Posts tagged with "Prompt Caching"

GPT-5.6 Sol retires on Foundry in January 2028, so moving to GPT-6 Astra is a choice rather than a deadline, and it touches more than a deployment name: tool calls must move to the Responses API, effort none and temperature disappear, and every line on the price sheet costs 2 to 2.5 times more. A phase-by-phase checklist built on Microsoft's migration process, OpenAI's model guidance and the Learn pages, with the Swedish residency caveat that keeps part of your traffic on Sol.

Spotify routes bulk file reads and boilerplate generation out of Claude Code to a cheaper worker model, cutting Claude token usage on those reads by 90% in tests on a Java monorepo. The pattern needs no Portal licence: Claude Code hooks, two wrapper scripts and a flash-class model in Microsoft Foundry rebuild it inside your own Azure tenant, with the boundaries Spotify itself draws around editing and reasoning.

Claude Fable 5.1 keeps Fable 5's $10/$50 pricing but cuts cache reads to $0.25 per million tokens, which turns a 60-turn agent session from $17.25 into $10.50 and shrinks the premium over Opus 5 from 2x to about 22%. On Microsoft Foundry it ships Anthropic-hosted only, with no EU data zone, no Batches API, a zero default quota on pay-as-you-go, mandatory 30-day retention until Enterprise Frontier Safeguards arrive, and three breaking changes for teams migrating from Fable 5.

Anthropic prices prompt caching with three numbers: a 1.25x or 2x premium on cache writes depending on TTL, a 0.1x rate on cache reads, and the base input rate for everything after the last breakpoint. This deep-dive verifies every figure against the current official docs and covers per-model minimums, break-even math, batch stacking and how Claude on Azure converts it all into CCUs.

Claude Opus 4.8 arrives at unchanged pricing with an effort control on all plans, a fast mode at a third of the previous fast-inference cost, and a Messages API change that lets system entries sit inside the messages array so mid-task instruction updates no longer invalidate the prompt cache. Worked token math shows cache hit rate remains the biggest cost lever, and a four-question framework matches effort, speed and fan-out to each workload.

Prompt caching is the highest-ROI cost lever on long-context LLM workloads in 2026. Anthropic, OpenAI, and Azure OpenAI all offer it with different pricing and breakpoint semantics. A worked comparison of the three providers, the placement patterns that actually hit cache, where the cache silently goes cold, and a 30-minute audit that pays back.

A technical guide to prompt caching across Claude, Azure OpenAI, and GPT — what belongs in the cache, how to structure cache breakpoints, TTL realities, hit-rate optimization, and the anti-patterns that erase the savings.