GPT-5.2-Codex: 400K Context Window, Multimodal Coding, and Azure AI Foundry Integration

OpenAI and Microsoft have released GPT-5.2-Codex—the latest evolution of the Codex line specifically optimized for software development. With a 400,000 token context window, support for 50+ programming languages, and multimodal capabilities that process code, natural language, images, and diagrams simultaneously, Codex 5.2 represents a step-change in AI-assisted development. Available through Azure AI Foundry and GitHub Copilot, this guide explores the architecture, capabilities, and integration patterns for enterprise development teams.

What’s New in GPT-5.2-Codex

The 400K Context Advantage

The 400K token context window fundamentally changes how AI assists with code. At approximately 300,000 lines of code, Codex 5.2 can understand entire repositories in a single context:

• Full repository analysis: Understand dependencies, patterns, and architecture across thousands of files
• Long conversation memory: Maintain context across extended development sessions
• Complete PR reviews: Analyze entire pull requests with full codebase context
• Documentation generation: Generate docs that accurately reference the entire codebase
• Migration assistance: Understand legacy systems completely before proposing changes

graph LR
    subgraph Context ["400K Token Context"]
        Repo["Full Repository
~300K lines"]
        Docs["Documentation
~50K tokens"]
        History["Conversation
~30K tokens"]
        Tools["Tool Results
~20K tokens"]
    end
    
    subgraph Codex ["GPT-5.2-Codex"]
        Understand["Repository
Understanding"]
        Generate["Code
Generation"]
        Review["Code
Review"]
        Refactor["Refactoring"]
    end
    
    subgraph Output ["Developer Output"]
        Code["Generated Code"]
        Explanation["Explanations"]
        PRReview["PR Reviews"]
        Tests["Test Cases"]
    end
    
    Repo --> Understand
    Docs --> Understand
    History --> Understand
    Tools --> Understand
    
    Understand --> Generate
    Understand --> Review
    Understand --> Refactor
    
    Generate --> Code
    Review --> PRReview
    Generate --> Tests
    Refactor --> Explanation
    
    style Context fill:#E8F5E9,stroke:#2E7D32
    style Codex fill:#E3F2FD,stroke:#1565C0
    style Output fill:#FFF3E0,stroke:#EF6C00

Multimodal Coding Capabilities

Codex 5.2 can process images alongside code, enabling powerful new workflows:

from openai import AzureOpenAI
import base64

client = AzureOpenAI(
    api_version="2026-01-01",
    azure_endpoint="https://my-foundry.openai.azure.com"
)

# Load architecture diagram
with open("system_architecture.png", "rb") as f:
    diagram_base64 = base64.standard_b64encode(f.read()).decode("utf-8")

# Generate implementation from diagram
response = client.chat.completions.create(
    model="gpt-5.2-codex",
    messages=[
        {
            "role": "system",
            "content": """You are an expert software architect. 
            Analyze the provided architecture diagram and generate 
            implementation code that matches the design."""
        },
        {
            "role": "user",
            "content": [
                {
                    "type": "image_url",
                    "image_url": {
                        "url": f"data:image/png;base64,{diagram_base64}"
                    }
                },
                {
                    "type": "text",
                    "text": """Based on this microservices architecture diagram:
                    1. Generate the API Gateway configuration (Kong)
                    2. Create service discovery setup (Consul)
                    3. Implement the message queue connections (RabbitMQ)
                    4. Set up the database schemas for each service
                    
                    Use Python/FastAPI for services, Docker Compose for local dev."""
                }
            ]
        }
    ],
    max_tokens=16000
)

print(response.choices[0].message.content)
# Output: Complete implementation matching the diagram's architecture

UI Mockup to Code

# Convert Figma mockup screenshot to React component
response = client.chat.completions.create(
    model="gpt-5.2-codex",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "image_url",
                    "image_url": {"url": f"data:image/png;base64,{mockup_base64}"}
                },
                {
                    "type": "text",
                    "text": """Convert this UI mockup to a React component using:
                    - TypeScript
                    - Tailwind CSS for styling
                    - Shadcn/ui components where applicable
                    - Proper accessibility attributes
                    - Responsive design (mobile-first)
                    
                    Include hover states and animations visible in the design."""
                }
            ]
        }
    ]
)

# Codex analyzes:
# - Layout structure and spacing
# - Color palette and typography
# - Interactive elements and states
# - Component hierarchy
# Generates pixel-accurate React implementation

Repository-Scale Operations

from azure.ai.foundry import FoundryClient
from azure.ai.foundry.codex import RepositoryContext

foundry = FoundryClient(credential=DefaultAzureCredential())

# Load entire repository into context
repo_context = RepositoryContext.from_github(
    owner="myorg",
    repo="backend-api",
    branch="main",
    include_patterns=["**/*.py", "**/*.yaml", "**/Dockerfile"],
    exclude_patterns=["**/test_*", "**/__pycache__"]
)

print(f"Loaded {repo_context.file_count} files ({repo_context.token_count:,} tokens)")
# Output: Loaded 847 files (289,432 tokens)

# Ask questions about the entire codebase
response = await foundry.codex.analyze(
    context=repo_context,
    query="""
    Analyze this codebase and identify:
    1. Potential security vulnerabilities (OWASP Top 10)
    2. Performance bottlenecks in database queries
    3. Inconsistent error handling patterns
    4. Missing test coverage for critical paths
    
    Provide specific file locations and remediation suggestions.
    """
)

for finding in response.findings:
    print(f"
[{finding.severity}] {finding.category}")
    print(f"  Location: {finding.file}:{finding.line}")
    print(f"  Issue: {finding.description}")
    print(f"  Fix: {finding.remediation}")
    
# Output:
# [HIGH] SQL Injection Risk
#   Location: src/api/users.py:145
#   Issue: String interpolation in SQL query
#   Fix: Use parameterized query: cursor.execute("SELECT * FROM users WHERE id = %s", (user_id,))
#
# [MEDIUM] N+1 Query Pattern
#   Location: src/services/orders.py:89
#   Issue: Database query inside loop fetches related items individually
#   Fix: Use eager loading: Order.objects.prefetch_related('items').filter(...)

Azure AI Foundry Integration

from azure.ai.foundry import FoundryClient
from azure.ai.foundry.agents import CodexAgent

foundry = FoundryClient(credential=DefaultAzureCredential())

# Create a Codex-powered development agent
agent = CodexAgent(
    model="gpt-5.2-codex",
    
    # Integrated tools
    tools=[
        "github_search",      # Search GitHub for examples
        "npm_registry",       # Check package versions/docs
        "pypi_registry",      # Python package info
        "stackoverflow",      # Search for solutions
        "azure_docs",         # Azure documentation
        "code_execution",     # Run code in sandbox
    ],
    
    # Repository context
    repository={
        "provider": "github",
        "owner": "myorg",
        "repo": "frontend-app"
    },
    
    # Guardrails
    guardrails={
        "no_secrets_in_code": True,
        "require_error_handling": True,
        "enforce_type_hints": True,
        "max_function_length": 50
    }
)

# Multi-step development task
result = await agent.execute("""
    Add OAuth 2.0 authentication to the application:
    1. Research best practices for React + OAuth
    2. Choose appropriate library (compare options)
    3. Implement login/logout flow
    4. Add protected route wrapper
    5. Create user context provider
    6. Write unit tests
    7. Update documentation
""")

print(f"Created {len(result.files_created)} files")
print(f"Modified {len(result.files_modified)} files")
print(f"Tests passing: {result.tests_passed}/{result.tests_total}")

GitHub Copilot Integration

Codex 5.2 powers the latest GitHub Copilot features:

// In VS Code with Copilot powered by Codex 5.2

// Type a comment describing what you need:
// Implement a rate limiter using sliding window algorithm
// - Support multiple time windows (second, minute, hour)
// - Redis backend for distributed rate limiting
// - Return detailed rate limit headers
// - Include bypass for internal services

// Codex 5.2 generates complete implementation:
import Redis from 'ioredis';

interface RateLimitConfig {
  windowMs: number;
  maxRequests: number;
  keyPrefix: string;
}

interface RateLimitResult {
  allowed: boolean;
  remaining: number;
  resetAt: Date;
  retryAfter?: number;
}

export class SlidingWindowRateLimiter {
  private redis: Redis;
  private configs: Map<string, RateLimitConfig>;
  private bypassTokens: Set<string>;

  constructor(redisUrl: string) {
    this.redis = new Redis(redisUrl);
    this.configs = new Map([
      ['second', { windowMs: 1000, maxRequests: 10, keyPrefix: 'rl:s:' }],
      ['minute', { windowMs: 60000, maxRequests: 100, keyPrefix: 'rl:m:' }],
      ['hour', { windowMs: 3600000, maxRequests: 1000, keyPrefix: 'rl:h:' }],
    ]);
    this.bypassTokens = new Set(process.env.RATE_LIMIT_BYPASS_TOKENS?.split(',') || []);
  }

  async checkLimit(
    identifier: string,
    window: 'second' | 'minute' | 'hour' = 'minute',
    bypassToken?: string
  ): Promise<RateLimitResult> {
    // Bypass for internal services
    if (bypassToken && this.bypassTokens.has(bypassToken)) {
      return { allowed: true, remaining: Infinity, resetAt: new Date() };
    }

    const config = this.configs.get(window)!;
    const now = Date.now();
    const windowStart = now - config.windowMs;
    const key = `${config.keyPrefix}${identifier}`;

    // Sliding window implementation using Redis sorted sets
    const pipeline = this.redis.pipeline();
    pipeline.zremrangebyscore(key, 0, windowStart);
    pipeline.zadd(key, now.toString(), `${now}-${Math.random()}`);
    pipeline.zcard(key);
    pipeline.expire(key, Math.ceil(config.windowMs / 1000));

    const results = await pipeline.exec();
    const currentCount = results![2][1] as number;
    const remaining = Math.max(0, config.maxRequests - currentCount);
    const resetAt = new Date(now + config.windowMs);

    if (currentCount > config.maxRequests) {
      return {
        allowed: false,
        remaining: 0,
        resetAt,
        retryAfter: Math.ceil(config.windowMs / 1000),
      };
    }

    return { allowed: true, remaining, resetAt };
  }

  getHeaders(result: RateLimitResult): Record<string, string> {
    return {
      'X-RateLimit-Remaining': result.remaining.toString(),
      'X-RateLimit-Reset': result.resetAt.toISOString(),
      ...(result.retryAfter && { 'Retry-After': result.retryAfter.toString() }),
    };
  }
}

Supported Languages (50+)

Pricing

Key Takeaways

• 400K token context enables understanding of entire codebases (~300K lines) in a single request.
• Multimodal input allows code generation from architecture diagrams, UI mockups, and whiteboard sketches.
• 89.2% HumanEval accuracy represents the highest code generation accuracy yet achieved.
• Azure AI Foundry integration provides enterprise-grade deployment with guardrails and monitoring.
• GitHub Copilot users get workspace-aware suggestions that reference their actual codebase.

Conclusion

GPT-5.2-Codex represents a generational leap in AI-assisted software development. The 400K context window eliminates the “limited context” problem that plagued earlier models, enabling true repository-scale understanding. Combined with multimodal capabilities that bridge the gap between design and implementation, Codex 5.2 moves AI from “smart autocomplete” toward genuine development partnership. For enterprises on Azure, the Foundry integration provides the governance and security controls needed for production adoption. The future of coding is here—and it understands your entire codebase.

References

• OpenAI: Introducing GPT-5.2-Codex
• Azure OpenAI GPT-5.2-Codex Documentation
• GitHub Blog: Copilot Powered by Codex 5.2
• Azure AI Foundry Documentation