Prompt caching - Mistral AI

2026-05-07 · Source: mistral.ai via Google News · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, quick

Summary

Prompt caching allows for the reuse of previously computed prompt tokens when API requests share an identical prefix, significantly reducing costs and latency. Cached prompt tokens are billed at 10% of the standard input token price. This feature is particularly beneficial for multi-turn conversations, applications with repeated system prompts, fill-in-the-middle requests, and agent completion requests that maintain consistent context. To enable caching, users set a stable application-level identifier, such as a conversation or session ID, as the `prompt_cache_key` in their requests. The API reports cached token usage in the `usage.prompt_tokens_details.cached_tokens` field of the completion response. Cache blocks are 64 tokens in size, meaning prompts with fewer than 64 tokens will not benefit from caching.

Key takeaway

For AI Engineers managing LLM inference costs and latency, implementing prompt caching with `prompt_cache_key` can yield substantial savings and performance gains. You should identify workloads with repeated prompt prefixes, such as conversational agents or applications using consistent system instructions, and integrate a stable application-level identifier. Monitor `usage.prompt_tokens_details.cached_tokens` to verify cache effectiveness and optimize your billing.

Key insights

Prompt caching reuses shared prompt prefixes to reduce LLM inference costs and latency.

Principles

• Cached tokens cost 10% of standard input.
• Cache hits reduce response latency.
• Cache blocks are 64 tokens.

Method

Set a consistent `prompt_cache_key` for requests sharing a prefix, like a conversation ID, to enable prompt caching and track `cached_tokens` in usage details.

In practice

• Use for multi-turn chat sessions.
• Apply to repeated system prompts.
• Avoid for short or unrelated prompts.

Topics

• Prompt Caching
• Mistral AI API
• Token Billing Optimization
• prompt_cache_key
• Multi-turn Chat

Best for: AI Engineer, Machine Learning Engineer, MLOps Engineer

Related on AIssential

• What is Prompt Caching? Optimize LLM Latency with AI Transformers — Prompt caching stores precomputed LLM input representations (KV pairs) to reduce latency and cost for repeated prompt prefixes.
• Prompt Caching with the OpenAI API: A Full Hands-On Python Tutorial — Prompt caching reuses repeated LLM input prefixes to enhance API app speed and cost-efficiency.
• How an Unexpected Reddit Spike Forced Me to Learn Prompt Caching the Hard Way — Multi-agent LLM pipelines offer significant prompt caching benefits, especially when context is passed sequentially between agents.
• Prompt Caching on Claude: Cut Input Costs 78% (The Math Nobody Writes Down) — Claude's ephemeral prefix caching drastically reduces LLM input costs by reusing stable prompt segments.
• Why Care About Prompt Caching in LLMs? — Prompt caching reduces LLM cost and latency by reusing results from repeated input tokens.
• Accelerating LLM Inference with Prompt Caching for Open‑Source Models on Databricks — Prompt caching for LLMs significantly reduces inference latency and increases throughput by reusing identical prompt prefixes.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by mistral.ai via Google News.