Posts tagged with "Prompt Caching"

Claude Haiku 5.5 is GA in Microsoft Foundry at $0.10/$0.50 per million tokens, the same Global Standard list price as GPT-6 Luna, and 90% below Haiku 4.5 on paper. The bills diverge on three lines: a tier that charges 5x on every token once a prompt passes 100K, a tokenizer that counts the same text as 30% more tokens, and an EU Data Zone that Luna offers at $0.12/$0.60 while Claude on Foundry has none.

Claude Sonnet 5.5 and GPT-6.1 Sol reached Microsoft Foundry a day apart at the same $2/$10 list price, yet a cache-heavy 60-turn agent session costs $3.45 on Sonnet 5.5 and $2.55 on GPT-6.1 Sol, or $3.06 in the EU Data Zone that Claude still lacks. Above 272K input tokens the order flips, because GPT-6.1 Sol doubles its input rate while Sonnet 5.5 keeps flat pricing to 1M tokens and can run with no up-front reasoning.

Claude Opus 5.5 is GA in Microsoft Foundry at $4/$20 per million tokens with cache reads at $0.20, which cuts a cache-heavy 60-turn agent session from $8.63 on Opus 5 to $5.10 and brings it within 1.5x of Sonnet 5. The saving can reverse on uncached endpoints that ran Opus 5 with thinking disabled, because thinking is now always on, the default effort dropped to medium, and Foundry still offers no EU data zone, batch pricing or fast mode.

GitHub ported the Copilot agent runtime from TypeScript to 832,378 lines of production Rust in about 14.5 weeks for roughly $120,000 in tokens plus three weeks of one developer's time, with a 96.22% prompt-cache hit rate and up to 18x faster in-process session startup. We break down the token bill into reusable unit costs, show why caching kept it around a sixth of the uncached price, and list the preconditions and EU residency checks to settle before budgeting your own agent-driven rewrite.

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.