Automatic Prompt Caching

Automatically injects cache_control into multi-turn calls to Anthropic models, so you get cache discounts with zero setup.

Anthropic prompt caching is opt-in — it only works if you attach cache_control markers yourself, so you miss the discount if you don't know about it. PleumRouter automatically injects cache breakpoints into multi-turn calls routed to Anthropic models — no request changes needed, enabled by default (operations setting routing.auto_cache_enabled).

multi-turn call — nothing to add

curl https://apirouter.pleum.ai/v1/chat/completions \
  -H "Authorization: Bearer plm_..." \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-sonnet-4-6",
    "messages": [
      {"role": "system", "content": "<long system prompt, 4000+ chars>"},
      {"role": "user", "content": "First question about the document."},
      {"role": "assistant", "content": "..."},
      {"role": "user", "content": "Follow-up question."}
    ]
  }'

How it decides#

Explicit always wins — if you attached cache_control anywhere in the request yourself, auto-injection backs off entirely. Requests already using explicit caching keep working unchanged.

No-op on the first turn — if the conversation has no assistant message yet (a first request), nothing is injected. It only intervenes on multi-turn conversations, where reuse is certain.

At most 2 injection points— the last block of the system prompt, and the last block of the second-to-last user message (where multi-turn prefix reuse is maximized). Each point is only used when its content totals at least 4,000 characters (matching Anthropic's minimum cacheable size; operator-tunable).

Injection happens only inside the request after it is converted to the Anthropic API format, so even if a fallback ends up serving the request from another (OpenAI-compatible) provider, cache_control never leaks there.

Why not inject on the first turn: an Anthropic cache write costs 1.25× the regular input rate, and a one-shot request never gets the read discount — it would pay the surcharge for nothing. By intervening only on multi-turn conversations, auto-injection can never make you worse off.

Billing and the response#

On a cache hit, the response usage exposes prompt_tokens_details.cached_tokens (hit tokens) and cache_creation_input_tokens (write tokens), and the discount/surcharge is applied to billing automatically. See the Chat Completions docs for caching rates.

usage on a cache hit

"usage": {
  "prompt_tokens": 12480,
  "completion_tokens": 210,
  "prompt_tokens_details": {"cached_tokens": 11900},
  "cache_creation_input_tokens": 0
}

If you want caching from the very first turn (when you know a conversation will follow) or want to control breakpoints yourself, specify cache_control explicitly — auto-injection steps aside and only your markers apply.