Anthropic: Claude Opus 4

Review this Python function for potential race conditions. It's meant to update a shared counter across multiple threads, but we're seeing inconsistent totals in production.

The function lacks thread synchronization. When multiple threads call `increment_counter()` simultaneously, they can read the same value before any writes complete, causing lost updates. The read-modify-write sequence isn't atomic. You need either a `threading.Lock` around the entire operation, or switch to `threading.local()` for per-thread counters that you sum at the end. The current approach will drop increments under load — your production inconsistency matches classic race condition symptoms. If this counter feeds billing or analytics, consider an atomic operation library or move the logic to a queue-based architecture.

This example shows Claude Opus 4's strength in multi-step reasoning about concurrency — it identifies the specific failure mode, explains the mechanism, and offers two architectural fixes. The 200K token context window supports pasting entire codebases for this kind of analysis, though response latency at $75/Mtok output makes it expensive for high-frequency debugging workflows.

I have 47 pages of legal discovery documents. Extract every mention of 'Project Falcon' with surrounding context, then summarize what the project's actual scope was versus what the initial proposal claimed.

Across the 47 documents, 'Project Falcon' appears 23 times. The initial proposal (Doc 3, pp. 12-14) described it as a 'customer retention analytics dashboard' with a six-month timeline and $200K budget. However, emails from months 4-8 (Docs 18, 22, 31) show scope expansion into predictive modeling and third-party data integration, neither of which were in the original SOW. By Doc 41, the project is described internally as 'a full customer data platform' — a fundamentally different deliverable. The budget overrun mentioned in Doc 45 ($340K actual spend) aligns with this scope creep. No formal change order appears in the document set.

This demonstrates Claude Opus 4's long-context synthesis capability — the 200K token window easily accommodates 47 pages of dense legal text, and the model tracks narrative threads across documents. The multimodal support means you can upload PDFs or images directly. Trade-off: at $15 input per million tokens, a 50-page brief costs roughly $3-4 to process, making per-query economics important for high-volume document review.

Explain how RLHF training works to a product manager who understands A/B testing but has no ML background. They need to decide whether to invest in it for our chatbot.

RLHF is like running thousands of A/B tests on your chatbot's responses, but instead of measuring clicks, you're measuring 'helpfulness' as judged by human raters. First, you generate multiple responses to the same prompt. Humans rank them: 'Response A was better than B.' You train a separate 'reward model' to predict those human preferences. Then you use that reward model as an automated judge to fine-tune your chatbot — it learns to produce responses the reward model scores highly. The advantage: you're optimizing for subjective quality, not just task completion. The cost: you need hundreds of hours of human rating work upfront, and the reward model can amplify rater biases. For your chatbot, it's worth it if user satisfaction matters more than speed-to-market, and if you can budget for ongoing human eval as user needs evolve.

This example highlights Claude Opus 4's ability to translate technical concepts into business context without dumbing down the trade-offs. The explanation includes decision-relevant details (cost, bias risk, timeline implications) rather than just definitions. The model's training likely includes significant technical writing, making it effective for cross-functional communication. The $75/Mtok output pricing means explanatory responses like this cost pennies, not dollars.

When Claude Opus 4 justifies its premium on contract review

A 12-person legal ops team processing 80+ vendor agreements per month needs to extract obligations, flag non-standard clauses, and route approvals without hiring paralegals. Claude Opus 4's 200k token context window fits 4-6 full contracts in a single prompt, letting you compare terms across MSAs, DPAs, and SOWs in one pass instead of chunking documents or running separate queries. At $15 input / $75 output per Mtok, a typical 150k-token batch (3 contracts + extraction instructions) costs roughly $2.25 in, $3.75 out if the model returns 50k tokens of structured findings. That's $6 per batch versus 90 minutes of paralegal time at $40/hour. The break-even is around 60 contracts per month; below that threshold, GPT-4o at half the output cost makes more sense unless you're seeing accuracy gaps on nuanced clause interpretation.

Why Opus 4 wins for large-repo documentation sprints

A 5-engineer SaaS team inherits a 40k-line Python monolith with zero inline docs and needs API references, architecture diagrams, and onboarding guides before the next funding round. Claude Opus 4's 200k context window ingests the entire codebase—models, controllers, utils, tests—in one prompt, preserving cross-file dependencies that chunked approaches miss. You paste the repo, ask for module summaries and call graphs, and get back 30k tokens of structured Markdown in under two minutes. At $15/$75 per Mtok, a full-repo pass costs about $3 input + $2.25 output, versus 8-10 hours of senior dev time at $150/hour writing docs manually. The model pays for itself after documenting two repos. If your codebase is under 50k tokens, GPT-4o's lower output pricing is fine; above that, Opus 4's context advantage is the deciding factor.

When Opus 4's context window beats multi-turn ticket routing

A 20-person B2B support team handles 200 tickets daily, each requiring account history, past tickets, product usage logs, and contract terms to route correctly—billing, technical, or account management. Claude Opus 4's 200k tokens fit a customer's full 18-month interaction history plus the new ticket in one prompt, eliminating the multi-turn retrieval dance that introduces latency and drops context between calls. You feed the model a JSON blob of CRM data + ticket text, get back a routing decision, priority score, and suggested first response in 8 seconds. At $15 input per Mtok, a 60k-token context (heavy customer) costs $0.90 per ticket; output is typically 2k tokens at $0.15. That's $1.05 per ticket versus 4 minutes of L1 agent time at $25/hour ($1.67). The model is cheaper and faster above 120 tickets per day; below that, you're paying for context you don't need—switch to GPT-4o-mini and save 80% on cost.