4
Audio -> Whisper -> Text -> GPT-4 -> Text -> TTS -> Audio
Image -> Vision Encoder -> Text -> GPT-4

GPT-4o approach:

Text/Audio/Image -> GPT-4o -> Text/Audio/Image

Single model, end-to-end training across all modalities.

Speed Improvements

Metric	GPT-4	GPT-4o
Text response start	~2-3 sec	~500ms
Audio response	N/A	232ms avg
Image understanding	~3-4 sec	~1-2 sec

Conclusion

GPT-4o represents a paradigm shift:

1. Speed - Real-time multimodal interaction is now possible
2. Cost - 50% cheaper than GPT-4 Turbo
3. Capability - Better vision, native audio (coming soon)
4. Accessibility - Available to free ChatGPT users

The “omni” in GPT-4o isn’t marketing - it’s a genuinely unified model that processes all modalities natively. This is what the future of AI interfaces looks like.

I’ll be building with GPT-4o extensively over the coming weeks. The combination of speed, capability, and cost makes it immediately applicable to production use cases that weren’t feasible before.

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References:

• GPT-4o Announcement
• OpenAI API Documentation
• GPT-4o System Card
• ChatGPT Updates