Prompt Caching in 2026: How It Works and Cuts API Costs
What Is Prompt Caching?
Prompt caching lets an AI provider reuse processing from a previously submitted prompt prefix instead of processing those same input tokens again. It is useful when requests repeatedly include long system instructions, tool definitions, reference documents, or conversation history.
For developers in 2026, the practical benefits are lower input-token costs and potentially faster time to first token. Prompt caching does not replay an old answer. The model still processes new content and generates a response, so output tokens remain billable under the model’s normal pricing.
How Does Prompt Caching Work?
During inference, a provider can retain reusable internal state for an eligible prompt prefix, often including attention key/value data. When another request begins with the same eligible prefix, the provider can reuse that work and process only the remaining input.
Think of a support assistant with 12,000 tokens of product documentation followed by a customer’s question. If the documentation stays identical and appears first, subsequent questions may reuse that cached prefix. If you insert a changing timestamp before the documentation, you can prevent that shared prefix from matching.
This is prefix reuse, not semantic similarity. Paraphrasing instructions usually creates a different token sequence. Message order, tool schemas, images, and formatting can also affect eligibility or matching.
How to Set Up Cache-Friendly Requests
- Put stable content first. Start with reusable instructions and reference material. Place the changing user question and request-specific metadata afterward, while preserving the correct instruction hierarchy.
- Keep serialization consistent. Avoid randomly reordered tool definitions, unnecessary whitespace changes, and regenerated document headers. Use deterministic prompt templates.
- Check your model’s requirements. Minimum cacheable lengths vary by provider and model. Many OpenAI models use automatic caching for eligible long prefixes; a short repeated prompt may not qualify.
- Use explicit controls where supported. With Anthropic-compatible APIs, eligible content blocks can use cache_control with type set to ephemeral to mark cache boundaries. Supported locations, minimum lengths, and breakpoint limits depend on the model and API.
- Choose retention intentionally. Anthropic’s standard ephemeral cache commonly uses a five-minute lifetime, with a one-hour option available in supported configurations. OpenAI retention options also vary. Confirm current support and pricing before configuring either.
- Preserve provider fields through your relay. A compatible API interface does not automatically guarantee every caching feature. Confirm that your relay forwards cache controls and returns cache-usage statistics.
Verify Hits Instead of Guessing
Run two requests with the same long prefix and different questions. Inspect usage rather than assuming that faster responses prove caching worked.
In OpenAI Chat Completions responses, check usage.prompt_tokens_details.cached_tokens when available. Anthropic responses can report cache_creation_input_tokens and cache_read_input_tokens. Other endpoints or relays may expose different structures, so consult their response documentation.
Measure cached tokens as a share of total input tokens across representative traffic. Also track total spend, latency, cache-write charges, and misses after idle periods. A successful local test does not guarantee production savings if requests rarely share prefixes.
Calculate the Real Savings
Suppose a hypothetical model charges $1 per million ordinary input tokens and $0.10 per million cached input tokens. For 100,000 input tokens with 80,000 cache hits, input cost becomes $0.028 instead of $0.10: a 72% reduction. This excludes output charges and any cache-write fees.
Actual economics depend on model pricing, reuse frequency, and retention. Some providers charge extra to create caches. Caching a document once and never reusing it may cost more, not less.
Keep Caching Safe and Affordable
Do not treat a cache as durable storage, application memory, or an authorization boundary. Verify provider isolation and retention policies, especially for sensitive documents. Keep tenant-specific data separated, and update cached reference content when its source changes.
59API offers low-cost, pay-as-you-go access to native, official-quality Claude and GPT models without downgrades. It works with Claude Code, Codex, and OpenAI SDKs, with API access based at https://api.59api.com. Confirm your selected model’s caching support and the relay’s billing treatment before estimating savings.
For a budget-conscious evaluation, sign up for 59API and benchmark a repeated-prefix workload; its referral rebate provides another potential saving alongside careful prompt design.