OpenAI: GPT-5.2-Codex

Refactor this Python function to use async/await and add proper error handling for network timeouts. The function currently blocks on requests.get() and doesn't handle connection failures gracefully.

The model would produce a refactored version replacing synchronous requests with aiohttp, wrapping the network call in try-except blocks for asyncio.TimeoutError and aiohttp.ClientError, and adding configurable timeout parameters with sensible defaults. The code would include type hints, maintain the original function's interface through a backwards-compatible wrapper, and add docstring notes about the async context requirement. Variable names would be preserved where semantically appropriate, and the refactor would suggest adding aiohttp to requirements.txt.

This example demonstrates GPT-5.2-Codex's strength in architectural refactoring across paradigms. The 400k token context window allows it to reference imported modules and calling code simultaneously. However, without public benchmarks, we can't verify its accuracy rate on async patterns compared to alternatives like Claude 3.7 Opus.

Review this TypeScript React component for accessibility issues. Focus on keyboard navigation, screen reader compatibility, and ARIA attributes. The component is a custom dropdown menu with nested submenus.

The model would produce a structured audit identifying missing role attributes on the menu container, absent aria-expanded states on submenu triggers, and lack of keyboard event handlers for Arrow keys and Escape. It would flag the missing focus trap when the menu opens and point out that the current div-based structure should use semantic button elements. The response would include corrected JSX snippets for each issue, explain why each change matters for assistive technology users, and reference WCAG 2.2 Level AA criteria where applicable.

This showcases the model's ability to apply domain-specific knowledge (WCAG standards) to code review. The multimodal capability means users could paste screenshots of the rendered component alongside code. The $14/Mtok output cost becomes relevant here — a thorough accessibility audit with examples could generate 2-3k tokens, costing $0.03-0.04 per review.

Generate a SQL migration script that adds a multi-tenant architecture to this existing e-commerce schema. Each tenant should have isolated data, but we need to maintain a single database for cost reasons. Include indexes for tenant_id filtering.

The model would produce a migration adding a tenant_id column to each relevant table (orders, products, users, etc.), creating a tenants table with metadata, and establishing foreign key constraints. It would generate CREATE INDEX statements for composite indexes pairing tenant_id with existing primary keys and frequently-queried columns. The script would include ALTER TABLE statements with NOT NULL constraints after backfilling, add a database trigger or check constraint to prevent cross-tenant data leaks, and provide rollback instructions. Comments would explain the performance implications of the indexing strategy.

This example highlights the model's architectural reasoning across a complex schema transformation. The large context window supports pasting entire schema definitions (20+ tables) for holistic analysis. The trade-off: at $1.75/Mtok input, submitting a 50k-token schema costs $0.09 per query — manageable for occasional architecture work, but expensive for iterative development.

When GPT-5.2-Codex handles large-scale codebase rewrites

A 12-person product team needs to migrate a legacy Python monolith to microservices, touching 80+ files per sprint. GPT-5.2-Codex's 400k token context window means you can load entire modules—models, controllers, tests—in one prompt and ask for consistent refactors across the stack. At $1.75/Mtok input, a 300k-token codebase costs $0.53 to ingest, then $14/Mtok output for the rewritten files. If your team runs 40 refactor sessions per month and each generates 50k tokens of code, you're spending roughly $100/month total. The break-even is clear: if one engineer saves 4 hours of manual find-and-replace work per sprint, the model pays for itself. Use GPT-5.2-Codex when your refactors span more than a dozen files and consistency matters more than speed.

Why GPT-5.2-Codex works for API reference at scale

A 5-person SaaS startup ships 20 new API endpoints per quarter and needs reference docs that match the actual codebase. GPT-5.2-Codex ingests your OpenAPI spec, route handlers, and existing docs (often 150k tokens combined) and writes consistent Markdown with examples, error tables, and rate-limit notes. Output cost is $14/Mtok, so generating 30k tokens of docs per endpoint costs $0.42—cheaper than an hour of junior-dev time. The 400k context window means you can include legacy endpoints for style consistency without splitting prompts. If your docs drift from code within a sprint, or if you're writing for multiple SDKs, this model keeps the source-of-truth in one pass. Use it when doc quality blocks customer onboarding more than raw generation speed does.

When image+code context speeds up front-end debugging

A 4-person agency gets 15 bug reports per week with screenshots of broken layouts—misaligned buttons, overflowing text, z-index collisions. GPT-5.2-Codex accepts the screenshot plus the relevant CSS and component code (typically 20k tokens) and returns a diagnosis with a proposed fix. At $1.75 input + $14 output per Mtok, each triage costs under $0.10 in tokens. The image modality means you skip the step where a dev describes the visual issue in text; the model sees the render and maps it to the code. If your team spends 90 minutes per week on "can't reproduce" back-and-forth, this model cuts that to 20 minutes. Use GPT-5.2-Codex when screenshots are your primary bug artifact and you need code-level fixes, not just descriptions.