Tag: prompt-caching

The 1M-token context window is now the default on Claude Opus 5, Opus 4.8/4.7/4.6, Sonnet 5, and Sonnet 4.6, with no beta header and no long-context premium. Here is when deleting the vector store actually pays off, the 10x cache-read rule that decides it, the ~30% tokenizer inflation that breaks your sizing estimate, and the seven-step migration with a verification line on each one.

The cache read and write multipliers are identical on both models, so the break-even point is the same. What differs is the minimum cacheable prefix (1,024 vs 4,096 tokens), the per-token dollar savings, and a new Opus 4.7 tokenizer that counts up to 35% more tokens. With claude-sonnet-4-6 and claude-opus-4-7 pricing math.

A 429 rate_limit_error on claude-sonnet-4-6 in a long agent loop is almost always ITPM, not RPM. Read retry-after, cache the system prompt, and gate on anthropic-ratelimit-input-tokens-remaining. Step-by-step fix with code.

A 2026 walkthrough for wiring RAG into Claude Code 2.1.x: when agentic grep stops scaling, how to attach a hybrid BM25 + dense vector MCP server, how to wrap a retrieval CLI in a custom skill, and how Anthropic's contextual embeddings technique pushes recall above 92%. Anchored to claude-sonnet-4-6, claude-opus-4-7, and Claude Context 0.x.

Place rolling cache_control breakpoints across messages, respect the 20-block lookback, and refresh the 5-minute TTL automatically so a 50-turn agent loop pays the prefix once, not fifty times. Verified against anthropic 0.42 (Python) and @anthropic-ai/sdk 0.30 (Node) in May 2026.

Add prompt caching to a Python or TypeScript Anthropic SDK app, place cache_control breakpoints correctly, and read cache_read_input_tokens and cache_creation_input_tokens to compute a real hit rate. With pricing math for Claude Sonnet 4.6 and Opus 4.7.