Deepseek

LLM Prompt Cache Options Across Providers A reference covering cache TTL options and other cache-control dimensions across major LLM providers as of May 2026. TTL mechanics Fixed-duration TTLs Anthropic: 5-min (default) and 1-hour (extended). Cache writes cost 1.25× base input for 5-min TTL, 2× for 1-hour. Cache reads ≈ 10% of base input. TTL refreshes on each read (sliding window). AWS Bedrock: 5-min default, 1-hour added Jan 2026 for Claude Sonnet 4.5, Haiku 4.5, Opus 4.5. Also refresh-on-read. OpenRouter (Gemini path): 5-min TTL that does NOT update on read (fixed window) — gateway-specific behavior worth checking when going through proxies. Arbitrary / configurable TTL Google Gemini explicit caching: No minimum or maximum bounds on TTL. Default 60 min. You can update TTL on an existing cache and delete it early to stop billing. Billed as cached_tokens × storage_duration (per token-hour), not via a write-time premium. Opaque / provider-managed retention OpenAI: No exposed TTL. Baseline ~5–10 min of idle retention; off-peak can persist up to 1 hour. Extended prompt caching retains KV tensors 1–2h typical, up to 24h max. DeepSeek, Grok, Moonshot, Groq, Kimi K2: Automatic, provider-managed, no exposed TTL. Implicit vs explicit control Implicit (zero-config): OpenAI, DeepSeek, Grok, Moonshot, Groq, Gemini implicit tier. Server decides what to cache when it detects a recurring prefix. Explicit (marked / lifecycle-managed): Anthropic and Alibaba use inline cache_control: {"type": "ephemeral"} markers. Gemini explicit caching exposes full CRUD on cache objects via API (create, get, update, delete) — caches behave like first-class resources, similar to Valkey keys. Cache breakpoints / layering Anthropic supports up to 4 cache_control breakpoints in a single request. You can mix TTLs within one request, but longer TTL blocks must appear before shorter TTL blocks in the prompt structure (tools → system → messages order). Practical use: 1-hour cache for stable system prompt + tool defs, 5-min cache for mid-conversation context, paying the higher write premium only on the truly stable prefix. ...

GRPO — Group Relative Policy Optimization GRPO is a reinforcement learning algorithm introduced by DeepSeek (DeepSeekMath, later DeepSeek-R1) as a more efficient alternative to PPO for fine-tuning LLMs with RL. Core Idea PPO needs a separate value model (critic) of comparable size to the policy to estimate the baseline for advantage calculation. That doubles memory and compute. GRPO ditches the critic entirely. Instead, for each prompt it samples a group of G outputs from the current policy, scores each with the reward model, and uses the group’s mean and standard deviation as the baseline: ...