Context Caching vs Prompt Caching: Long Context Storage in Gemini and Claude

Overview #

A technical comparison between Anthropic's dynamic prefix Prompt Caching and Google Gemini's explicit Context Caching, detailing TTL storage fees, minimum token thresholds, and architectural integration.

Anthropic Prompt Caching: Dynamic Automatic Invalidation #

Anthropic's prompt caching relies on prefix matching. Developers place a cache_control: {"type": "ephemeral"} breakpoint in system messages or tools. When subsequent requests share that exact prefix, Anthropic charges 90% less for input tokens. The cache lasts for 5 minutes and is refreshed automatically with every cache hit, requiring zero upfront storage commitment.

Google Gemini Context Caching: Explicit TTL Storage #

Google Gemini takes an explicit database-style approach. Developers call a dedicated /v1beta/cachedContents endpoint to pre-store a large dataset (minimum 32,768 tokens) with a specified Time-To-Live (e.g., 2 hours). Queries referencing that cache URI pay 75% lower input fees, plus an hourly storage charge (e.g. $1.00 per 1M tokens per hour).

Which Caching Model Suits Your Workload? #

• Dynamic Multi-Turn Chat (Claude): Ideal when conversations have frequent continuous interaction, resetting after user disconnects.
• Heavy Document / Video Analysis (Gemini): Ideal when multiple independent users will query a 500-page enterprise handbook or 1-hour video over an entire working day.

Code Example: Configuring Anthropic Ephemeral Prompt Cache Breakpoint #

import anthropic

client = anthropic.Anthropic()

response = client.messages.create(
    model="claude-3-5-sonnet-20241022",
    max_tokens=1024,
    system=[
        {
            "type": "text",
            "text": "Here is the 100-page enterprise compliance manual: ... [90k tokens of text]",
            # Mark for 90% discount on subsequent requests
            "cache_control": {"type": "ephemeral"}
        }
    ],
    messages=[{"role": "user", "content": "What is the policy for overseas data transfers?"}]
)
print("Cached Tokens Read:", response.usage.cache_read_input_tokens)

Frequently Asked Questions #

Q: What is the minimum token count for prompt caching?

Anthropic requires a minimum of 1,024 tokens for Claude 3.5 Sonnet (2,048 for Haiku). Gemini requires 32,768 tokens for explicit context caching.

Q: How much money does prompt caching save?

Prompt caching reduces cached input token costs by up to 90% ($0.30 per 1M tokens on Sonnet compared to $3.00 standard).

Q: Does changing a single word invalidate the cache?

Yes. Because caching relies on prefix hash matching, altering any token before the cache breakpoint invalidates the entire downstream cache.