Anthropic: Claude Haiku Latest

Review this Python function for potential bugs. It's supposed to merge two sorted lists but users report incorrect output when one list is empty.

The function has an off-by-one error in the final cleanup loop. When `i < len(list1)` completes, you're appending `list1[i:]` — but `i` is already past the last processed element. This works when both lists have remaining items, but fails when `list1` is empty because you never enter the first while loop, leaving `i` uninitialized. Initialize `i = 0` and `j = 0` before the loops, or use `result.extend(list1[i:])` only after confirming `i` is defined. The logic for `list2` has the same issue.

Haiku's 200k context window lets it hold entire codebases in memory, so it catches bugs that require understanding distant function calls. This example shows its ability to trace edge cases through control flow. The response is fast and direct, though it doesn't suggest a refactored implementation — you'd need to prompt again for that.

Summarize this 40-page quarterly financial report (attached PDF) focusing on revenue trends, cost structure changes, and forward guidance. I need talking points for an investor call in 30 minutes.

Revenue grew 12% YoY to $847M, driven by enterprise subscriptions (+18%) while consumer segment declined 3%. Gross margin compressed 220bps to 64% due to increased cloud infrastructure costs and a one-time data center migration expense of $8M. Operating expenses rose 15%, primarily from R&D headcount additions in AI product lines. Management guides Q4 revenue of $880-900M (8-10% growth) and expects margin recovery to 66% as migration costs sunset. Key risk: enterprise renewal rates softened to 89% from 92% last quarter.

Haiku processes the full document in one pass thanks to its 200k token window, so it can cross-reference forward guidance against historical trend tables buried in appendices. At $1/Mtok input, a 40-page PDF costs roughly $0.15 to process — competitive for high-volume document analysis. The summary is factual but won't catch nuance in management tone or footnote disclaimers without explicit prompting.

I'm teaching a 10-year-old about photosynthesis. Explain it in simple terms, then give me three follow-up questions I can ask to check their understanding.

Photosynthesis is how plants make their own food using sunlight. Imagine the plant's leaves are tiny solar panels. They catch sunlight and use that energy to turn water (from the roots) and carbon dioxide (from the air) into sugar, which the plant eats to grow. As a bonus, the plant releases oxygen — the air we breathe. It's like a factory that runs on sunshine instead of electricity. Follow-up questions: 1. What three things does a plant need to make food? 2. What does the plant release that we need to breathe? 3. Why do you think plants need sunlight to survive?

Haiku adapts its explanation to the specified audience age without over-simplifying or condescending. The follow-up questions scaffold understanding at the right level. This example highlights its instruction-following and tone calibration. However, it doesn't generate visual analogies or suggest hands-on activities — you'd need to prompt for those explicitly.

When Claude Haiku wins on support ticket routing at scale

A 12-person SaaS company processing 800+ support tickets daily needs fast classification without burning budget. Claude Haiku delivers sub-second response times at $1/Mtok input, making it the right call when speed and cost matter more than nuanced reasoning. The 200k context window handles full ticket histories plus knowledge base excerpts in a single call, so you're not stitching fragments. At 800 tickets/day averaging 1k tokens each, you're spending roughly $24/month on input—compare that to $8/Mtok models where the same load costs $192. The trade-off: if more than 15% of tickets need multi-step reasoning or complex policy interpretation, route those to Opus and keep Haiku for the high-volume layer. For pure triage and tagging, this is the model that pays for itself in week one.

Why Haiku handles contract summaries for small legal teams

A 4-person contract review shop needs to summarize 60-page NDAs, MSAs, and SOWs into 200-word briefs for client intake. Claude Haiku's 200k token context fits most contracts in one pass, and at $1/Mtok input you're paying pennies per document even when processing 50+ files a week. The model handles structured extraction (party names, term lengths, liability caps) reliably enough that paralegals spend time reviewing summaries instead of reading full docs. The boundary: if contracts involve non-standard clauses or cross-jurisdictional nuance, Haiku will miss edge cases that Sonnet catches. For standard commercial agreements under 100 pages, though, Haiku delivers 80% of the value at 20% of the cost. Run it as your first pass, flag outliers for human review, and you've just freed up 15 hours a week.

When Haiku's speed beats accuracy for comment filtering

A 20k-user community platform needs to flag toxic comments before they hit threads, and the moderation queue can't lag more than 2 seconds behind post time. Claude Haiku's sub-second latency and $5/Mtok output pricing make it viable to run on every comment, not just flagged ones. At 10k comments/day averaging 150 tokens each, you're spending roughly $7.50/day on output—sustainable for a small team without ad revenue. The model catches obvious spam, slurs, and harassment with 90%+ precision, which is good enough when humans review the edge cases. The limit: if your community discusses sensitive topics where context collapse (sarcasm, in-group language, coded speech) causes false positives, you'll need a hybrid setup with Sonnet on appeals. For general-purpose forums where speed is the bottleneck, Haiku keeps the queue clear without killing your runway.