OpenAI: GPT-5.4 Pro

Review this 847-page M&A contract PDF and flag any clauses where indemnification caps fall below industry standard for deals in this size range. Cross-reference the termination provisions.

In this illustrative example, the model would identify three problematic clauses across sections 8.3, 12.7, and 19.2, noting that the $15M indemnification cap represents only 3.8% of transaction value versus the 10-15% industry norm for deals exceeding $400M. It would flag the interaction between the survival period in section 8.3 (18 months) and the termination rights in section 14, explaining how the shortened timeline creates asymmetric risk exposure for the buyer in scenarios involving delayed discovery of breaches.

The 1.05M token context window handles book-length documents in a single pass, eliminating the chunking strategies required by smaller models. At $30 input per million tokens, a full contract review costs roughly $25 — feasible for high-stakes work where accuracy justifies the premium over models with 200K windows.

I'm debugging a React Native app where useEffect runs twice on mount in development but not production. The dependency array includes a memoized callback. Walk me through what's happening and how to fix it.

In this illustrative response, the model would explain that React 18's Strict Mode intentionally double-invokes effects in development to surface bugs in cleanup logic, then clarify that the memoized callback likely has an unstable reference due to missing dependencies in its own useCallback hook. It would provide a corrected code snippet showing proper dependency tracking, explain why the production build doesn't exhibit this behavior (Strict Mode disabled), and note that the double-invocation is actually catching a real bug where the effect would misbehave during Fast Refresh or component remounting.

This example shows reasoning through a multi-layered framework quirk that requires connecting React's internals, development tooling, and callback memoization patterns. The model's training recency would be critical here — React 18's Strict Mode behavior changed in 2022, and older models often give outdated advice about this specific issue.

Generate a 6-month content calendar for a B2B SaaS company selling API monitoring tools to platform engineering teams. Include topic clusters, target keywords, and suggested formats for each piece.

In this illustrative output, the model would produce a structured calendar organized into three topic clusters: incident response workflows (months 1-2), observability stack integration (months 3-4), and cost optimization through proactive monitoring (months 5-6). Each month would include 8-10 content pieces spanning formats like technical deep-dives (e.g., 'Implementing distributed tracing across polyglot microservices'), comparison guides ('Prometheus vs. Datadog for API latency tracking'), and case studies. Keywords would target long-tail technical phrases with commercial intent, and the model would note seasonal timing considerations like conference schedules and budget planning cycles.

Multimodal input support means you could upload competitor content, analytics exports, or brand guidelines as images/files to inform the calendar. The $180/Mtok output cost makes this expensive for high-volume content generation — a 4,000-word calendar costs roughly $0.72 — but the context window allows incorporating extensive background materials without summarization loss.

When 1M+ token context justifies the $180/Mtok output cost

A 12-person legal ops team needs to compare clauses across 40+ vendor agreements and generate a unified compliance summary every quarter. GPT-5.4 Pro's 1.05M token context window fits all contracts in a single prompt—no chunking, no retrieval overhead, no context-loss errors. The $30 input cost is negligible when you're loading 800K tokens once; the $180 output rate stings only if you're generating 50K+ token reports. For this team, the alternative is three days of paralegal time or a RAG pipeline that still misses cross-document nuance. If your synthesis output stays under 20K tokens and you run this monthly or quarterly, the model pays for itself in labor savings. If you're generating daily reports with 100K token outputs, switch to a cheaper long-context model and accept the quality drop.

Why a 4-person fund uses this for board decks despite the price

A venture fund writes 8-12 board memos per quarter, each synthesizing market research, portfolio updates, and financial models into a 15-page narrative. GPT-5.4 Pro's multimodal input (text, image, file) means they upload spreadsheets, cap tables, and competitor slide decks directly—no manual transcription, no separate OCR step. The $180/Mtok output cost translates to roughly $9 per 50K-token memo, which is trivial against the $40K/month they'd pay a junior associate to draft the same material. The 1M+ context window lets them include six months of meeting notes and still have room for the current quarter's data. If you're writing fewer than 10 long-form documents per month and each one justifies two hours of senior review time, this model's cost is a rounding error. If you're drafting 50+ memos monthly, you need a cheaper workhorse.

When to use this for invoice processing vs. a vision-specialist model

A 20-person accounting firm processes 300 client invoices per month, extracting line items into JSON for their ERP system. GPT-5.4 Pro's image modality handles scanned PDFs and photos without a separate OCR pass, and the large context window means they can include a 50-page vendor catalog as reference in every prompt. At $30 input per million tokens, processing 300 invoices (assume 5K tokens each after image encoding) costs roughly $45/month; output is minimal (JSON records), so the $180 rate barely registers. The trade-off: without public benchmarks, you're trusting OpenAI's reputation over proven vision-specialist scores. If your invoices are standard layouts and you value the all-in-one workflow, this works. If you're processing 5K+ invoices monthly or need sub-1% error rates with audit trails, test a dedicated document-AI model first.