GPT-4o: Real-Time AI and What It Enables

gpt4o_capabilities:
  text:
    - GPT-4 Turbo level quality
    - Faster response times
    - Lower cost than GPT-4 Turbo

  vision:
    - Native image understanding
    - Faster than GPT-4V
    - Better accuracy

  audio:
    - Native speech input/output
    - Real-time conversation (232ms average latency)
    - Emotion and tone understanding
    - Multiple voices

  combined:
    - Cross-modal reasoning
    - Seamless switching between modalities
    - Single model, not pipeline

before_gpt4o:
  voice_assistant:
    pipeline: Speech-to-text → LLM → Text-to-speech
    latency: 2-3 seconds typical
    loss: Tone, emotion, nuance lost in transcription

  vision:
    pipeline: Separate vision API calls
    latency: Multiple round trips
    integration: Manual combination with text

gpt4o:
  voice_assistant:
    pipeline: Single model, native audio
    latency: 232ms average
    gain: Preserves tone, emotion, context

  vision:
    pipeline: Native multimodal
    latency: Single inference
    integration: Natural combination with text/audio

import openai

# Text (same as before, faster and cheaper)
response = openai.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "user", "content": "Explain quantum computing simply."}
    ]
)

# Vision (improved)
response = openai.chat.completions.create(
    model="gpt-4o",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What's in this image?"},
                {
                    "type": "image_url",
                    "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}
                }
            ]
        }
    ]
)

# Conceptual - actual API may differ
# Real-time audio requires WebSocket connection

import asyncio
import websockets

async def voice_conversation():
    async with websockets.connect("wss://api.openai.com/v1/realtime") as ws:
        # Send audio stream
        async def send_audio():
            while True:
                audio_chunk = await get_microphone_chunk()
                await ws.send(audio_chunk)

        # Receive responses
        async def receive_audio():
            while True:
                response = await ws.recv()
                if response.type == "audio":
                    play_audio(response.data)
                elif response.type == "text":
                    print(response.data)

        await asyncio.gather(send_audio(), receive_audio())

voice_applications:
  customer_service:
    before: IVR → transcription → processing → TTS
    after: Natural conversation with context
    benefit: Better experience, lower latency

  accessibility:
    before: Separate tools, disjointed experience
    after: Seamless voice interaction with any content
    benefit: True accessibility

  hands_free:
    before: Laggy, frustrating
    after: Natural conversation speed
    benefit: Actually usable in context

multimodal_workflows:
  document_analysis:
    - Show document (image)
    - Ask questions verbally
    - Get explanations with visual references
    - Follow-up conversation

  tutoring:
    - Student shows work (image)
    - Explains verbally
    - AI provides feedback in natural speech
    - Points to specific areas

  technical_support:
    - User shows error (screenshot)
    - Describes problem verbally
    - AI diagnoses and explains solution
    - Guides through fix conversationally

# Before: Complex pipeline
class VoiceAssistant:
    def __init__(self):
        self.stt = SpeechToText()
        self.llm = LLM()
        self.tts = TextToSpeech()

    async def process(self, audio_input):
        # Multiple models, multiple latencies
        text = await self.stt.transcribe(audio_input)  # 500ms
        response = await self.llm.generate(text)        # 1000ms
        audio = await self.tts.synthesize(response)     # 500ms
        return audio  # Total: ~2000ms

# After: Single model
class VoiceAssistant:
    def __init__(self):
        self.model = GPT4o()

    async def process(self, audio_input):
        # Single model, native audio
        return await self.model.generate(audio_input)  # ~250ms

cost_comparison:
  gpt4o_pricing:
    input: $5/1M tokens (text)
    output: $15/1M tokens (text)
    comparison: 50% cheaper than GPT-4 Turbo

  pipeline_vs_native:
    pipeline_cost:
      - Whisper: $0.006/minute
      - GPT-4: $0.03/1K tokens
      - TTS: $0.015/1K characters

    native_cost:
      - GPT-4o: Single model pricing
      - Likely cheaper overall