Anthropic: Claude Opus Latest

Review this 47-page merger agreement PDF and flag any clauses that shift liability risk asymmetrically toward the acquiring party. Summarize each finding in plain language.

The model would parse the entire document within its 1M token context window and return a structured analysis identifying 6-8 problematic clauses. Each summary would cite specific section numbers, quote the relevant language, explain the liability imbalance in business terms, and suggest negotiation angles. The response would maintain legal precision while remaining accessible to non-lawyers, demonstrating both technical accuracy and communication skill across a complex, multi-page document.

This showcases Claude Opus's strength in long-document reasoning and professional-domain synthesis. The 1M token window handles book-length inputs without chunking. However, at $25/Mtok output, a thorough analysis of this length could cost $2-4 per query—teams should weigh thoroughness against budget for high-volume contract review workflows.

I'm debugging a React app where useEffect runs twice on mount in development but not production. Walk me through why this happens and whether I should change my code.

The model would explain that React 18's Strict Mode intentionally double-invokes effects in development to surface bugs related to missing cleanup functions. It would clarify that this behavior disappears in production builds, describe the specific lifecycle sequence (mount → unmount → remount), and provide a concrete code example showing proper cleanup with return statements. The explanation would distinguish between cases requiring fixes (subscriptions, timers) versus benign double-runs (logging, analytics), concluding with actionable guidance on when to refactor versus when to ignore the behavior.

This demonstrates Claude Opus's ability to teach complex framework behavior with precision and context-awareness. The response balances theory with practical debugging advice. The model handles technical nuance well, though developers working in less-common frameworks may find responses less authoritative than for mainstream tools like React.

Generate a 3-day itinerary for Tokyo focused on architecture and design, avoiding tourist traps. I'm staying in Shibuya and prefer walking or short train rides.

The model would produce a day-by-day plan featuring specific venues like the 21_21 Design Sight museum, Daikanyama T-Site bookstore complex, and lesser-known Metabolism-era buildings in Shinjuku. Each recommendation would include transit directions from Shibuya, estimated visit durations, and brief architectural context (e.g., 'Nakagin Capsule Tower—if still standing—exemplifies 1970s metabolist ideals'). The itinerary would cluster geographically to minimize travel, suggest meal spots aligned with design aesthetics, and note seasonal considerations like museum closures.

This highlights Claude Opus's strength in synthesizing domain knowledge (architecture history) with practical constraints (location, transit). The multimodal capability means users could upload neighborhood maps for route refinement. Trade-off: the model's training cutoff means recent openings or demolitions may be missed—users should verify venue status for time-sensitive plans.

When 1M-token context justifies premium pricing for legal teams

A 4-person litigation support team needs to cross-reference depositions, contracts, and email threads spanning 800+ pages per case. Claude Opus Latest handles the full document set in a single context window—no chunking, no retrieval lag—which cuts review time from days to hours. At $5 input / $25 output per Mtok, a typical 600k-token load costs $3 in, $12.50 out if you generate a 500k-token summary. That's $15.50 per case, which pencils when your billable rate is $200+/hour and you're saving 6+ hours of associate time. If you're running discovery on 50+ cases per month, the math flips: consider a cheaper long-context model and invest the delta in prompt tuning. For teams doing 5-20 cases monthly where accuracy trumps cost, this is the call.

Why massive context beats RAG for whole-repo rewrites

A 12-engineer SaaS team is migrating a 300k-line monolith to microservices and needs to trace dependencies across 40+ modules. Claude Opus Latest ingests the entire codebase—including tests, configs, and documentation—in one shot, so it can propose refactors that respect cross-module contracts without hallucinating broken imports. The 1M-token window means you load once and iterate on the same context for hours, avoiding the retrieval errors that plague RAG setups on large codebases. At $5/Mtok input, a 400k-token repo costs $2 to load; if you generate 50k tokens of refactor plans, that's another $1.25. For a multi-week migration where one missed dependency costs 8 engineer-hours to debug, the $3.25 per session is a rounding error. If your repo is under 100k tokens, save the money and use a mid-tier model.

When investor relations teams pay for image-plus-text reasoning

A 3-person IR consultancy prepares board decks by analyzing earnings transcripts, slide decks, and competitor filings—often mixing PDFs with embedded charts and tables. Claude Opus Latest handles text and image inputs natively, so it can read a 60-page slide deck, extract the revenue waterfall chart on page 14, and cross-reference it against the CFO's verbal guidance in the transcript without manual preprocessing. The multimodal capability eliminates the 2-hour step of converting slides to text and re-keying chart data. At $5 input / $25 output per Mtok, a 200k-token transcript plus 50-page deck costs roughly $1.25 in, $5 out for a 200k-token summary—$6.25 total. If you're doing this for 8 clients per quarter, that's $50 in model costs versus 16 hours of analyst time saved. For teams processing fewer than 3 decks per month, a cheaper text-only model plus manual chart extraction is the better trade.