OpenAI: GPT-5 (batch)

Review this Python function for edge cases and suggest improvements. The function merges overlapping time intervals from a list of tuples.

The function handles basic overlap detection but misses three edge cases: (1) intervals that touch at boundaries (e.g., (1,3) and (3,5)) aren't merged when they should be, (2) unsorted input causes incorrect merges, and (3) empty input list raises an unhandled exception. Add input validation, sort intervals by start time first, and adjust the overlap condition to `end >= next_start` instead of `end > next_start`. Also consider whether touching intervals should merge based on your domain requirements—calendar events typically should, but time-series data segments often shouldn't.

With a 400k token context window, GPT-5 can review entire codebases in one pass rather than isolated functions. This example shows its systematic edge-case analysis, but the batch API introduces 24-hour latency—fine for overnight code reviews, impractical for interactive debugging sessions.

Analyze this 50-page financial report PDF and summarize the three biggest risks to next quarter's revenue guidance. Focus on operational risks, not market conditions.

Three operational risks emerge: (1) The manufacturing facility in Austin is running at 94% capacity with no expansion planned until Q3, creating a hard ceiling on unit shipments if demand spikes. (2) Two key suppliers mentioned in footnote 12 are single-source for critical components, and both have flagged raw material constraints. (3) The customer success team headcount grew only 8% while the customer base grew 34%, and page 31 shows support ticket resolution time increased from 2.1 to 4.7 days—churn risk if service quality continues degrading.

The 400k context window handles lengthy documents without chunking, and file upload support means users can drop PDFs directly. However, batch processing means this analysis arrives hours later—acceptable for quarterly reviews, but teams needing real-time document Q&A should use the standard API instead.

Generate a technical specification document for a REST API that manages user permissions in a multi-tenant SaaS application. Include authentication flow, rate limits, and error handling.

**Authentication**: All endpoints require Bearer token in Authorization header. Tokens expire after 1 hour; refresh using `/auth/refresh` with valid refresh token (7-day expiry). **Endpoints**: `POST /tenants/{tenantId}/users/{userId}/permissions` adds permission (admin only), `GET /tenants/{tenantId}/users/{userId}/permissions` lists permissions (self or admin), `DELETE` revokes. **Rate Limits**: 100 requests/minute per tenant, 429 status with Retry-After header on exceed. **Errors**: 401 for invalid/expired tokens, 403 for insufficient permissions, 404 for non-existent resources, 422 for invalid permission types. All errors return JSON with `{"error": {"code": "...", "message": "...", "details": {...}}}` structure.

GPT-5's structured output generation produces consistent, complete specifications across long documents. The $5/Mtok output cost makes batch processing economical for bulk documentation tasks—generating 50 API specs overnight costs roughly $0.25 in output tokens—but interactive spec refinement becomes expensive quickly.

When to use GPT-5 batch for high-volume document analysis

A 12-person legal tech startup processes 800 contracts nightly, extracting clauses and flagging non-standard terms. GPT-5 batch cuts input cost to $0.63/Mtok—half the real-time rate—while the 400k context window handles full contracts without chunking. The team queues jobs at 6pm, pulls results at 8am, and never waits on a response. This works because the 24-hour SLA matches their workflow and the volume justifies batch discounts. If you need answers in under an hour, pay for real-time GPT-5. If you're running fewer than 200 documents per night, the batch setup overhead outweighs the savings. For this team, batch mode turns a $4k/month API bill into $2k without changing output quality.

GPT-5 batch for image-text annotation at scale

A 5-person ML team labels 10,000 product images weekly, generating structured JSON descriptions for a recommendation model. GPT-5 batch handles both image and text inputs in one call, and the 400k context lets them pass full style guides and example schemas without truncation. At $0.63 input and $5.00 output per Mtok, a typical 500-token output costs $0.0025—acceptable when labeling thousands of assets overnight. The batch queue absorbs their Friday upload spike without rate-limit errors. This breaks down if you need real-time labeling for user-uploaded content, or if your images require specialized vision models. For offline training pipelines where accuracy matters more than latency, GPT-5 batch delivers production-grade annotations at half the cost of real-time alternatives.

When GPT-5 batch handles complex file-based reporting

A 20-person finance team generates 150 client reports on the first of each month, each pulling data from Excel files, PDFs, and prior reports. GPT-5 batch accepts file inputs directly and the 400k context window fits an entire quarter's transaction history without summarization. They queue all jobs overnight, review outputs the next morning, and the $0.63 input rate makes the monthly run cost $180 instead of $360. The 24-hour turnaround aligns with their review-and-send workflow. If clients demanded same-day reports, batch wouldn't work. If the team ran reports weekly instead of monthly, the cost difference would shrink and real-time might make more sense. For this monthly cadence with complex multi-file inputs, GPT-5 batch is the right call.