OpenAI vs. Anthropic prompt caching: which delivers better AI cost reduction?
OpenAI vs Anthropic prompt caching for high-volume workloads: pricing math, break-even thresholds, and which provider wins for your traffic pattern.
Prompt caching is now the single biggest lever for AI cost reduction on high-volume LLM workloads — but OpenAI and Anthropic implement it differently, and the cheaper provider on the pricing page is not always the cheaper provider on your invoice.
Anthropic charges a 1.25× write premium on cached tokens and 0.1× on reads, with a 5-minute TTL (extendable to 1 hour at higher write cost). OpenAI's automatic caching has no write premium and discounts cached input tokens by 50%, with a ~5–10 minute opportunistic TTL the developer doesn't control.
Break-even math: Anthropic wins decisively when a stable prefix is reused 4+ times within the TTL — the 0.1× read rate crushes OpenAI's 0.5× rate. OpenAI wins when reuse is sporadic or unpredictable, because there is no write penalty to amortize. For chat applications with long system prompts and rapid user turns, Anthropic typically delivers 55–70% input-token savings vs. OpenAI's 30–45%.
The trap most teams fall into: enabling Anthropic caching on workloads with rotating few-shot examples. The prefix changes every request, the write premium fires every time, and spend goes up 15–20%. Run SpendTensor's prefix-entropy report before flipping the cache flag in production.
Our recommendation for mixed workloads: route classification and retrieval-augmented chat to Anthropic with explicit cache breakpoints, and keep one-shot completions and variable-context calls on OpenAI's automatic cache. The two-provider routing strategy consistently outperforms either provider alone on real customer traffic.