Claude Sonnet 4.6

Claude Sonnet 4.6 通过 · 3 sentences

Here is a three-sentence explanation for a CFO: Reasoning models "think out loud" by generating a long internal chain of thought before producing the final answer, and every token in that thinking process is billed at the same rate as the output you actually see. This means a single user request that previously cost 500 tokens of output may now cost 5,000 tokens once the hidden reasoning steps are counted, multiplying cost per call by roughly 5-10x depending on task complexity. To control this, we can cap reasoning depth, route simpler tasks back to non-reasoning models, and audit which use cases actually require that level of accuracy to ensure we are only paying the premium where it delivers measurable business value.

输出 156 tok 延迟 4.9 s

Claude Sonnet 4.6 通过 · 8/8 cases

## The Bug There are two related off-by-one errors: 1. **`cur` starts at `0` instead of `1`** — a single matching pair increments `cur` to `1`, which correctly represents a run of 2, but a single-element list (or any non-repeating list) leaves `cur = 0` and `best = 0`, when the answer should be `1`. 2. **`best` is only updated inside the `if` branch** — a run that ends at the last index is captured correctly *in that branch*, but the initialization problem means the baseline "every element is a run of at least 1" is never established. The root cause is that **`cur` tracks the number of *consecutive equal pairs*, not the length of the run**. Switching to tracking run length directly (starting `cur = 1`, updating `best` on *every* iteration including the `else` branch reset) fixes both issues. […]

输出 685 tok 延迟 12.2 s

Claude Sonnet 4.6 通过 · 5/5 fields, guidance null

```json { "company": "Meridian Robotics", "quarter": "Q3 (September quarter)", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null } ```

输出 67 tok 延迟 1.3 s

Claude Sonnet 4.6 通过 · 121 words, 0 banned, 1 question

**Introducing Prompt Caching Across Providers** Managing AI costs gets complicated when identical prompts hit multiple providers repeatedly, burning through tokens unnecessarily. Today, we're launching cross-provider prompt caching in our API gateway — a feature that stores prompt responses and serves cached results regardless of which underlying model handles your request. Here's how it works: when your application sends a repeated prompt, the gateway checks its cache first, returning stored outputs in milliseconds rather than forwarding requests to OpenAI, Anthropic, or any connected provider. Why pay for the same generation twice? With configurable TTL settings and cache-key customization, teams processing high-volume or repetitive workloads will see immediate latency improvements and measurable cost reductions starting on day one. Available now on all paid plans.

输出 177 tok 延迟 5.4 s