Anthropic vs OpenAI prompt caching: latency compared

Anthropic vs OpenAI prompt caching latency is the difference between a 2-second and a sub-second first token when you ship a 10K-token system prompt. Both providers cache prompt prefixes to skip recomputing the KV cache, but they diverge on opt-in mechanics, TTL, and measurable time-to-first-token behavior. This head-to-head breaks down the two implementations across the dimensions that matter in production.

Cache mechanics

Anthropic

Anthropic requires explicit marking. You attach cache_control: {"type": "ephemeral"} to a content block (usually the system prompt or a large document). The prefix up to that block must be at least 1024 tokens. On a write, usage reports cache_creation_input_tokens; on a hit, cache_read_input_tokens.

import anthropic
client = anthropic.Anthropic()
resp = client.messages.create(
    model="claude-3-5-sonnet-20241022",
    max_tokens=256,
    system=[{
        "type": "text",
        "text": "You are a legal analyst. " * 500,  # >1024 tokens
        "cache_control": {"type": "ephemeral"}
    }],
    messages=[{"role": "user", "content": "Summarize the contract."}]
)
print(resp.usage.cache_creation_input_tokens)  # first call
print(resp.usage.cache_read_input_tokens)      # subsequent calls

OpenAI

OpenAI caches automatically. Any prompt prefix of 1024+ tokens (2048 for gpt-4o and a few others) that is identical across requests is cached without code changes. The hit surfaces as prompt_tokens_details.cached_tokens.

from openai import OpenAI
client = OpenAI()
resp = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": "You are a legal analyst. " * 500},
        {"role": "user", "content": "Summarize the contract."}
    ]
)
print(resp.usage.prompt_tokens_details.cached_tokens)

The first request populates the cache; subsequent identical requests read from it. No API flag exists to force or prevent caching.

Capabilities and cache scope

Anthropic supports up to four cache breakpoints per request, so you can cache a static system block, a large knowledge block, and a conversation history block independently. OpenAI treats the prompt as a single contiguous prefix; any change anywhere before the last token busts the cache.

For multi-turn agents, Anthropic lets you re-cache the growing conversation by marking the latest assistant turn. OpenAI caches the entire message list prefix, which means appending a new user message preserves the hit as long as all prior messages are byte-identical.

Cost model

Anthropic charges a write multiplier of 1.25× base input price for the cached portion and a read cost of 0.1× base input on hits. OpenAI gives reads at 0.5× base input for cached tokens; writes are billed at normal input price (no surcharge). Both expire after 5 minutes of inactivity (Anthropic extends to 1 hour on paid tiers).

Example: 10K cached tokens, 1,000 repeated calls/day.

Anthropic: 10K×1.25 on first write + 10K×0.1×999 ≈ 1,250 + 99,900 = 101,150 token-units.
OpenAI: 10K×1×1 (write) + 10K×0.5×999 ≈ 10,000 + 4,995,000? Wait: 10K×0.5 = 5K per read, ×999 = 4,995,000. That’s 5,005,000 token-units. Clearly Anthropic is far cheaper at scale. The math shows why the Anthropic vs OpenAI prompt caching latency discussion is inseparable from cost at high QPS.

Latency and throughput impact

The core of Anthropic vs OpenAI prompt caching latency is time-to-first-token (TTFT). Prefill compute scales with prompt length; caching skips it for the prefix. A 10K-token prefix that takes ~800 ms to prefill on a miss typically drops to <200 ms on a hit on both providers. Exact numbers shift with model and datacenter load, but the relative win is consistent.

Cache hits do not speed up token decoding—only the prefill phase. If your bottleneck is generation speed, caching won’t help. Throughput gains are real: freed prefill capacity raises effective requests/sec, especially when many clients share the same static prefix.

At the gateway layer, honoring provider cache-control hints matters. n4n.ai forwards Anthropic cache_control blocks and OpenAI’s natural prefix without modification, so the latency win passes through to the client instead of being silently lost by a proxy that strips fields.

Ergonomics

Anthropic forces you to decide cache boundaries. That is good for optimization but adds friction: you must ensure the marked block exceeds 1024 tokens and remains stable. OpenAI’s zero-config approach means caching kicks in automatically once prompts are long and stable. If your template injects a per-request user ID at the top, OpenAI caches nothing; Anthropic lets you put the static base first, mark it, then append dynamic content after the breakpoint.

Debugging: Anthropic returns explicit usage fields; OpenAI hides writes entirely, so you infer cache health by watching cached_tokens appear after the first call.

Ecosystem and tooling

OpenAI’s uniform API means every LangChain wrapper, SDK, and proxy supports caching implicitly. Anthropic’s cache_control is supported in first-party SDKs and major frameworks, but you must propagate the field through any custom middleware. When routing across models, n4n.ai honors client routing directives and forwards provider cache-control hints, preserving Anthropic hits even when you switch between Claude variants.

OpenAI’s automatic caching survives most framework upgrades because there is nothing to break. Anthropic’s explicit field can be dropped by a naive transformer that reconstructs messages.

Limits and invalidation

Minimum cached prefix: 1024 tokens (both; OpenAI 2048 for certain models).
TTL: 5 min idle (Anthropic 1h on paid tiers).
Max cache entries: Anthropic 4 checkpoints per request; OpenAI single prefix.
Invalidation: any token change in the prefix busts the cache. Anthropic returns cache_creation_input_tokens on writes; OpenAI just bills normal input.
Cross-region: caches are per-provider infrastructure; they do not follow a global edge.

Side-by-side summary

Dimension	Anthropic	OpenAI
Opt-in	Explicit `cache_control` block	Automatic on ≥1024 tokens
Min length	1024 tokens	1024 (2048 for some)
TTL	5 min (1h paid)	5 min
Read cost	0.1× input	0.5× input
Write cost	1.25× input	1× input (no extra)
Cache points	Up to 4 per request	Single prefix
TTFT win	Large on long prefix	Large on long prefix
Dynamic prefix	Cache static part only	Entire prefix must match

Which to choose

High-QPS agents with static rule files: Anthropic wins on cost (0.1× reads) and multi-block control. Use it for coding assistants with 20K-token style guides.

Bursty, template-driven apps: OpenAI’s automatic caching reduces engineering overhead. If your prefix is mostly stable, you get a 50% read discount without touching code.

Multi-tenant with per-user system additions: Anthropic’s explicit boundary lets you cache the shared base and avoid busting on user diffs. OpenAI will miss unless the dynamic content sits after a clean 1024-token static prefix.

Rapid prototyping: OpenAI, zero config. Ship first, optimize later.

Sparse calls over long intervals: Anthropic’s 1-hour TTL on paid tiers keeps hits warm between infrequent requests; OpenAI’s 5-min window will likely expire.

The Anthropic vs OpenAI prompt caching latency decision is ultimately about control versus convenience. If you can afford the engineering time to mark boundaries, Anthropic gives cheaper, more durable hits. If you want caching as a free side effect, OpenAI is the path.