Prompt Caching 201 (developers.openai.com)

OpenAI has announced significant enhancements to its Prompt Caching capabilities, which allow for more efficient processing of model prompts by reusing previously computed prefixes. This new feature can slash time-to-first-token latency by up to 80% and reduce token costs by as much as 90%, making it a crucial advancement for developers using AI models. The ability to cache entire request prefixes—including messages, images, and tool definitions—automatically applies to prompts over 1024 tokens, thus optimizing operational efficiency without incurring additional fees. The caching mechanism works by routing requests with repeated content to servers that have already processed those prefixes, effectively skipping significant computational overhead during inference. Key strategies for improving cache hit rates include stabilizing prompt prefixes, utilizing the `prompt_cache_key` for better routing, and leveraging the Responses API instead of Chat Completions for higher cache utilization. As models continue to evolve, particularly with advancements seen in the latest iterations like GPT-5, the implications of effective Prompt Caching are substantial, leading to not only cost savings but also quicker response times that enhance user experience in AI-driven applications.