OpenAI: GPT-5.5 Pro (batch)

Review this 40-page technical spec for our payment gateway migration. Flag any security gaps, suggest architectural improvements, and estimate integration effort for our existing Rails monolith.

This example would produce a structured analysis spanning security review (identifying PCI-DSS gaps in the proposed token storage, recommending additional encryption layers), architectural commentary (suggesting event-sourcing for audit trails, noting the spec's coupling to a specific vendor), and integration estimates (breaking down the Rails work into database migrations, controller refactors, and background job changes—roughly 3-4 sprint cycles given the monolith's age). The response would reference specific page numbers and quote problematic sections directly.

The 1M+ token context window handles enterprise documents that would require chunking in smaller models. GPT-5.5 Pro maintains coherence across the full spec, cross-referencing early assumptions with later implementation details. Trade-off: batch processing means no real-time interaction—you submit and wait for the complete analysis.

Generate a React component that renders a filterable data table with column sorting, pagination, and CSV export. Use TypeScript, follow our existing design system tokens, and include comprehensive JSDoc comments.

This example would yield a production-ready TypeScript component with proper type definitions for props and state, integration hooks for the design system's spacing and color tokens, accessible ARIA labels on interactive elements, and a clean separation between presentation and data logic. The CSV export would handle edge cases like escaped commas and quoted fields. JSDoc blocks would explain each prop's purpose and provide usage examples. The code would follow modern React patterns—hooks over classes, composition over inheritance.

GPT-5.5 Pro's training includes recent framework idioms and design system conventions, producing code that fits existing codebases without extensive refactoring. The $90/Mtok output cost matters here—a 500-line component with documentation runs ~$0.045. Batch mode works well for code generation where you review before merging, less so for iterative debugging.

I'm preparing a board presentation on our Q3 performance. Attached are our financial statements, customer churn data, and competitor analysis. Write an executive summary that connects revenue trends to product decisions and recommends two strategic pivots.

This example would produce a concise executive summary opening with the headline numbers (revenue, churn rate, market position), then drawing causal links between product launch timing and revenue inflection points visible in the statements. It would identify patterns in the churn data—perhaps enterprise customers leaving after price increases—and contrast that with competitors' retention tactics from the analysis. The two recommended pivots would be specific and justified: one addressing the churn driver, one capitalizing on a competitor weakness. Tone would be boardroom-appropriate—direct, evidence-based, no jargon.

Multi-document synthesis across different data formats (spreadsheets, reports, market research) is where the large context window justifies the premium pricing. GPT-5.5 Pro connects dots across documents that smaller models would process separately. The batch constraint means you can't ask follow-up questions in the same session—plan your prompt to anticipate what the board will ask.

When 1M+ token context justifies the $90/Mtok output cost

A 12-person legal ops team needs to compare clauses across 40+ vendor agreements before drafting a master services template. GPT-5.5 Pro (batch) handles this because its 1.05M token window ingests all contracts in one prompt—no chunking, no retrieval layer, no context loss. The $15 input cost is negligible when you're loading documents once; the $90 output rate matters only if you're generating 50k+ token summaries, which batch mode amortizes overnight. If your synthesis output stays under 10k tokens per job, this beats running 40 separate calls to a cheaper model and stitching results. The trade-off: if you need answers in under 10 minutes, batch queueing (typically 30-90 min) kills the workflow. For end-of-day deliverables where context density drives accuracy, this is the call.

Multimodal batch processing for design QA at agency scale

A 25-person creative agency runs nightly QA on 200+ client mockups, checking brand guideline compliance across typography, color, and layout. GPT-5.5 Pro (batch) processes image+text prompts at a fraction of real-time API cost, and the 1M+ context window means you can include the full brand book as reference text alongside each image. The $90 output price is manageable because QA reports are structured JSON (under 2k tokens per image). If you're running this during business hours and need sub-5-minute turnaround, the batch queue is a deal-breaker—switch to the real-time endpoint. But for overnight processing where you're analyzing hundreds of assets and context fidelity matters more than speed, this model's multimodal reach and cost structure win.

When batch mode and massive context beat real-time cheaper models

A 4-person dev tools startup generates API reference docs from 80k lines of TypeScript source plus 300 pages of internal architecture notes. GPT-5.5 Pro (batch) fits the entire codebase and notes in one context window, so cross-references stay accurate and examples pull from real implementation patterns. The $15 input rate is a non-issue when you're loading code once per release cycle; $90 output becomes expensive only if you're generating 100k+ token docs, but batch discounting (compared to real-time) keeps it under $10 per full doc set. The threshold: if you're publishing docs more than twice a week and need same-hour turnaround, batch latency breaks your CI pipeline. For weekly or monthly releases where context completeness prevents hallucinated API signatures, this is the right model.