Preparing for GPT-4: What We Know So Far

Tomorrow OpenAI is expected to announce GPT-4. The AI community is buzzing with speculation. Based on what we know from leaks, rumors, and OpenAI’s research trajectory, here’s how to prepare your applications and infrastructure.

What We Expect

Larger Context Windows

GPT-3.5 maxes out at 4K tokens (~3000 words). GPT-4 is rumored to support 32K tokens (~25,000 words). This changes everything for:

• Document analysis: Process entire reports, contracts, codebases
• Long conversations: Maintain context across extended sessions
• Complex reasoning: More room for chain-of-thought prompting

Improved Reasoning

GPT-4 should score higher on standardized tests and reasoning benchmarks. Better at:

• Multi-step problems
• Mathematical reasoning
• Code generation and debugging
• Nuanced analysis

Potential Multimodal Capabilities

There’s speculation about image understanding. If true, this enables:

• Analyzing charts and diagrams
• Processing screenshots
• Understanding visual content

Preparing Your Applications

1. Design for Longer Context

class ContextManager:
    """Manage context for variable token limits."""

    def __init__(self, model_limits: dict):
        self.limits = model_limits

    def prepare_context(
        self,
        messages: list,
        model: str,
        max_response_tokens: int = 1000
    ) -> list:
        """Prepare context within model limits."""
        limit = self.limits.get(model, 4096)
        available = limit - max_response_tokens

        # Estimate tokens (rough: 4 chars per token)
        total_tokens = sum(len(m["content"]) // 4 for m in messages)

        if total_tokens <= available:
            return messages

        # Truncate from the middle, keeping system and recent messages
        system_messages = [m for m in messages if m["role"] == "system"]
        other_messages = [m for m in messages if m["role"] != "system"]

        # Keep recent messages
        truncated = system_messages + other_messages[-10:]
        return truncated

# Prepare for GPT-4 limits
context_manager = ContextManager({
    "gpt-35-turbo": 4096,
    "gpt-4": 8192,
    "gpt-4-32k": 32768,  # Expected
})

2. Build Model-Agnostic Interfaces

from abc import ABC, abstractmethod
from dataclasses import dataclass

@dataclass
class CompletionResult:
    content: str
    model: str
    tokens_used: int
    finish_reason: str

class AIProvider(ABC):
    @abstractmethod
    async def complete(self, messages: list, **kwargs) -> CompletionResult:
        pass

class AzureOpenAIProvider(AIProvider):
    def __init__(self, deployments: dict):
        self.deployments = deployments  # {"gpt35": "deploy-name", "gpt4": "deploy-name"}

    async def complete(
        self,
        messages: list,
        model: str = "gpt35",
        **kwargs
    ) -> CompletionResult:
        deployment = self.deployments.get(model, self.deployments["gpt35"])

        response = await openai.ChatCompletion.acreate(
            engine=deployment,
            messages=messages,
            **kwargs
        )

        return CompletionResult(
            content=response.choices[0].message.content,
            model=model,
            tokens_used=response.usage.total_tokens,
            finish_reason=response.choices[0].finish_reason
        )

# Easy to switch models
provider = AzureOpenAIProvider({
    "gpt35": "gpt-35-turbo",
    "gpt4": "gpt-4",  # Add when available
})

# Application code doesn't change
result = await provider.complete(messages, model="gpt4")

3. Implement Model Selection Logic

class ModelSelector:
    """Select appropriate model based on task requirements."""

    def __init__(self):
        self.model_capabilities = {
            "gpt-35-turbo": {
                "context": 4096,
                "cost_per_1k": 0.002,
                "speed": "fast",
                "reasoning": "moderate"
            },
            "gpt-4": {
                "context": 8192,
                "cost_per_1k": 0.03,
                "speed": "slow",
                "reasoning": "excellent"
            },
            "gpt-4-32k": {
                "context": 32768,
                "cost_per_1k": 0.06,
                "speed": "slow",
                "reasoning": "excellent"
            }
        }

    def select(
        self,
        task_type: str,
        context_length: int,
        budget_priority: bool = True
    ) -> str:
        """Select best model for task."""

        # Context-driven selection
        if context_length > 8000:
            return "gpt-4-32k"

        if context_length > 4000:
            return "gpt-4"

        # Task-driven selection
        complex_tasks = ["code_review", "analysis", "reasoning", "debugging"]
        if task_type in complex_tasks and not budget_priority:
            return "gpt-4"

        simple_tasks = ["classification", "extraction", "summarization"]
        if task_type in simple_tasks:
            return "gpt-35-turbo"

        # Default
        return "gpt-35-turbo" if budget_priority else "gpt-4"

4. Prepare for Multimodal Input

from dataclasses import dataclass
from typing import Union
import base64

@dataclass
class TextContent:
    text: str

@dataclass
class ImageContent:
    url: str = None
    base64_data: str = None

    @classmethod
    def from_file(cls, path: str) -> "ImageContent":
        with open(path, "rb") as f:
            data = base64.b64encode(f.read()).decode()
        return cls(base64_data=data)

Content = Union[TextContent, ImageContent]

class MultimodalMessage:
    """Message that can contain text and images."""

    def __init__(self, role: str):
        self.role = role
        self.content: list[Content] = []

    def add_text(self, text: str) -> "MultimodalMessage":
        self.content.append(TextContent(text))
        return self

    def add_image(self, image: ImageContent) -> "MultimodalMessage":
        self.content.append(image)
        return self

    def to_api_format(self, supports_vision: bool = False) -> dict:
        """Convert to API format."""
        if not supports_vision or all(isinstance(c, TextContent) for c in self.content):
            # Text-only format
            text = " ".join(c.text for c in self.content if isinstance(c, TextContent))
            return {"role": self.role, "content": text}

        # Multimodal format (expected for GPT-4V)
        content = []
        for c in self.content:
            if isinstance(c, TextContent):
                content.append({"type": "text", "text": c.text})
            elif isinstance(c, ImageContent):
                if c.url:
                    content.append({"type": "image_url", "image_url": {"url": c.url}})
                elif c.base64_data:
                    content.append({
                        "type": "image_url",
                        "image_url": {"url": f"data:image/jpeg;base64,{c.base64_data}"}
                    })

        return {"role": self.role, "content": content}

# Usage
message = (MultimodalMessage("user")
    .add_text("What's in this architecture diagram?")
    .add_image(ImageContent.from_file("architecture.png")))

# When GPT-4V is available
api_message = message.to_api_format(supports_vision=True)

5. Set Up Cost Monitoring

class CostTracker:
    """Track costs across model versions."""

    PRICING = {
        "gpt-35-turbo": {"input": 0.002, "output": 0.002},
        "gpt-4": {"input": 0.03, "output": 0.06},
        "gpt-4-32k": {"input": 0.06, "output": 0.12},
    }

    def __init__(self):
        self.usage = {}

    def record(
        self,
        model: str,
        input_tokens: int,
        output_tokens: int,
        project: str = "default"
    ):
        """Record token usage."""
        if project not in self.usage:
            self.usage[project] = {}

        if model not in self.usage[project]:
            self.usage[project][model] = {"input": 0, "output": 0}

        self.usage[project][model]["input"] += input_tokens
        self.usage[project][model]["output"] += output_tokens

    def get_cost(self, project: str = None) -> dict:
        """Calculate costs."""
        costs = {}

        projects = [project] if project else self.usage.keys()

        for p in projects:
            if p not in self.usage:
                continue

            costs[p] = {"total": 0, "by_model": {}}

            for model, tokens in self.usage[p].items():
                pricing = self.PRICING.get(model, {"input": 0.002, "output": 0.002})
                input_cost = (tokens["input"] / 1000) * pricing["input"]
                output_cost = (tokens["output"] / 1000) * pricing["output"]
                model_cost = input_cost + output_cost

                costs[p]["by_model"][model] = {
                    "input_tokens": tokens["input"],
                    "output_tokens": tokens["output"],
                    "input_cost": input_cost,
                    "output_cost": output_cost,
                    "total_cost": model_cost
                }
                costs[p]["total"] += model_cost

        return costs

    def project_monthly_cost(self, project: str, days_elapsed: int) -> float:
        """Project monthly cost based on current usage."""
        costs = self.get_cost(project)
        if project not in costs:
            return 0

        daily_avg = costs[project]["total"] / max(days_elapsed, 1)
        return daily_avg * 30

Infrastructure Preparation

Azure Resource Planning

# Request GPT-4 access (when available)
# Currently requires application through Azure portal

# Plan additional quotas
# GPT-4 will likely have separate TPM limits

# Consider dedicated capacity for high-volume scenarios
az cognitiveservices account create \
    --name my-openai-dedicated \
    --resource-group rg-ai \
    --kind OpenAI \
    --sku S0 \
    --location eastus

# Set up monitoring
az monitor diagnostic-settings create \
    --name openai-diagnostics \
    --resource /subscriptions/{sub}/resourceGroups/rg-ai/providers/Microsoft.CognitiveServices/accounts/my-openai \
    --logs '[{"category": "RequestResponse", "enabled": true}]' \
    --workspace /subscriptions/{sub}/resourceGroups/rg-ai/providers/Microsoft.OperationalInsights/workspaces/my-workspace

What to Do Tomorrow

When GPT-4 drops:

1. Review the announcement for actual capabilities
2. Check Azure availability timeline
3. Test with simple prompts to understand differences
4. Compare quality vs cost for your use cases
5. Update model selection logic based on real performance

The AI landscape is evolving rapidly. Build flexible systems that can adopt new capabilities without major rewrites.

Stay tuned for tomorrow’s deep dive once GPT-4 is officially released.