OpenAI: GPT-5.2 (batch)

Review this Python function for edge cases and suggest defensive improvements. The function parses user-uploaded CSV files and calculates rolling averages over a configurable window.

This function would produce a structured code review identifying four edge cases: empty file handling (currently raises an unhandled exception), non-numeric column data (fails silently), window size exceeding dataset length (produces NaN without warning), and memory issues with files approaching the 400k token context limit. The model would suggest adding explicit type validation, implementing chunked processing for large files, and wrapping the rolling calculation in a try-except block with informative error messages. Each suggestion includes a 3-5 line code snippet showing the defensive pattern.

Demonstrates GPT-5.2's strength in architectural code review rather than just syntax fixes. The 400k context window allows analyzing entire codebases, but batch processing means no interactive clarification—prompts must be self-contained and specific about what 'defensive' means in your context.

Extract all financial commitments, deadlines, and penalty clauses from this 47-page SaaS vendor contract. Output as a structured table with risk severity ratings.

The model would return a markdown table with columns for clause reference, commitment type, deadline, penalty amount, and a risk rating (low/medium/high). Each row would cite specific page and section numbers from the contract. For a typical enterprise SaaS agreement, this might yield 15-20 rows covering payment terms, data retention obligations, termination notice periods, and liability caps. The risk ratings would reflect both financial exposure and operational impact, with brief justifications in a notes column.

Showcases the 400k token window handling long documents in a single pass—no chunking or summarization loss. However, batch mode means you can't ask follow-up questions about ambiguous clauses. Legal accuracy depends on prompt clarity: specify your risk framework upfront or ratings will be generic.

Generate a technical FAQ for our API documentation. Context: we're a logistics platform, our API handles shipment tracking, rate quotes, and label generation. Assume developers are integrating for the first time.

The model would produce 8-12 FAQ entries covering authentication setup, rate limiting behavior, webhook retry logic, and common error codes. Each answer would be 2-3 paragraphs with code examples in curl and Python. The tone would match technical documentation standards—precise, example-driven, no marketing language. Questions would anticipate real integration friction points: 'Why am I getting 429 errors during testing?' or 'How do I handle partial shipment updates?' rather than basic 'What is an API key?' content.

Highlights GPT-5.2's ability to infer domain-appropriate questions from minimal context and write in documentation voice. The batch pricing ($7/Mtok output) makes this cost-effective for bulk content generation. Trade-off: no real-time feedback loop, so provide example API responses or error messages in the prompt if you want accurate troubleshooting content.

When 400K context beats chaining smaller models for legal review

A 12-person legal ops team processes vendor agreements that reference 8-12 prior contracts and internal policy docs. GPT-5.2 batch handles the entire corpus in one pass—no chunking, no retrieval step, no context-window juggling. At $0.88/Mtok input, a 300K-token review (all contracts plus policies) costs $0.26 per job. The 400K window means you load everything once and ask follow-up questions without re-uploading. If your contracts average under 50K tokens and don't cross-reference heavily, a smaller model saves money. But once you're stitching together 6+ documents per review and running 200+ jobs a month, the batch discount and single-pass architecture make this the default.

Batch pricing makes GPT-5.2 viable for daily research digests

A 4-person investment research team scrapes 80-120 PDFs and earnings transcripts every night, then generates a 10-page synthesis by 6 AM. GPT-5.2 batch runs asynchronously with a 24-hour SLA, so they queue the job at market close and pull results at breakfast. The $7.00/Mtok output rate is steep, but batch mode cuts total cost by 50% versus real-time API calls, and the 400K context means one model call per digest instead of map-reduce across 12 chunks. If you need results in under 2 hours, this won't work—batch jobs can take up to a day. But for overnight or weekend workloads where latency doesn't matter, the economics flip in your favor above 50 digests per month.

When to skip GPT-5.2 batch for image-heavy workflows

A 20-person e-commerce team generates alt text for 5,000 product photos per week. GPT-5.2 batch supports images, but the $7.00/Mtok output price makes it expensive at scale—each 80-token caption costs $0.56 in output alone, or $2,800/week for the full run. A vision-specialized model at $0.50-$1.50/Mtok output cuts that bill by 75%. The 400K context window is wasted here because each image is an independent job. Use GPT-5.2 batch when you're processing multi-page visual documents (slide decks, design specs) where the context window adds value. For single-image tasks at volume, a cheaper vision model wins on unit economics every time.