Prompt Caching vs Context Compression: Which Saves More on Long Coding Sessions

The Long Session Cost Problem

Long coding sessions are expensive. As your conversation with an AI coding assistant grows, the context window fills with previous messages, code snippets, and explanations. Every new request re-sends all of that history as input tokens. A 2-hour debugging session can accumulate 500,000–2,000,000 input tokens — at Claude Sonnet 4.6 pricing of $3 per million, that is $1.50–$6.00 just in input costs for a single session.

Two strategies address this problem: prompt caching and context compression. They work differently, have different tradeoffs, and are often most effective when used together.

How Prompt Caching Works

Prompt caching stores a snapshot of your input tokens on the provider's servers. When you send the same prefix again — your system prompt, codebase context, or conversation history — the provider serves those tokens from cache instead of reprocessing them. Cache reads cost roughly 10% of standard input token prices.

The key constraint: the cached prefix must be identical across requests. Any change to the cached portion invalidates the cache. This makes caching most effective for stable content:

• System prompts (rarely change)
• Codebase context loaded at session start (stable within a session)
• Documentation or reference material (static)
• Long conversation history (append-only, so prefix stays stable)

Savings example: A system prompt + codebase context of 100,000 tokens, sent with every request in a 50-request session. Without caching: 5,000,000 input tokens at $3/M = $15.00. With caching (one cache write + 49 cache reads): $0.375 (write) + $1.47 (reads) = $1.85. That is an 88% reduction on those tokens.

How Context Compression Works

Context compression reduces the size of the context window by summarizing or removing older content. Instead of sending the full conversation history with every request, you periodically compress older exchanges into a shorter summary and discard the originals.

There are two main approaches:

• Summarization: Use a cheap model (Haiku, DeepSeek V4 Flash) to summarize older conversation turns into a compact summary. Replace the original turns with the summary. This preserves the key information at a fraction of the token cost.
• Selective retention: Keep only the most recent N turns plus any explicitly marked "important" context. Discard everything else. Simpler to implement but risks losing relevant context.

Savings example: A 200-turn conversation where each turn averages 2,000 tokens. Without compression, turn 200 sends 400,000 tokens of history. With compression (summarize every 50 turns into 5,000 tokens), turn 200 sends roughly 60,000 tokens. That is an 85% reduction in context size for later turns.

Head-to-Head Comparison

When to Use Each Strategy

Use prompt caching when: You have a large, stable system prompt or codebase context that you load at the start of every session. This is the highest-ROI optimization available — minimal implementation effort, maximum savings on the most expensive part of your context.

Use context compression when: Your sessions are long and conversational, with many back-and-forth exchanges that accumulate over time. Compression is especially valuable for agent workflows where the conversation history grows with each tool call and iteration.

Use both when: You have a large stable prefix (cache it) AND long evolving conversations (compress the history). This combination can reduce total session costs by 85–95% compared to naive context management.

Practical Implementation

For prompt caching on Anthropic's API, add cache_control: {"type": "ephemeral"} to the content blocks you want cached. The cache is keyed on the exact content, so ensure your system prompt and codebase context are deterministic.

For context compression, a simple approach: when your conversation exceeds 50 turns, send the oldest 40 turns to a cheap model with the prompt "Summarize this conversation history in 2,000 tokens, preserving all technical decisions, code changes, and open questions." Replace those 40 turns with the summary.

Use the AI Cost Estimator to model your current session costs and see how much caching and compression could save for your specific usage patterns.