Optimizing prompt caching to reduce token costs for long-context RAG pipelines

DeepSeek-V3 and Claude 3.5 Sonnet have completely changed the math for long-context RAG, specifically because of how they handle prompt caching. If you're still feeding 20k+ tokens of context into every single API call without caching, you're essentially burning money. I've spent the last month benchmarking these two against GPT-4o for a technical documentation bot, and the cost delta is staggering.

The core issue is that in a standard RAG pipeline, the "system prompt + retrieved documents" block stays mostly static while only the user query changes. When I ran a test set of 500 queries against a 30k token knowledge base, the non-cached costs were linear. With caching, the "hit" cost for the prefix is significantly cheaper—DeepSeek, in particular, is aggressive with its caching discounts.

Here is how they actually stack up in a production loop:

Claude 3.5 Sonnet (Anthropic)
The prompt caching is explicit. You have to mark the breakpoints in your prompt using the cache_control block. It's a bit more manual, but the control is precise. For my RAG pipeline, I cached the entire documentation set.
Pros: Extremely stable cache hits; the "write" cost is higher, but the "read" cost is a fraction of the original.
Cons: The 5-minute TTL (Time To Live) is a bottleneck if your traffic is bursty rather than constant.

DeepSeek-V3
Their DiskCache mechanism is essentially seamless. You don't need to manually tag blocks; the system identifies common prefixes automatically.
Pros: The pricing is the most disruptive in the market. For long-context caching, it's nearly unbeatable on a price-to-performance basis.
Cons: Occasional latency spikes during the initial "cache miss" phase compared to Claude.

GPT-4o (OpenAI)
OpenAI's caching is automatic and opaque. You don't control what is cached, you just see the cached_tokens count in the usage metadata.
Pros: Zero configuration required.
Cons: Less predictable. I've noticed the cache eviction policy is more aggressive, leading to more frequent misses on long-tail queries compared to the explicit markers in Claude.

For those implementing this, don't just dump everything into the prompt. The order of your tokens matters. Always put your static context (the docs) before the dynamic user query. If you flip them, you break the prefix match and the cache becomes useless.

If you're using a Python wrapper, your payload for Claude should look something like this to actually trigger the savings:

{
  "model": "claude-3-5-sonnet-20240620",
  "messages": [
    {
      "role": "user",
      "content": [
        {
          "type": "text",
          "text": "Here is the technical manual: [Insert 20k tokens here]",
          "cache_control": {"type": "ephemeral"}
        },
        {
          "type": "text",
          "text": "Based on the manual, how do I configure the API?"
        }
      ]
    }
  ]
}

The performance trade-off is negligible. In my benchmarks, cached hits actually reduced Time To First Token (TTFT) by about 30-50% because the model doesn't have to re-process the KV cache for the prefix.

If you are running a high-throughput RAG system, DeepSeek is the current winner for raw cost efficiency, but Claude's explicit caching gives you the most reliability for enterprise-grade latency SLAs. GPT-4o is fine for low-volume, but the lack of transparency in their caching logic makes it harder to optimize your token budget precisely.