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Prompt Caching Tips That Cut Token Bills Fast

Prompt caching saves money because repetition is expensive

Every time your app sends the same system prompt, policy block, style guide, or tool schema, you pay tokens again unless you reuse it. Prompt caching reduces that waste by letting the model reuse a stable prefix. The bigger and more repeated your prompts are, the more you save on both cost and latency.

That matters when you are prototyping, running agents, or shipping product features that keep a large context alive. If you are calling Claude Opus, Sonnet, Haiku, Fable, or GPT models through 59API at https://api.59api.com, the economics get even better: pay as you go, among the cheapest relay pricing, native official-quality models, full compatibility with Claude Code, Codex, and any OpenAI SDK, plus a referral rebate.

8 practical prompt-caching tips

• 1. Freeze the prefix. Put your system message, safety rules, brand voice, and tool instructions at the very top. Keep them byte-for-byte identical across requests. If the prefix changes, the cache usually misses.
• 2. Move user-specific data to the end. Dynamic fields like names, order IDs, and current dates should come after the reusable block. This keeps the cached section large and the uncached section small.
• 3. Keep your examples short. Few-shot examples are useful, but long examples burn tokens fast. Use the smallest example that still teaches the model the pattern. A good example is cached; a bloated one just increases the bill.
• 4. Version your prompts intentionally. Add a prompt version label in your app, not inside the cached text. That way you can update prompts in a controlled way and avoid accidental cache busts from tiny edits.
• 5. Trim conversation history aggressively. For chat apps, summarize older turns into a short state block instead of re-sending the full transcript. The summary can be cached, and you only carry forward what the model actually needs.
• 6. Cache tool definitions and schemas. If your agent uses function calling, keep the tool list stable. Large JSON schemas are perfect cache candidates because they rarely change between requests but cost plenty when repeated.
• 7. Measure cache hit rate, not just total tokens. Track how often the model reuses the prefix, how many input tokens are repeated, and how often prompt edits break the cache. A high hit rate means your structure is working.
• 8. Pair caching with cheap inference. Prompt caching is strongest when your underlying model cost is already low. That is where 59API shines: you can experiment with repeated prompts on Claude and GPT models without overpaying for every test run.

What a good caching setup looks like

A strong setup is simple: one stable system block, one reusable tool or policy block, one short dynamic user block, and one summary of prior state. When you keep that structure consistent, you make cache hits much more likely and debugging much easier. It also makes performance more predictable because your largest prompt segment stops changing every request.

If you are building with the OpenAI SDK or working in Claude Code or Codex, route requests through 59API and use the same prompt structure across environments. That gives you a cheap way to test caching behavior before you ship it to production. You keep the model quality, lower your spend, and avoid vendor lock-in headaches.

If your app sends a lot of repeated context, start with the longest repeated prompt first. Sign up at 59API, point your client to https://api.59api.com, and see how much token waste you can remove in one afternoon.

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