OpenAI: GPT-5 Nano (batch)

Review this 12-page API design doc and flag any inconsistencies between the endpoint specs and the authentication flow described in section 3.

The model would produce a structured analysis identifying three key inconsistencies: endpoint `/v2/users/{id}` requires OAuth2 but section 3.2 only documents API key auth; the rate limit table lists 1000 req/min for premium tier while the auth flow caps it at 500; and the webhook signature verification uses HMAC-SHA256 in examples but the security section specifies HMAC-SHA512. It would quote specific line numbers and suggest alignment options for each discrepancy.

With a 400K token context window, this model can ingest entire technical documents and cross-reference claims across sections—a task that would require chunking strategies on smaller-context models. The batch pricing ($0.20/Mtok output) makes exhaustive document review economically viable, though real-time use cases remain expensive.

Generate 50 variations of this product description for A/B testing, each emphasizing a different benefit angle while maintaining brand voice and keeping it under 280 characters.

The model would produce 50 distinct variations, each foregrounding a different value proposition—durability, aesthetics, price, sustainability, ease-of-use, status signaling, time-saving, health benefits, etc.—while preserving the original's tone and staying within the character limit. Variations would avoid repetitive phrasing and demonstrate nuanced repositioning of the same product facts.

Batch processing makes high-volume generation tasks like this cost-effective at $0.03/Mtok input. The model's multimodal capability means you could include product images for context. However, the 'nano' designation suggests this isn't the flagship reasoning model, so expect less sophisticated persuasion architecture than GPT-5 standard.

Here's a CSV of 8,000 customer support tickets from Q4. Identify the top 10 complaint themes, estimate resolution time for each theme, and suggest which could be automated with a knowledge base article.

The model would parse the CSV, cluster complaints into themes (login issues 18%, shipping delays 14%, refund processing 12%, etc.), calculate median resolution times per theme from the timestamp data, and flag five themes where responses follow templated patterns suitable for self-service documentation. It would provide sample KB article outlines for the top automation candidates and note which themes require human judgment.

The 400K context window allows direct ingestion of large datasets without preprocessing, and batch pricing makes bulk analysis affordable. The file modality means you can upload the CSV directly rather than pasting text. Trade-off: batch processing introduces latency—this isn't suitable for real-time dashboards, only scheduled reporting workflows.

When batch pricing makes overnight doc analysis viable for small teams

A 4-person compliance team needs to extract clauses from 200-300 vendor contracts every Monday morning. GPT-5 Nano in batch mode hits the sweet spot: the 400k token context window handles most contracts in a single call, and batch pricing drops output cost to $0.20/Mtok—roughly 80% cheaper than real-time alternatives. Queue the job Sunday night, pull structured JSON by 7am. The trade-off is latency: batch jobs can take hours to complete, so this only works when you can wait. If you need answers during business hours or handle fewer than 50 documents per run, real-time models make more sense despite higher cost. For scheduled, high-volume document work where overnight turnaround is acceptable, this model's economics are hard to beat.

How image+file support scales design QA without adding headcount

A 12-person product studio reviews 40-60 Figma exports weekly for brand compliance—checking logo placement, color hex codes, and copy tone. GPT-5 Nano's image and file modalities let you send PNG exports plus the brand guide PDF in one call, then get structured feedback on violations. The 400k context window means the entire brand guide stays in context across all checks. At $0.03 input per Mtok, processing 50 multi-page designs costs under $2 in tokens. The limitation: batch mode adds 2-6 hour delay, so this works for end-of-sprint reviews, not live design critiques. If your team needs instant feedback during design calls, pay for real-time inference. For weekly QA sweeps where you can batch the work overnight, this setup replaces 4-6 hours of junior designer time per week.

When context window and batch economics align for periodic reporting

A 3-person investment research team summarizes 80-100 public company 10-Qs each quarter into 2-page investment memos. GPT-5 Nano's 400k token context fits most 10-Qs without chunking, and batch pricing at $0.20/Mtok output makes generating 100 summaries cost roughly $8-12 in tokens. The file modality means you drop PDFs directly into the prompt without preprocessing. The catch: batch jobs queue for hours, so you can't run this the morning of a client call. If your workflow is quarterly (not daily) and you can schedule the job 12-24 hours before you need results, the cost advantage is 5-8x over real-time models. For ad-hoc research requests or daily news summarization, the latency kills the value. For scheduled, high-volume report generation where overnight processing works, this model's unit economics let small teams punch above their weight.